With the automotive industry seeking to create a more comfortable and enjoyable travel experience for its customers, automakers and designers are stepping up investment in in-car infotainment solutions. These technologies aim to personalize the travel experience by providing noise reduction, in-vehicle sound zones, and immersive audio/video content tailored to the driver's lifestyle. And more importantly, they contribute to building OEM customer loyalty, new revenue streams, and creating lasting customer relationships.

Four in-car entertainment strategies, almost endless opportunities to build a new offer

There is no turning back from in-car entertainment. The development of flat screens, broadband internet access, and the possibility of personalization and customization of content to the viewer are the reasons why no one is willing to give up this form of service.

 For automakers, it's an opportunity to build a stronger relationship with their customers and create new (often  subscription-based) revenue models .

 For the driver and especially for passengers, it's about overcoming the boredom associated with long journeys and introducing solutions into vehicles that until now have been known for their high-end audiophile systems.

No wonder an increasing number of OEMs today are consciously developing the concept of the  digital cockpit of the future . This approach is supposed to shift the focus from the practical functionality of the vehicle to providing compelling entertainment.

Most popular in-car infotainment strategies

Of course, the process of change is not simple and cannot happen overnight. It requires continuous growth in vehicle technological performance, partially related to the car's GPU, E/E Architecture, 5G Internet access, and the development of new display forms. Nevertheless, OEMs have at their disposal one of the following four strategies for developing such solutions.

1. Rear-Seat Entertainment

Using this strategy, entertainment is streamed to displays located in the rear seats or on the roof of the vehicle (for example, the BMW i7)... The user does not need to use a smartphone or tablet in addition to this solution to enjoy the leisure experience.

2. Any-Seat Entertainment

In this solution, content streaming can apply to all displays of the infotainment system in the car (including the driver's seat) - again without having to use smartphones and tablets to navigate. This concept allows video viewing, e.g., while charging an electric car.

3. Augmented Entertainment

A step further can be taken by manufacturers who incorporate the capabilities of 4D cinema and AR applications into their offer. This approach creates a whole new kind of driver's cockpit and develops a unique UX that may become a vehicle's trademark. This type of screen has a variety of potential applications, such as displaying destination-related information, traffic warnings, or intelligent terrain mapping of other vehicles. In the near future, this technology may also be used for augmented marketing, such as showing interesting offers and discounts from nearby restaurants, shops, or malls on the windscreen.

4. Live Entertainment

Finally, real-time entertainment services enable car-to-car interaction and/or social networking among vehicle users. These types of solutions can be used to share viewing, commentary, and engagement among vehicle users through cultural events or music concerts, for example.

New possibilities of infotainment in cars. The 2022 perspective

The ability to combine modern audio-visual technologies with  data analytic s and personalized user information allows OEMs to create entirely new services and products, often ones previously unrelated to the automotive industry.

•  Among these the most notable is, of course, streaming     video and audio content,    which is usually based on partnerships with third parties (e.g., Netflix, Amazon, YouTube, Apple TV or Spotify) and usually operate on a subscription model. In the future, the car is likely to become another medium where we can simply activate the service and continue watching videos, as we move from the living room to the vehicle.

•     Access to gaming platforms and services    that will provide an interactive way of spending time for the driver and passengers while traveling or charging their EV.

•     A tourist offer that is related to visiting a particular place -    acts as a virtual travel agency, which highlights specific points on the map and expands the knowledge of the visited locations (also in the form of quizzes or riddles for passengers).

•     Audiovisual, highly personalized ads,    tailored to the context of where we are traveling, the driver's needs, or resulting from the wear and tear of the vehicle components.

Dynamic growth of the in-car infotainment market

With each passing year, there are new examples of interesting implementations in in-car entertainment, and the market itself is growing rapidly. In 2020, it was valued at more than $21 million, while by 2028 it is predicted to grow to more than $37.5 million, registering a CAGR of 7.5% from 2021 to 2028.

Below is a rundown of the most interesting advanced infotainment solutions that are becoming increasingly popular and have been implemented by specific OEMs in recent times.

•     Larger display and video entertainment on demand  

With large displays and the increasingly widespread streaming of video and audio content into the vehicle, a substantial base is becoming established for the development of infotainment in the automotive industry.

It is all about introducing more immersive services and features in vehicles. This is especially relevant for EVs, because, after all, drivers need to pass their time somehow while charging their vehicles at public stations, and this can be done from the comfort of the car seat.

    Total spending on video on demand is expected to reach $127 billion by 2025       (11.8%       CAGR). Video streaming alone is expected to account for 86% of revenues       .  

The chart below illustrates how trends are changing in terms of screen sizes in today's  automotive market . It is noticeable that, roughly since 2017, there has been a marked increase in the use of large displays in vehicles.

Continuing down this path, major OEMs are already announcing in-vehicle video streaming services that use this type of display and video entertainment. Some of the latest examples from the market are as follows:

•     Jeep Wagoneer    and     Grand Wagoneer    from the Stellantis fleet have recently been offering Amazon Fire TV as infotainment for rear-seat passengers. It allows users to stream movies, shows, or games via a Wi-Fi hotspot or download content for later. The same service will also be implemented in vehicles from     Ford Motor Co    . factories.

•     Volvo    intends to implement a YouTube app, available via the Play Store in vehicles equipped with Google Automotive Services. The idea is to provide entertainment while charging an EV.

•     BMW    has recently unveiled its proprietary     Theater Screen    concept. This mode adjusts lighting and dimming, converting the car's interior into a mobile theater. The idea is supposed to be workable thanks to a 31-inch widescreen display. It offers a 32:9 aspect ratio and 8K resolution and includes built-in Amazon Fire TV and smart TV features.

•     Enhanced in-vehicle display capabilities with flexible interactive displays  

Design teams are persistently seeking ways to further integrate displays into the surface of a car's dashboard. Some of the most remarkable solutions in the automotive market today are  Flexible Interactive Displays (FITs).

They allow manufacturers to  provide more display areas for infotainment functions . All vehicle elements, such as Center consoles, pillars, and seat backs, can potentially be converted into one large screen. It could extend across the entire passenger space, turning the vehicle into a mobile movie theater or high-tech office. These types of solutions are already being tested by     ROYOLE.  

This technology is essential not only for convenience but also for safety. This is due to the idea of replacing layers of glass (traditionally used in automotive electronics) with durable plastic.

In the future, it might be possible to build sensors (e.g. fingerprint reader) into the display area. This will enable the  interface to be controlled from a screen spanning the entire surface.

•     The sound provides       a       more comfortable       experience  

While video content has only recently begun to make its way into cars, a good sound system and access to music have always been part of the automotive experience (in fact, for many consumers they were an essential factor in deciding whether to purchase a particular vehicle). Car manufacturers and designers are well aware of this and are stepping up their investments in the following solutions.

•     Noise reduction.    A focused anti-noise system prevents the penetration of noises common to driving but does not block sounds important to maintaining safety (such as emergency sirens). Next-generation active noise cancellation has been applied, for example, in the new Cadillac Lyriq, where it intelligently measures road vibrations and, using an AKG speaker system, actively reduces noises from outside.

•     In-vehicle Sound Zones.    This allows the driver and passengers to access different audio content. There is also an option to share the same sound at different volume levels. An additional advantage of such solutions is that unwanted music from the back seat does not distract the driver during the ride.

 Hyundai , for example, is testing such a solution. Whilst using the latest-generation  Separated Sound Zone  (SSZ) technology developed in-house, the Asian brand is able to create and control sound fields in the car. Both the driver and each passenger are able to hear isolated sounds without a headset.

•     Immersive solutions customized to the driver's lifestyle  

Key players in the market are becoming bolder in developing immersive technologies in their vehicles and emphasizing personalized options to tailor the journey to the user's specifications.

Developed by Mercedes-Benz,  MBUX Hyperscreen is a prime example. It is designed to provide  a high degree of individualized infotainment content. The vehicle users can determine for themselves which information to display in a specific place, in a specific order, and in line with a selected theme. In order to aggregate streaming content from various sources within its own vehicles, Mercedes has partnered with California-based ZYNC. The collaboration is expected to help develop a platform and interface that will allow access to digital entertainment from different providers.

The designers of the         NIO ES7     Electric SUV are taking it even further. This vehicle will be equipped with Banyan smart OS software and NIO's Aquila Super Sensing and Adam Super Computing platform. All of these will be combined with a compatible digital cockpit based on AR/VR.  This is a golden opportunity to"immerse" yourself in a world filled with colors and sounds . The passenger riding in the back can delight in a 200+ inch "screen" and a 7.1.4 Dolby Atmos sound system.

After all, the aforementioned EV is already being advertised by the developers as a "second living room," being a continuation of home entertainment. What is noteworthy is not just the audiovisual system itself, but also the options for dimming the lights or the appropriate choice of seat positions, which are supposed to make the viewer feel as if they are watching a movie at home. This is a perfect example of how  the automotive industry is getting closer to the users, their daily life  ,  and  their  habits.

Why is in-car infotainment a key to building long-term relationships and new revenue streams?

Tremendous competition and market oversupply make it more difficult than ever to commit a customer to a brand. User loyalty needs to be built in many possible ways and new ways to reach out with an offer need to be sought.

 Car infotainment is at the cusp of dynamic development and is certainly one of the leading areas that can help find new groups of customers and create lasting relationships with them.

This type of technology is no longer an add-on option, but an indication of "being up-to-date." Manufacturers who focus on the development of car infotainment become trendsetters and initiators of change. They make it clear that they follow their consumers, and understand their lifestyles and expectations. And this is not insignificant for the modern consumer.

 Research shows that customers are willing to pay more for a vehicle with these types of solutions - we're talking about up to $10,000 for a single car. In contrast, more than 70% of younger millennials list infotainment technology and features as "must-haves" when buying a car.

With these statistics in mind, it is necessary to remain competitive in this area and constantly develop the offer.

A special focus should be also put on the areas that build customer loyalty and connect them more strongly with the brand, for example:

•  access to media- based on a subscription model,
•  rich entertainment offerings for the driver and passengers;
•  personalization features and adjusting to users' lifestyles;
•  a fine-tuned user interface (UX), for a seamless and intuitive experience.

If you combine seamless and intuitive in-car infotainment with a deep understanding of consumer needs, chances for building long-term relationships and unlocking new revenue streams grow dramatically.

Have you ever wanted to build your own Android? It’s easy according to the official manual, but it’s getting harder on a Windows (or Mac) machine, or if you’d like to run it on physical hardware. Still too easy? Let’s build Android Automotive OS – the same source code, but another layer of complexity. In this manual, we’ll cover all steps needed to build and run Android Automotive OS 11 AOSP on Raspberry Pi 4B using Windows. The solution is not perfect, however. The most principal issue is a lack of Google Services because the entire AAOS is on an open-source project and Google doesn’t provide its services this way. Nevertheless, let’s build the open-source version first, and then we can try to face incoming issues.

TL;DR: If you don't want to configure and build the system step-by-step, follow the simplified instruction at  https://github.com/grapeup/aaos_11_local_manifest

Prerequisites

Hardware

If you want to run the system on a physical device, you need one. I use the Raspberry Pi 4 model B with 8GB of RAM (  https://www.raspberrypi.com/products/raspberry-pi-4-model-b/ ). By the way, if you want to build and run an emulator from the source, it’s also possible, but there is a small limitation – packaging the emulator to a zip file, moving it to another computer, or even running it under Android Studio was introduced in Android 12.

To power your Raspberry, you need a power adapter (USB C, min. 5V 3A). I use the Raspberry-official 5.1V 3A model. You can also power the Raspberry computer from your desktop/laptop’s USB port, especially if you’re going to debug it via a serial connection. Check the “If it doesn’t work” section below for the required hardware.

Another piece of hardware needed is an SD card. In theory, 4GB is all you need, however, I recommend buying a larger card to have some extra space for  your applications on Android . I use 32GB and 64GB cards. You’ll also need a built-in or external card reader. I use the latter.

The next step is a screen. It’s optional but fancy. You can connect your mouse and optionally keyboard to your Raspberry Pi via USB and connect any display you have via micro-HDMI but using a touch screen is much more intuitive. I use a Waveshare 10-inch screen dedicated to Raspberry (  https://www.waveshare.com/wiki/10.1inch_HDMI_LCD_(B)_(with_case ). The screen box has a place to screw the Raspberry too, so you don’t need any extra case. You can also buy it with a power adapter and a display cable.

If you don’t buy a bundle, make sure you have all necessary accessories: micro-HDMI – HDMI cable to connect a screen (Waveshare or any other), USB A – USB mini A cable to connect a touch sensor of the screen, USB mini A 5V 3A adapter to power the screen.

Of course, you need a computer. In this manual, we use a Windows machine with at least 512GB of storage (the Android source is huge) and 16GB of RAM.

Software

You can probably build everything in pure Windows, but the recommended method is to use WSL. I assume you already have it installed, so just make sure you have the newest WSL2 version. If you have never used WSL before, see the full manual here  https://learn.microsoft.com/en-us/windows/wsl/install .

 WSL adjustments

The standard WSL installation uses a too-small virtual drive and limited RAM, so you need to adjust it.

Let’s start with the disk. Make sure the WSL is shut down by running ‘wsl –shutdown’ in the command prompt. Open Windows Command Prompt with admin privileges and enter ‘diskpart  ’. Then run ‘select vdisk file=”<path to WSL drive file>”’. For me, the path is “C:\Users\<user>\AppData\Local\Packages\CanonicalGroupLimited.Ubuntu_<WSL_instance_id>\LocalState\ext4.vhdx  ”. Now you can expand it with the command ‘expand vdisk maximum=512000’. Around 300GB is enough for Android 11, but if you want to play with multiple branches of Android at the same time, you need more space. Now you can close the diskpart with the ‘exit’ command. Next, open the WSL and run ‘sudo resize2fs /dev/sdb 512000M’. I assume you have only a single drive attached to the WSL and it’s visible in the Linux subsystem as /dev/sdb. You can check it with the commands ‘sudo mount -t devtmpfs none /dev || mount | grep ext4’.

Now, let’s adjust the memory. Stop the WSL again. Open your home directory in Windows and open .wslconfig file. Create it if this file doesn’t exist yet. In the file, you need to create a [wsl2] section and memory configuration. The complete file should look like this:

[wsl2]
memory=16GB

As you can see, I’ve attached 16GB to the virtual machine. It’s assigned dynamically, according to needs, but you must be aware that the virtual machine can take all of it, so if you allow it to eat your entire RAM, it can force your Windows to use a hard disk to survive (which will slow everything down significantly).

 Disclaimer:

 Building Android on 8 cores, 16GB RAM machine takes around 4 hours. If you want to do it faster or you don’t have a computer powerful enough at your home or office, you can consider building in the cloud. Simple AWS EC2 with 32 cores and 64GB of memory does the job in one hour (to download and build) and costs just a few bucks.

Let's get ready to rumble!!!

..or at least to building.

More prerequisites

We need some software but not much. Just install the following packages. This set of libraries allows you to build Android Automotive OS versions 11 to 13.

sudo apt update && sudo apt install gcc-aarch64-linux-gnu libssl-dev bc python3-setuptools repo python-is-python3 libncurses5 zip unzip make gcc flex bison -y

Source code downloading

Let’s create a home directory for our android and download sources.

mkdir android-11.0.0_r48 && cd android-11.0.0_r48
repo init -u https://android.googlesource.com/platform/manifest -b android-11.0.0_r48 --partial-clone --clone-filter=blob:limit=10M
git clone https://github.com/android-rpi/local_manifests .repo/local_manifests -b arpi-11
repo sync

“repo init” will ask you for some personal data. It’s collected by Google. To learn more about optimizations here, check this manual:  https://docs.gitlab.com/ee/topics/git/partial_clone.html . ‘git clone’ adds a custom code from Android RPI project (  https://groups.google.com/g/android-rpi ) with drivers for your Raspberry Pi. The project is great and it’s all you need if you want to run Android TV. To run Android Automotive OS, we’ll need to adjust it slightly (see “Adjustments” section below). ‘repo sync’ will take some time because you need to download around 200GB of code. If you have a powerful machine with a great Internet connection, you can use more threads with ‘-j X’ parameter added to the command. The default thread count is 4. If you have already synchronized your source code without android-rpi local manifest, you need to add --force-sync to the ’repo-sync’ command.

Adjustments

 All changes from this section can download as a patch file attached to this article. See the “Path file” section below.

Android-rpi provides Android TV for Raspberry Pi. We need to remove the TV-related configuration and add the Automotive OS one.

Let’s start with removing unnecessary files. You can safely remove the following files and directories:

•  device/arpi/rpi4/overlay/frameworks/base/core/res/res/anim
•  device/arpi/rpi4/overlay/frameworks/base/core/res/res/values-television
•  device/arpi/rpi4/overlay/frameworks/base/core/res/res/values/dimens.xml
•  device/arpi/rpi4/overlay/frameworks/base/core/res/res/values/styles.xml
•  device/arpi/rpi4/overlay/frameworks/base/packages

To remove the user notice screen not needed in Automotive OS, create a new file device/arpi/rpi4/overlay/packages/services/Car/service/res/values/config.xml with the following content:

<?xml version="1.0" encoding="utf-8"?>
<resources xmlns:xliff="urn:oasis:names:tc:xliff:document:1.2">
<string name="config_userNoticeUiService" translatable="false"></string>
</resources>

To replace the basic TV overlay config with the Automotive overlay config, adjust the configuration in device/arpi/rpi4/overlay/frameworks/base/core/res/res/values/config.xml.

Remove:

•  <integer name="config_defaultUiModeType">4</integer> <!--disable forced UI_MODE_TYPE_TELEVISION, as there is only MODE_TYPE_CAR available now-->
•  <integer name="config_longPressOnHomeBehavior">0</integer> <!--disable home button long press action-->
•  <bool name="config_hasPermanentDpad">true</bool> <!--disable D-pad-->
•  <string name="config_appsAuthorizedForSharedAccounts">;com.android.tv.settings;</string> <!--remove unnecessary access for a shared account as there is nothing in com.android.tv.* now-->

… and add:

•  <bool name="config_showNavigationBar">true</bool> <!--enable software navigation bar, as there is no hardwave one-->
•  <bool name="config_enableMultiUserUI">true</bool> <!--enable multi-user, as AAOS uses background processes called in another sessions -->
•  <integer name="config_multiuserMaximumUsers">8</integer> <!--set maximum user count, required by the previous one-->

Now let’s rename the android-rpi original /device/arpi/rpi4/rpi4.mk to /device/arpi/rpi4/android_rpi4.mk. We need to adjust the file a little bit.

Remove the following variables definitions. Some of them you will re-create in another file, while some of them are not needed.

•  PRODUCT_NAME
•  PRODUCT_DEVICE
•  PRODUCT_BRAND
•  PRODUCT_MANUFACTURER
•  PRODUCT_MODEL
•  USE_OEM_TV_APP
•  DEVICE_PACKAGE_OVERLAYS
•  PRODUCT_AAPT_PRED_CONFIG
•  PRODUCT_CHARACTERISTICS

Remove the following invocations. We’re going to call necessary external files in another mk file.

•  $(call inherit-product, device/google/atv/products/atv_base.mk)
•  $(call inherit-product, $(SRC_TARGET_DIR)/product/core_64_bit_only.mk)
•  $(call inherit-product, $(SRC_TARGET_DIR)/product/languages_full.mk)
•  include frameworks/native/build/tablet-10in-xhdpi-2048-dalvik-heap.mk

In PRODUCT_PROPERTY_OVERRIDES remove debug.drm.mode.force=1280x720 and add the following properties. This way you remove the TV launcher configuration and override the default automotive launcher configuration.

•  dalvik.vm.dex2oat64.enabled=true
•  keyguard.no_require_sim=true
•  ro.logd.size=1m

Now you need to completely remove the android-rpi TV launcher and add RenderScript support for Automotive OS. In PRODUCT_PACKAGES remove:

•  DeskClock
•  RpLauncher

… and add:

•  librs_jni

Create a new rpi4.mk4 with the following content:

PRODUCT_PACKAGE_OVERLAYS += device/generic/car/common/overlay
$(call inherit-product, $(SRC_TARGET_DIR)/product/core_64_bit.mk)
$(call inherit-product, device/arpi/rpi4/android_rpi4.mk)
$(call inherit-product, $(SRC_TARGET_DIR)/product/full_base.mk)
$(call inherit-product, device/generic/car/common/car.mk)
PRODUCT_SYSTEM_DEFAULT_PROPERTIES += \
   android.car.number_pre_created_users=1 \
   android.car.number_pre_created_guests=1 \
   android.car.user_hal_enabled=true
DEVICE_PACKAGE_OVERLAYS += device/arpi/rpi4/overlay device/generic/car/car_x86_64/overlay

PRODUCT_NAME := rpi4
PRODUCT_DEVICE := rpi4
PRODUCT_BRAND := arpi
PRODUCT_MODEL := Raspberry Pi 4
PRODUCT_MANUFACTURER := GrapeUp and ARPi

Due to the license, remember to add yourself to the PRODUCT_MANUFACTURER field.

Now you have two mk files – android-rpi.mk is borrowed from android-rpi project and adjusted, and rpi.mk contains all changes for Automotive OS. You can meld these two together or split them into more files if you’d like, but keep in mind that the order of invocations does matter (not always, but still).

As Android Automotive OS is bigger than Android TV, we need to increase the system partition size to fit the new image. In device/arpi/rpi4/BoardConfig.mk increase BOARD_SYSTEMIMAGE_PARTITION_SIZE to 2147483648, which means 2GB.

You need to apply all changes described in  https://github.com/android-rpi/device_arpi_rpi4/wiki/arpi-11-:-framework-patch too. Those changes are also included in the  patch file attached .

If you use the 8GB version of Raspberry Pi, you need to replace device/arpi/rpi4/boot/fixup4.dat and device/arpi/rpi4/boot/start4.elf files. You can find the correct files in the patch file attached or you may use the official source:  https://github.com/raspberrypi/firmware/tree/master/boot . It’s probably not needed for 4GB version of Raspberry, but I don’t have such a device for verification.

Path file

If you prefer to apply all changes described above as a single file, go to your sources directory and run ‘git apply --no-index <path_to_patch_file>  ’. There is also a boot animation replaced in the  patch file . If you want to create one of your own, follow the official manual here:  https://android.googlesource.com/platform/frameworks/base/+/master/cmds/bootanimation/FORMAT.md .

Now we can build!

That’s the easy part. Just run a few commands from below. Firstly, we need to build a custom kernel for Android. ‘merge_config.sh’ script just configures all variables required. The first ‘make’ command builds the real kernel image (which can take a few minutes). Next, build a device tree configuration.

cd kernel/arpi
ARCH=arm64 scripts/kconfig/merge_config.sh arch/arm64/configs/bcm2711_defconfig kernel/configs/android-base.config kernel/configs/android-recommended.config
ARCH=arm64 CROSS_COMPILE=aarch64-linux-gnu- make Image.gz
ARCH=arm64 CROSS_COMPILE=aarch64-linux-gnu- DTC_FLAGS="-@" make broadcom/bcm2711-rpi-4-b.dtb
ARCH=arm64 CROSS_COMPILE=aarch64-linux-gnu- DTC_FLAGS="-@" make overlays/vc4-kms-v3d-pi4.dtbo
cd ../..

The next part is to build the entire system. “envsetup.sh” script sets up variables and adds custom commands to your terminal. Then you can pick the correct pair of Android versions and devices with “lunch”. You can run it without parameters to see (almost) all possible configurations. In this step, you can decide to build a system for dedicated hardware (eg. Dragonboard) and switch between phone/tablet/TV/wearable/automotive versions of Android. The last line is a real building. We can’t run just “make” or “m”, as documented in the official manual because we need to create three specific images to write them on an SD card and run them on Raspberry Pi. Replace “X” in ‘-j X’ with the number of threads you want to use. The default value is the number of logical processors on your computer.

source build/envsetup.sh
lunch rpi4-eng
make -j X ramdisk systemimage vendorimage

I hope you have a delightful book next to you because the last building takes a few hours depending on your hardware. Good news!  If you need to adapt something and build again, in most cases you just need the three last lines (or even just the very last one) – to source the environment setup, to pick the lunch configuration, and to make ramdisk, system, and vendor images. And it takes hours for the first time only.

Creating an SD card

This step seems to be easy, but it isn’t. WSL doesn’t contain drivers for the USB card reader. You can use usbip to forward a device from Windows to the subsystem, but it doesn’t work well with external storage without partitions. The solution is a VirtualBox with Ubuntu installed. Just create a virtual machine, install Ubuntu, and install Guest Additions. Then you can connect the card reader and pass it to the virtual machine. If you’re a minimalist, you can use Ubuntu Server or any other Linux distribution you like. Be aware that using a card reader built into your computer may be challenging depending on drivers and the hardware connection type (USB-like, or PCI-e).

Now, you need to create a partition schema on the SD card. I assume the card is loaded to the system as /dev/sdb. Check your configuration before continuing to avoid formatting your main drive or another disaster. Let’s erase the current partition table and create a new one.

sudo umount /dev/sdb*
sudo wipefs -a /dev/sdb
sudo fdisk /dev/sdb

Now let’s create partitions. First, you need a 128MB active partition of the W95 FAT32 (LBA) type, second a 2GB Linux partition, third a 128MB Linux partition, and the rest of the card for user data (also Linux partition). Here’s how to navigate through fdisk menu to configure all partitions.

Welcome to fdisk (util-linux 2.37.2).
Changes will remain in memory only, until you decide to write them.
Be careful before using the write command.
Device does not contain a recognized partition table.
Created a new DOS disklabel with disk identifier 0x179fb9bc.
Command (m for help): n
Partition type
  p   primary (0 primary, 0 extended, 4 free)
  e   extended (container for logical partitions)
Select (default p):
Using default response p.
Partition number (1-4, default 1):
First sector (2048-61022207, default 2048):
Last sector, +/-sectors or +/-size{K,M,G,T,P} (2048-61022207, default 61022207): +128M
Created a new partition 1 of type 'Linux' and of size 128 MiB.
Command (m for help): a
Selected partition 1
The bootable flag on partition 1 is enabled now.
Command (m for help): t
Selected partition 1
Hex code or alias (type L to list all): 0c
Changed type of partition 'Linux' to 'W95 FAT32 (LBA)'.
Command (m for help): n
Partition type
  p   primary (1 primary, 0 extended, 3 free)
  e   extended (container for logical partitions)
Select (default p):
Using default response p.
Partition number (2-4, default 2):
First sector (264192-61022207, default 264192):
Last sector, +/-sectors or +/-size{K,M,G,T,P} (264192-61022207, default 61022207): +2G
Created a new partition 2 of type 'Linux' and of size 2 GiB.
Command (m for help): n
Partition type
  p   primary (2 primary, 0 extended, 2 free)
  e   extended (container for logical partitions)
Select (default p):
Using default response p.
Partition number (3,4, default 3):
First sector (4458496-61022207, default 4458496):
Last sector, +/-sectors or +/-size{K,M,G,T,P} (4458496-61022207, default 61022207): +128M
Created a new partition 3 of type 'Linux' and of size 128 MiB.
Command (m for help): n
Partition type
  p   primary (3 primary, 0 extended, 1 free)
  e   extended (container for logical partitions)
Select (default e): p
Selected partition 4
First sector (4720640-61022207, default 4720640):
Last sector, +/-sectors or +/-size{K,M,G,T,P} (4720640-61022207, default 61022207):
Created a new partition 4 of type 'Linux' and of size 26,8 GiB.
Command (m for help): w
The partition table has been altered.
Calling ioctl() to re-read partition table.
Syncing disks.

Be careful with the last partition – fdisk proposes creating an extended one by default, which is not needed in our use case.

If you don’t do it for the first time on the same card, you may see a warning that some partition already contains a file system signature. You can safely agree to remove it.

Partition #4 contains a ext4 signature.
Do you want to remove the signature? [Y]es/[N]o: Y
The signature will be removed by a write command.

Now, let’s supply file systems for the first and the last partitions.

sudo mkdosfs -F 32 /dev/sdb1
sudo mkfs.ext4 -L userdata /dev/sdb4

We won’t write anything to the last one, as it’s for user data only and will be filled by Android during the first boot. But we need to write some files for the first one. Let’s create a temporary mount directory under /mnt/p1 (like “partition 1”), mount it, and copy the necessary files from the Android built in the  earlier section. It’s strange, but we’re going to copy files from one virtual machine (WSL) to another (VirtualBox). You can simply mount a wsl drive as a shared folder in VirtualBox. If you don’t see a WSL drive in your Windows Explorer, you can map it as a network drive using “\\wsl$\Ubuntu” path

sudo mkdir /mnt/p1
sudo mount /dev/sdb1 /mnt/p1
sudo mkdir /mnt/p1/overlays
cd <PATH_TO_YOUR_ANDROID_SOURCES_IN_WSL>
sudo cp device/arpi/rpi4/boot/* /mnt/p1
sudo cp kernel/arpi/arch/arm64/boot/Image.gz /mnt/p1
sudo cp kernel/arpi/arch/arm64/boot/dts/broadcom/bcm2711-rpi-4-b.dtb /mnt/p1
sudo cp kernel/arpi/arch/arm/boot/dts/overlays/vc4-kms-v3d-pi4.dtbo /mnt/p1/overlays/
sudo cp out/target/product/rpi4/ramdisk.img /mnt/p1
sudo umount /mnt/p1
sudo rm -rf /mnt/p1

If you’re looking at the official android-rpi project manual, there is a different path for vc4-kms-v3d-pi4.dtbo file. That’s OK – they use a symbolic link we are unable to use in this filesystem.

Sometimes, you can see an error message when creating an “overlays” directory. It happens from time to time, because “mount” returns to the console before really mounting the drive. In such a case, just call “mkdir” again. Be aware of that, especially if you’re going to copy-paste the entire listing from above.

Now, let’s copy the two remaining partitions. If you’re struggling with dd command (it may hang), you can try to copy big *.img files from WSL to VirtualBox first.

cd <PATH_TO_YOUR_ANDROID_SOURCES_IN_WSL>/out/target/product/rpi4/
sudo dd if=system.img of=/dev/sdb2 bs=1M status=progress
sudo dd if=vendor.img of=/dev/sdb3 bs=1M status=progress

Congratulations!

You’re done. You’ve downloaded, prepared, built, and saved your own Android Automotive OS. Now you can put the SD card into Raspberry, and connect all cables (make sure you connect the Raspberry power cable at the end). There is no “power” button, and it doesn’t matter which micro-HDMI or USB port of Raspberry you use. It’s now time to enjoy your own Android Automotive OS!

If it doesn’t work

The world is not perfect and sometimes something goes terribly wrong. If you see the boot animation for a long time, or if your device crashes in a loop a few seconds after boot, you can try to debug it.

You need a USB-TTL bridge (like this one  https://www.sunrom.com/p/cp2102-usb-ttl-uart-module ) to connect the correct pins from the Raspberry to the USB. You need to connect pin 6 (ground) to the GND pin in the bridge, pin 8 (RXD) to the RXD pin of the bridge and pin 10 (TXD) to the TXD pin of the bridge. If you want to power the Raspberry via the bridge, you need to also connect pin 2 to +5V pin of the bridge. It is not recommended, because of the lower voltage, so your system might be unstable. If you don’t have a power adapter, you can simply connect a USB cable between your computer port and the USB C port of the Raspberry.  Warning! You can’t connect both a +5V connector here and a USB C power port of the Raspberry or you’ll burn the Raspberry board.

See the schema for the connection reference.

Depending on your bridge model, you may need an additional driver. I use this one:  https://www.silabs.com/developers/usb-to-uart-bridge-vcp-drivers?tab=downloads .

When you connect the +5V pin or USB-C power adapter (  again, never both at the same time! ), the Raspberry starts. Now you can open Putty and connect to your Android. Pick Serial and type COMX in the serial line definition. X is the number of your COM port. You can check it in your device manager – look for “USB to UART bridge (COM4)” or the like. The correct connection speed is 115200.

Open the connection to access the Android shell. By default, Android spawns all logs to the standard output, so you should see a lot of them right away. Anyway, it’s dual-side communication and you have full terminal access to your Android if you need to check/modify any file or call any command. Just strike enter to see the command prompt. You can even call ‘su’ to gain superuser access on your Android running on Raspberry.

Connecting via adb

If you want to use Android Debug Bridge to connect to your device, using a USB bridge is not enough. When running ‘adb devices’ on your computer, the Android Automotive OS running on Raspberry is not recognized. You can use a putty connection to turn on a TCP debug bridge instead.

Make sure you’ve connected Android and your computer to the same network. Open putty and connect to the running Android console. Log as root and enable ADB via TCP. Then check your IP address.

su
setprop service.adb.tcp.port 5555
stop adbd
start adbd
ifconfig wlan0

Now, using your Windows command line, go to the Android SDK platform-tools directory and connect to your device. As you can see, the IP address of mine Raspnberry is 192.168.50.47.

cd %userprofile%\AppData\Local\Android\Sdk\platform-tools
adb connect 192.168.50.47:5555

If you want to use ADB in WSL, you can link the Windows program in WSL using the following command.

sudo ln -s /mnt/c/Users/<your_username>/AppData/Local/Android/Sdk/platform-tools/adb.exe /usr/bin/adb

You can now use ADB to use logcat without putty or to install applications without manually transferring APK files to the SD card. Fun fact – if you use a USB bridge and USB power supply, you have two physical connections between your computer and the Android-running one, however, you still need to use ADB over WiFi to use the debug bridge.

Summary: Android Automotive OS on Raspberry Pi 4B

That’s all. Android Automotive OS 11 is running. You can install the apps you need, take them to your car, or do whatever you’d like with them.  Using hardware instead of an emulator allows you to manually manage partitions (e.g. for the  OTA update ) and connect external devices like a real GPS receiver or accelerometer. The bare metal hardware overperforms the emulator too. And most importantly – you can easily take it to your car, receive power from an in-car USB port, connect it to an ODB-II port and run real-life tests without a laptop.

 Is your project ready? Great, now you can     try doing the same with AAOS 13    .

 Digital twins, or virtual copies of material objects, are being used in various types of simulations and the automotive industry is tapping into the potential offered by this technology. Representatives of this market can comprehensively monitor equipment and systems and prevent numerous failures. But what does the future hold for Digital Twin solutions, and who will play the leading role in their development in the years ahead?

The concept of Digital Twin today

To get started, let's have a few words of reminder. A virtual model called a digital twin is based on data from an actual physical object, equipped with special sensors. The collected information allows to the creation of a simulation of the object’s behavior in the real world, while testing takes place in virtual space.

The concept of Digital Twins is developing by leaps and bounds, with its origins dating back to 2003.  For many years, more components have been added to this technology . Currently, we distinguish the following:

•  digital (virtual) aspect,
•  physical object,
•  the connection between the two,
•  data,
•  services.

The last two were added to the classification by experts only in recent years. This was triggered by developments such as machine learning,  Big Data , IoT, and cybersecurity technologies.

Capabilities of digital twins in automotive

Digital twins are excelling in many fields when it comes to working on high-tech cars, especially those connected to the network. Below are selected areas of influence.

Designing the vehicle

3D modeling is a way of designing that has been around for many years in the widespread automotive manufacturing industry. But this one is not standing still, and the growing popularity of digital twins is proof of that.  Digital replicas extend the concept of physical 3D modeling to virtual representations of software, interactive systems, and usage simulations. As such, they take the conceptual process to a higher level of sophistication.

Production stage

Design is not everything.  In fact, the technology mentioned above also works well at the production stage . First and foremost, DT's solutions facilitate control over advanced manufacturing techniques. Since virtual twins improve real-time monitoring and management of facilities, they support  the construction of increasingly complex products.

Besides,  the safety of the work itself during the production of cars and parts adds to the issue. By  simulating manufacturing processes , digital twins contribute to the creation of appropriate employment conditions.

Advanced event prediction

Virtual copies have the ability to simulate the physical state of a vehicle and thus predict the future. Predictive maintenance in this case is based on such reliable data as  temperature, route, engine condition, or driver behavior. This can be used to ensure optimal vehicle performance.

Aspects of cyber security

DT predicted for  automotive software can help simulate the risk of data theft or other cybersecurity threats. The digital twin of the whole Datacenter can be created to simulate different attack vectors. Continuous software monitoring is also helpful in the early detection of vulnerabilities to hacking attacks (and more)

Development of security-improving systems

Virtual replicas of vehicles and the real world also enable the prediction of specific driving situations and potential vehicle responses. This is valuable knowledge that can be used, for example, to further develop ADAS systems such as electronic stability control and autonomous driving. This is all aimed at ensuring safer, faster, and more economical driving.

How will the digital twin trend evolve in the coming years?

One of the leading trend analysis companies from the automotive world has developed its own prediction of the development of specific sub-trends within the scope of the digital twin. In this regard, the experts analyzed such areas of development as:

•  Predictive Maintenance.
•  Powertrain Control (e.g. vehicle speed and other software parameters).
•  Cybersecurity.
•  Vehicle Manufacturing.
•  Development and Testing.

The analysis shows that all of the above issues will move into the mainstream  in the third decade of the 21st century. On the other hand, some of them will develop at a slower pace in the years to come, while others will develop at a slightly higher rate.

 Subtrend Powertrain Control will have a lot to say. As early as around 2025, we will see that basic control parameters will be defined and tested primarily in the digital twin.

 To a lesser extent, but still, Development and Testing solutions will also be implemented. DTs will be created to simulate systems in such a way as to accelerate development processes.  The same will be true in the area of Predictive Maintenance. Vehicle condition information will soon be sent in bulk to the cloud or database. There, a virtual copy will be used to predict how certain changes will affect maintenance needs.

Key players in DT development in automotive

The market is already witnessing the emergence of brands that will push (with varying intensity) DT technology in the broader automotive sector (cars, software, parts). Specifically standing out in this regard are:

•  Tesla,
•  BOSCH,
•  SIEMENS,
•  Porsche,
•  Volkswagen,
•  Continental.

 Both OEMs and Suppliers will shift their focus to the Development and Testing area. The proportions are somewhat different in the case of Vehicle Manufacturing, as this slice of the pie tends to go to OEMs for the time being. However, it is possible that parts manufacturers will also get their share before long. On the other hand, without any doubt,  the area of Cybersecurity already belongs to OEMs , and the percentage of such companies that use DT to improve cybersecurity is prevalent.

The digital twin and the future of automotive brands

The digital twin is a solution that helps address mature challenges specific to the entire modern automotive industry. It supports digitization processes and data-driven decision-making. Manufacturers can apply this technology at all stages of the production process, thus eliminating potential abnormalities.

 In the upcoming years, we can expect DT-type applications to become more common, especially among OEMs.

So what are brands supposed to do if they want to secure a significant position in a market where the DM trend is becoming highly relevant? First, it's a good idea if they collaborate with those driving change. Second, it'  s worth adopting a specific strategy, as not every sub-trend needs to be addressed in every scenario. This is brilliantly illustrated in the SBD chart below. The authors of this chart recommend certain behaviors, breaking them down into specific categories and relating them to specific market participants.

Based on this overview, it's good to see that the leaders don't have too much choice, and over the next 12 months, they should be releasing solutions that fall into every sub-trend.  The issue of cyber security is becoming essential as well . The digital twins have great potential in developing it, so basically all stakeholders should focus on this area.

One of the most crucial technical decisions during designing API is to choose the proper protocol for interchanging data. It is not an easy task. You have to answer at least a few important questions - which will integrate with API, if you have any network limitations, what is the amount and frequency of calls, and will the level of your organization's technological maturity allow you to maintain this in the future?

When you gather all the information, you can compare different technologies to choose one that fits you best. You can pick and choose between well-known SOAP, REST, or GraphQL. But in this article, we would like to introduce quite a new player in the microservices world - gRPC Remote Procedure Call.

What is gRPC (Remote Procedure Call)?

gRPC is a cross-platform open-source Remote Procedure Call (RPC) framework initially created by Google. The platform uses Protocol Buffers as a data serialization protocol, as the binary format requires fewer resources and messages are smaller. Also, a contract between the client and server is defined in proto format, so code can be automatically generated. The framework relies on HTTP/2 (supports TLS) and beyond performance, interoperability, and code generation offers streaming features and channels.

Declaring methods in contract

Have you read our article about serializing data with Protocol Buffers ? We are going to add some more definitions there:

message SearchRequest {

string vin = 1;

google.protobuf.Timestamp from = 2;

google.protobuf.Timestamp to = 3;

}



message SearchResponse {

repeated Geolocation geolocations = 1;

}



service GeolocationServer {

rpc Insert(Geolocation) returns (google.protobuf.Empty);

rpc Search(SearchRequest) returns (SearchResponse);

}

The structure of the file is pretty straightforward - but there are a few things worth noticing:

• service GeolocationServer - service is declared by keyword with that name
• rpc Insert(Geolocation) - methods are defined by rpc keyword, its name, and request parameter type
• returns (google.protobuf.Empty) - and at the end finally a return type. As you can see you have to always return any value, in this case, is a wrapper for an empty structure
• message SearchResponse {repeated Geolocation geolocations = 1}; - if you want to return a list of objects, you have to mark them as repeated and provide a name for the field

Build configuration

We can combine features of Spring Boot and simplify the setup of gRPC server by using the dedicated library GitHub - yidongnan/grpc-spring-boot-starter: Spring Boot starter module for gRPC framework. (follow the installation guide there) .

It let us use all the goodness of the Spring framework (such as Dependency Injection or Annotations).

Now you are ready to generate Java code! ./gradlew generateProto

Server implementation

To implement the server for our methods definition, first of all, we have to extend the proper abstract class, which had been generated in the previous step:

public class GeolocationServer extends GeolocationServerGrpc.GeolocationServerImplBase

As the next step add the @GrpcService annotation at the class level to register gRPC server and override server methods:

@Override

public void insert(Geolocation request, StreamObserver<Empty> responseObserver) {

GeolocationEvent geolocationEvent = convertToGeolocationEvent(request);

geolocationRepository.save(geolocationEvent);



responseObserver.onNext(Empty.newBuilder().build());

responseObserver.onCompleted();

}



@Override

public void search(SearchRequest request, StreamObserver<SearchResponse> responseObserver) {

List<GeolocationEvent> geolocationEvents = geolocationRepository.searchByVinAndOccurredOnFromTo(

request.getVin(),

convertTimestampToInstant(request.getFrom()),

convertTimestampToInstant(request.getTo())

);



List<Geolocation> geolocations = geolocationEvents.stream().map(this::convertToGeolocation).toList();



responseObserver.onNext(SearchResponse.newBuilder()

.addAllGeolocations(geolocations)

.build()

);

responseObserver.onCompleted();

}

• StreamObserver<> responseObserver - stream of messages to send
• responseObserver.onNext() - writes responses to the client. Unary calls must invoke onNext at most once
• responseObserver.onCompleted() - receives a notification of successful stream completion

We have to convert internal gRPC objects to our domain entities:

private GeolocationEvent convertToGeolocationEvent(Geolocation request) {

Instant occurredOn = convertTimestampToInstant(request.getOccurredOn());

return new GeolocationEvent(

request.getVin(),

occurredOn,

request.getSpeed().getValue(),

new Coordinates(request.getCoordinates().getLatitude(), request.getCoordinates().getLongitude())

);

}



private Instant convertTimestampToInstant(Timestamp timestamp) {

return Instant.ofEpochSecond(timestamp.getSeconds(), timestamp.getNanos());

}

Error handling

Neither client always sends us a valid message nor our system is resilient enough to handle all errors, so we have to provide ways to handle exceptions.

If an error occurs, gRPC returns one of its error status codes instead, with an optional description.

We can handle it with ease in a Spring’s way, using annotations already available in the library:

@GrpcAdvice

public class GrpcExceptionAdvice {



@GrpcExceptionHandler

public Status handleInvalidArgument(IllegalArgumentException e) {

return Status.INVALID_ARGUMENT.withDescription(e.getMessage()).withCause(e);

}

}

• @GrpcAdvice - marks the class as a container for specific exception handlers
• @GrpcExceptionHandler - method to be invoked when an exception specified as an argument is thrown

Now we ensured that our error messages are clear and meaningful for clients.

gRPC - is that the right option for you?

As demonstrated in this article, gRPC integrates well with Spring Boot, so if you’re familiar with it, the learning curve is smooth.

gRPC is a worthy option to consider when you’re working with low latency, highly scalable, distributed systems. It provides an accurate, efficient, and language-independent protocol.

Check out the official documentation for more knowledge! gRPC

 The driving properties or the external appearance of cars, which used to serve as a differentiator between manufacturers, no longer play a key marketing role today. It is the car's software that has become the new growth engine for the automotive industry. Yet, the question remains where this software should come from and whether it pays to use a free-access license. Here we compare the most popular automotive open-source solutions.

What exactly is Open Source Software in the automotive industry?

Most of the software developed by the major automotive companies is copyrighted to other players in the market. Does this mean that being a less well-resourced player, it is impossible to thrive in the SDV sector? Not necessarily, and one of the solutions may be to take advantage of  open-source software (OSS).

A characteristic of such access is that  the source code is freely available to programmers under certain licensing conditions.

Flexible customization to meet your needs

It is important to know that OSS does not necessarily entail that a given vehicle manufacturer is "doomed" to certain functionalities. After all, the operating system, even if based on publicly available code, can then be developed manually.

The programmer is therefore authorized to benefit from free libraries, and cut and paste individual values into the code at will,  modifying the content of the whole .

OSS is gaining ground

According to Flexera's research, more than 50% of all code written globally today runs on open source. That's a large percentage, which reflects the popularity of free software.

The OSS trend  has also gained importance in the automotive industry in recent years, with OEMs trying with all their might to keep up with technological advances and new consumer demands. According to the same study, between 50% and 70% of the automotive software stack today comes from open source.

In contrast, Black Duck software audits of commercial applications demonstrate that open-source components are predicted to account for 23% of automotive applications.

Automotive Open-Source Software implies a number of benefits. But can we already talk about a revolution?

Why is the mentioned solution so popular nowadays? In fact, there are several reasons.

•     Allows minimizing costly investments (budget saved can be used as a way of developing other solutions).  
•     Enables vehicle manufacturers to offer consumers a fresh and compelling digital experience    .
•     Contributes to faster business growth    due to reduced expenses and "tailor-made" software development teams.
•     Provides benefits to consumers    by making cars safer with more reliable data.
•     It is used to maximize product agility cost-effectively.  

Clearly, these arguments are quite strong. Yet, to be able to talk about a revolution and a complete transition to OSS in the automotive industry, it will still take some more time.  After all, at present, this is applied mainly to selected vehicle functions, such as entertainment.

Nevertheless, some companies are already embracing free licensing, seeing it as a new business model. The potential is certainly substantial, although not yet fully harnessed. For instance, it is said to be very difficult to meet all the requirements of SDV, including those related to digital security issues, as we write later in the article.

Examples of open-source solutions in the auto industry

Automotive Grande Linux

The Linux operating system is a prime example of the power of an open-source solution.  The base of this tech giant ranks among the top operating systems worldwide, especially when talking about automotive.

 The Automotive Grade Linux (AGL) project is particularly noteworthy here, as it brings together manufacturers, suppliers, and representatives of technology companies. AGL platform, with Linux at its core, develops an open software platform from the ground up that can serve as the de facto industry standard, enabling the rapid development of the connected car market. Automotive companies, including Toyota, already leverage Linux open-source for automotive.

As of today, AGL (hosted by the Linux Foundation, the world's) is the only organization that seeks to fully aggregate all the functionalities of modern vehicles into Open-Source software. This includes such areas as:

•  Infotainment System – UCB 8.0 currently available, SDK available.
•  Instrument Cluster – device profile available with UCB 6.0 (Funky Flounder).
•  Telematics – device profile available with UCB 6.0 (Funky Flounder).
•  Heads-up Display (HUD).
•  Advanced Driver Assistance Systems (ADAS).
•  Functional Safety.
•  Autonomous Driving.

The founders of the project assume that in the current reality it is becoming obvious that the amount of code needed to support autonomous driving is too large for any one company to develop it independently. That's why they are  the first in the world aiming to create a coherent OSS ecosystem for the automotive industry.

Red Hat In-Vehicle Operating System

A competitive approach is being adopted by Red Hat, which has also mushroomed into a group of free software innovators in connected cars. Their proprietary solution,  Red Hat In-Vehicle Operating System, is designed to help automakers integrate  software-defined vehicle technology into their production line faster than ever.

General Motors and Qualcomm Technologies Inc. have already declared their interest in such an approach.

Part of the mission of the above-mentioned company is to develop certified functional safety systems built on Linux with functional safety certification (ASIL-B) to support critical in-vehicle applications. IVOS from Red Hat is currently (Fall 2022) being tested on the  Snapdragon® Digital Chassis™ . This is a set of cloud-connected platforms for telematics and connectivity, digital cockpit, and advanced driver assistance systems. This collaboration is intended to provide:

•  faster implementation of new digital services and innovative new features connected to the cloud,
•  new opportunities for more in-depth customer engagement,
•  the ability to update services over the vehicle's lifetime via the cloud,
•  the option of gaining expanded capabilities to perform simple and efficient vehicle updates and maintain functional safety,
•  the ability to redefine the driving experience for customers by ensuring seamless connectivity and enhanced intelligence.

Android Automotive OS

Great opportunities are also offered by the software based on a system featuring a distinctive green robot in its logo.

 Android Automotive OS (AAOS), as its name is known, is earning increasing recognition across the globe. This is no coincidence, as it allows car companies to provide customers with the most tailor-made experience. Polestar and Volvo were among the first to introduce Android Automotive OS to their  Polestar 2 and XC40 Recharge, andrecently  Renault has done this with  Megane E-Tech.

Other brands have followed suit. Manufacturers such as  PSA, Ford, Honda, and GM have already declared their intention to incorporate AAOS into the vehicles they develop.

Part of the implementations come with Google Automotive Services (GAS): Play Store, Google Maps, Google Assistant, and other parts without, their own app stores, and assistants.

Here are selected capabilities of the above-mentioned software:

•  AAOS being an integral part of the car brings ideas about controlling features of a car, or at least reading them and reacting within an application accordingly. Emulation provides just a few options to simulate car state, ignition, speed, gear, parking brake, low fuel level, night mode, and environment sensors(temperature, pressure, etc.).
•  There is still a requirement to follow design patterns for automotive, and Google is providing a whole design system page.
•  Applications submitted to the store are mandatory for an additional review.
•  Right now, the documentation states that supported categories for Android Automotive OS apps are focused on in-vehicle infotainment systems: Media, Navigation, Point of Interest, and Video.

Regrettably, though Android has a lot of potential, it still has limitations in terms of functionality and capabilities. Hence, it cannot be described as an ideal solution at this point. We wrote more about these issues and  possible solutions to AAOS .

Meanwhile, if you are interested in automotive  implementation using Android read this guide.

COVESA / Genivi

The embedded Android Automotive system in vehicles requires proper integration with existing software and with other systems found in the car (for safety, car data, etc.). The  Android Automotive SIG project, led by GENIVI, was created with large-scale rollouts in mind.

The premise of the  AASIG Android Development Platform is that OEMs, their suppliers, and the broader cockpit software ecosystem can easily and successfully identify both the shortcomings and requirements. This is intended to be done in close collaboration with Google's Android Automotive team.

Among the issues addressed are the following:

•  safety,
•  access to vehicle information,
•  responsibility for long-term maintenance,
•  multi-display operation,
•  audio management,
•  extensions for Android in the automotive environment,
•  keeping the in-vehicle system updated to support new Android versions,
•  outlining the boundaries within which Tier 1/OEM suppliers must take over major responsibility for supporting Google's Android Automotive team.

As can be seen, in the case of Android, there are a number of hot spots that need to be properly dealt with.

What limitations do you need to be aware of?

Ensuring a high level of security in safety-critical automotive environments has always posed a major challenge for Open-Source Software.  This is because you have to reconcile customer expectations while also ensuring data protection.

Certainly, open-source software has more vulnerabilities than dedicated software and thus  is more susceptible to hacker attacks. Even a single exploit can be used to compromise hundreds of thousands of applications and websites. Obviously,  static and dynamic application security testing (SAST and DAST) can be implemented to identify coding errors. However, such testers do not perform particularly well in identifying vulnerabilities in third-party code.

So if you plan to use  connected car technology ,  you need to examine the ecosystem of software used to deliver these functions. It is also critical to properly manage open-source software in your overall security strategy.

OSS opportunities and challenges

All told, until some time ago, OSS was mainly focused on entertainment. Besides, OEMs have historically been forced to choose between only a few software stacks and technologies. But today they are faced with a rapidly growing number of OSS proposals, APIs, and other solutions.

 On top of that, they have a growing number of partners and tech companies to collaborate with. And initiatives such as Autoware and Apollo shift their focus toward applications relevant to the safety and comfort of autonomous vehicles. Of course, these opportunities are also coupled with challenges, such as those related to  security or license compliance . On the other hand, this still does not negate the enormous potential of open-source software.

It can be hypothesized that in the long term, a complete transition to SDV will require manufacturers to make optimal use of open-source software. And this will include an increasing range of vehicle functionality.  This is an obvious consequence of the rapidly changing automotive market (which in a way forces the search for agile solutions) and growing consumer and infrastructure demands.

Sooner or later, major OEMs and the automotive community will have to face a decision and choose: either proprietary comfort (such as CARIAD from Volkswagen) or the flexibility offered by OSS projects.

In a world of microservices, we often have to pass information between applications. We serialize data into a format that can be retrieved by both sides. One of the serialization solutions is Protocol Buffers (Protobuf) - Google's language-neutral mechanism. Messages can be interpreted by a receiver using the same or different language than a producer. Many languages are supported, such as Java, Go, Python, and C++.

A data structure is defined using neutral language through  .proto files. The file is then compiled into code to be used in applications. It is designed for performance. Protocol Buffers encode data into binary format, which reduces message size and improves transmission speed.

Defining message format

This  .proto the file represents geolocation information for a given vehicle.

1 syntax = "proto3";

2

3 package com.grapeup.geolocation;

4

5 import "google/type/latlng.proto";

6 import "google/protobuf/timestamp.proto";

7

8 message Geolocation {

9  string vin = 1;

10  google.protobuf.Timestamp occurredOn = 2;

11  int32 speed = 3;

12  google.type.LatLng coordinates = 4;

13}

1 syntax = "proto3";

Syntax refers to Protobuf version, it can be  proto2 or  proto3 .

1package com.grapeup.geolocation;

Package declaration prevents naming conflicts between different projects.

1 message Geolocation {

2  string vin = 1;

3  google.protobuf.Timestamp occurredOn = 2;

4  int32 speed = 3;

5  google.type.LatLng coordinates = 4;

6}

Message definition contains a name and a set of typed fields. Simple data types are available, such as bool, int32, double, string, etc. You can also define your own types or import them.

1google.protobuf.Timestamp occurredOn = 2;

The  = 1 ,  = 2 markers identify the unique tag. Tags are a numeric representation for the field and are used to identify the field in the message binary format. They have to be unique in a message and should not be changed once the message is in use. If a field is removed from a definition that is already used, it must be  reserved .

Field types

Aside from scalar types, there are many other type options when defining messages. Here are few, but you can find all of them in the Language Guide  Language Guide (proto3)  |  Protocol Buffers  |  Google Developers .

 Well Known Types

1 import "google/type/latlng.proto";

2 import "google/protobuf/timestamp.proto";

3

4 google.protobuf.Timestamp occurredOn = 2;

5 google.type.LatLng coordinates = 4;

There are predefined types available to use  Overview  |  Protocol Buffers  |  Google Developers . They are known as Well Know Types and have to be imported into  .proto .

 LatLng represents a latitude and longitude pair.

 Timestamp is a specific point in time with nanosecond precision.

 Custom types

1 message SingularSearchResponse {

2  Geolocation geolocation = 1;

3}

You can use your custom-defined type as a field in another message definition.

 Lists

1 message SearchResponse {

2  repeated Geolocation geolocations = 1;

3}

You can define lists by using repeated keyword.

    OneOf  

It can happen that in a message there will always be only one field set. In this case,  TelemetryUpdate will contain either geolocation, mileage, or fuel level information.

This can be achieved by using  oneof . Setting value to one of the fields will clear all other fields defined in     oneof    .

1 message TelemetryUpdate {

2  string vin = 1;

3  oneof update {

4    Geolocation geolocation = 2;

5    Mileage mileage =3;

6    FuelLevel fuelLevel = 4;

7  }

8}

9

10 message Geolocation {

11  ...

12}

13

14 message Mileage {

15  ...

16}

17

18 message FuelLevel {

19  ...

20}

Keep in mind backward-compatibility when removing fields. If you receive a message with     oneof   that has been removed from  .proto definition, it will not set any of the values. This behavior is the same as not setting any value in the first place.

You can perform different actions based on which value is set using the     getUpdateCase()   method.

1 public Optional<Object> getTelemetry(TelemetryUpdate telemetryUpdate) {

2        Optional<Object> telemetry = Optional.empty();

3        switch (telemetryUpdate.getUpdateCase()) {

4            case MILEAGE -> telemetry = Optional.of(telemetryUpdate.getMileage());

5            case FUELLEVEL -> telemetry = Optional.of(telemetryUpdate.getFuelLevel());

6            case GEOLOCATION -> telemetry = Optional.of(telemetryUpdate.getGeolocation());

7            case UPDATE_NOT_SET -> telemetry = Optional.empty();

8        }

9        return telemetry;

10    }

Default values

In  proto3 format fields will always have a value. Thanks to this  proto3 can have a smaller size because fields with default values are omitted from payload. However this causes one issue - for scalar message fields, there is no way of telling if a field was explicitly set to the default value or not set at all.

In our example, speed is an optional field -  some modules in a car might send speed data , and some might not. If we do not set speed, then the geolocation object will have speed with the default value set to 0. This is not the same as not having speed set on messages.

In order to deal with default values you can use official wrapper types  protobuf/wrappers.proto at main · protocolbuffers/protobuf . They allow distinguishing between absence and default. Instead of having a simple type, we use Int32Value, which is a wrapper for the int32 scalar type.

1 import "google/protobuf/wrappers.proto";

2

3 message Geolocation {

4  google.protobuf.Int32Value speed = 3;

5}

If we do not provide speed, it will be set to  nil .

Configure with Gradle

Once you’ve defined your messages, you can use  protoc , a protocol buffer compiler, to generate classes in a chosen language. The generated class can then be used to build and retrieve messages.

In order to compile into Java code, we need to add dependency and plugin in  build.gradle

1 plugins {

2    id 'com.google.protobuf' version '0.8.18'

3}

4

5 dependencies {

6    implementation 'com.google.protobuf:protobuf-java-util:3.17.2'

7}

and setup the compiler. For Mac users an osx specific version has to be used.

1 protobuf {

2    protoc {

3        if (osdetector.os == "osx") {

4            artifact = "com.google.protobuf:protoc:${protobuf_version}:osx-x86_64"

5        } else {

6            artifact = "com.google.protobuf:protoc:${protobuf_version}"

7        }

8    }

9}

Code will be generated using  generateProto task.

The code will be located in  build/generated/source/proto/main/java in a package as specified in  .proto file.

We also need to tell gradle where the generated code is located

1 sourceSets {

2    main {

3        java {

4            srcDirs 'build/generated/source/proto/main/grpc'

5            srcDirs 'build/generated/source/proto/main/java'

6        }

7    }

8}

The generated class contains all the necessary methods for building the message as well as retrieving field values.

1 Geolocation geolocation = Geolocation.newBuilder()

2            .setCoordinates(LatLng.newBuilder().setLatitude(1.2).setLongitude(1.2).build())

3            .setVin("1G2NF12FX2C129610")

4            .setOccurredOn(Timestamp.newBuilder().setSeconds(12349023).build())

5            .build();

6

7 LatLng coordinates = geolocation.getCoordinates();

8 String vin = geolocation.getVin();

Protocol Buffers - summary

As shown protocol buffers are easily configured. The mechanism is language agnostic, and it’s easy to share the same  .proto definition across different microservices.

Protobuf is easily paired with gRPC, where methods can be defined in  .proto files and generated with gradle.

There is official documentation available  Protocol Buffers  |  Google Developers and guides  Language Guide (proto3)  |  Protocol Buffers  |  Google Developers .

 Technology that allows vehicles to communicate wirelessly with other vehicles and road infrastructure is the go-to solution of the future. Regrettably, for the time being, the business justification for V2X roll-out by most OEMs remains beyond reach. What are the prospects for the coming years and what can be done to bring the vision of mass V2X implementation closer?

The role of V2X in supporting ADAS and AV

To begin with the basics, let's explore the dynamics of change today when it comes to automation in  the automotive industry .

The relationship between ADAS (i.e., the systems that currently prevail) and V2X (new type systems) is best captured in the chart below. It shows that the higher the SAE automation levels are, the more the role of V2X technology is emphasized.

Levels 0 to 2 represent the dominance of old-style, sensor-based security systems. Higher levels of automation are already more oriented toward extensive collaboration:

•  Between vehicles,
•  Between vehicles and infrastructure.

Advanced Driver Assistance Systems (ADAS) for the past 20 years have relied mainly on elements such as onboard sensors (cameras, radars, ultrasound). Low-level automation worked for a while, but it has its shortcomings. Primarily it is about its maximum range, which is only up to 200 meters. The other thing is its low performance in contact with obstacles, such as blind bends and densely parked vehicles.

Meanwhile, sensing technologies have developed so widely that today it is possible  to fully collaborate in such configurations as:

•  V2V – Vehicle-to-Vehicle.
•  V2D – Vehicle-to-device.
•  V2P – Vehicle-to-pedestrian.
•  V2H – Vehicle-to-home.
•  V2G – Vehicle-to-grid.
•  V2I – Vehicle-to-Infrastructure.

The most optimistic scenario assumes, among other things, that most  vehicles will be able to connect with each other on highways. And this will markedly increase road safety.

Does this mean that as automotive development continues, V2X will replace existing ADAS solutions? Not necessarily. Yet, it is possible that V2X will greatly expand the applications of current and future driver assistance systems. Thus, it will facilitate reaching  greater levels of vehicle autonomy.

V2X: benefits and unique selling points

V2X technology, which enables vehicle-to-vehicle and vehicle-to-infrastructure information exchange, is considered a resource worth having and developing in any automotive company. This is chiefly due to the numerous benefits that can be achieved in terms of traffic efficiency and safety. Here are just some of them.

•     Addressing the LoS       (Lack       of Sight)    problem which involves the non-visibility of another object. V2X can even detect elements that are invisible or undetectable by traditional sensors. This includes "blind spots" in the side mirror or objects behind a sharp bend.

•     Early warning.    Drivers of connected vehicles learn in a timely manner about dangers on multi-lane roads, especially high-speed roads. This allows them to react to the problem at an early stage. It is advisable to know that a vehicle's ABS system is activated within 1 mile of another vehicle equipped with V2X.

•     Reducing congestion and streamlining traffic.    With the technology described here, such modern fleet management methods as platooning can be successfully applied (see the following paragraphs for more details on this thread).

•  Driving assistance     even in adverse weather conditions.    Fog interferes with " standard" sensors, such as cameras. Meanwhile, the V2X also performs well during limited visibility.

•     Efficient alerting.    Approaching emergency vehicles can signal their presence from a great distance. Drivers are therefore able to quickly form an emergency lane on the highway.

•  Attaining higher levels of     vehicle autonomy.    Partially autonomous vehicles perform well in a wide range of scenarios on the road (including merging scenarios).

Are there any limitations when implementing V2X?

One of the obstacles facing OEMs at this point is insufficient demand. Although the technology is up and running and there are already many use cases around the world, consumers are reluctant to pay extra for it. This is happening for a good reason.

For example, most customers don't understand why they should pay extra for safety-related functionalities. These are already regulated by law anyway, and besides, they are guaranteed as part of ADAS (and these systems are already included in the basic vehicle price). Let's also bear in mind that a high level of data penetration in a car is not always possible. Most cars are still not high-tech enough. Many features would therefore simply be unavailable. So - why should we incur the cost of it anyway?

Beyond that, there is a long delay between the availability of the technology and the existence of a sufficient number of cars equipped with it. Meanwhile, to talk about V2X on a large scale, these two factors must exist in parallel.

Also, the road infrastructure is not necessarily designed to handle V2X. City authorities still have to focus on "putting out the current fires," so technological development sometimes takes a back seat. Besides, not all of the city's road investment is being carried out at the same time due to limited funds.

Of course, in the long run, safer roads and less congestion are the goal worth achieving, but things can't be done all at once. Examples from specific regions of the world, described in the following paragraphs, in fact, illustrate this point well.

DSRC vs. C-V2X

In the framework of V2X, there are two competing technology solutions:

DSRC - Dedicated Short-Range Communication

A form of wireless communication technology defined by the 802.11p standard. It is essentially an amendment to the IEEE 802.11 (WLAN) standard that defines changes and enhancements in order to effectively support Intelligent Transport Systems (ITS).

C-V2X - Cellular – V2X

A form of a wireless communication solution using mobile network technology. C-V2X has two modes of operation: PC5 (Direct communication) and Uu (Indirect method of communication using a cellular network).

Implementation of systems in specified regions of the world

After decades of development of the aforementioned technology, it is slowly becoming apparent that DSRC is giving way to the popularity of C-V2X. Although the former system still dominates in Europe and the US, this will certainly not last forever. According to experts, before long US and European OEMs will prefer C-V2X in their vehicles exclusively.  For the time being, however, both solutions are operating equally in these markets.

This is quite different from China, where the use of Cellular V2X has been embraced without question. For what it's worth, the issue is a bit more complicated in another Asian region, namely Japan, where DSRC-based ETC (electronic toll collection) has been under development for many years. In the Land of the Cherry Blossom, there is uncertainty about which way to eventually head. Cautious predictions, however, point to a slow transition to C-V2X.

Fundamentals for developing V2X

One thing to realize with V2X is that  the benefits are spread across all traffic users. For this to happen, however, some key driving forces are needed for the introduction and market adoption of this technology. These are:

1.     Platooning.  
2.     Fuel efficiency.  
3.     Smart cities.  
4.     Driver and pedestrian safety.  

•     Platooning  

Trucks moving in a single formation is an environmentally friendly and commercially viable solution. But what does it have in common with V2X? Quite a lot, because for platooning to take place, advanced communications technology is a must.

V2X allows trucks in a platoon  to coordinate braking and acceleration among each other. It also makes it possible  to perform many complex maneuvers .

    Main beneficiaries  

Carriers, fleet operators and the entire logistics industry, in general, would benefit enormously from V2X technology. This would not only optimize the transportation costs themselves but also fit in with increasingly stringent emissions standards.

•     Fuel efficiency  

Governments around the world strive to reduce their environmental impact by cutting emissions. In Europe, for instance, the EC Strategy on Sustainable and Smart Mobility is being prepared, outlining plans to reduce them by up to 90 (!) percent by 2050. To achieve this goal, policymakers are looking for technologies that help comply with the aforementioned limits. V2X shows huge potential in this regard.

Example? Solutions such as  GLOSA (green light speed optimization) minimize the need for a car to come to a complete stop just before traffic lights and then restart the engine or accelerate. Consequently, fuel consumption and harmful gas emissions are reduced.

    Main beneficiaries  

Environmental policymakers and regulators are (and will be) under mounting pressure related to emissions. V2X can play a key role in this puzzle, and it is up to policymakers to adopt and implement this technology.

The advantages of the aforementioned technology, however, can also be enjoyed by OEMs. Since V2X reduces fuel consumption, the driver spends less on a monthly basis. Such information can be quoted in marketing communications.

•     Smart cities  

The idea of a smart city is based on interconnected technologies and systems for collecting and using data. So it is quite natural that for functioning, smart cities need V2K solutions.

They enable communication between vehicles and buildings, signals, pedestrians, and other road users. All information is transmitted in real-time, so you gain greater awareness of your surroundings and current needs. More broadly, such intelligent transportation and road infrastructure management systems help reduce congestion. Noise levels, pollution in densely populated areas, and the likelihood of collisions are also curbed.

Automated urban logistics is the future of urbanization - without any doubt.

    Main beneficiaries  

Connected through VCX, a smart urban area can offer many benefits not only for overall security, but also for local commerce and the quality of life for its residents.

City authorities can plan individual processes more efficiently, resulting in real savings. In a potential scenario, city-funded traffic operators are immediately notified of incidents via V2X and smart cameras. By doing so, they warn other road users of the danger or make an instant decision to set up a detour. If necessary, they prioritize emergency vehicles.

Urban businesses are also enjoying the perks of a V2X-equipped smart city. That's because they benefit from shorter times for transporting goods from the place of manufacture to the point of trade. This is due to less congestion on the roads, intelligent route planning, and fully automated city logistics.

•     Driver and pedestrian safety  

Traffic collisions, injuries, and deaths not only incur unit costs but also seriously drain the public budget.

The solution to these problems may lie exactly in V2X technology, which makes it possible to identify more hazards on the road than ever before. Drivers can react more quickly to dangerous maneuvers by other road users and make early decisions that could potentially affect someone's health or life.

    Main beneficiaries  

Consumer purchasing power and public opinion certainly have a bearing on the success of V2X deployment. If road users understand that such solutions actually contribute to safety, they will be eager to push them.

Local politicians will also benefit from the achievements of new connected vehicle technologies. They, in fact, often base their election campaigns on claims related to reducing road accidents in their regions. And V2X is helping to fulfill those promises.

Who will implement first - cities or OEMs?

An important question to be answered is who will ultimately be responsible for the introduction and development of V2X. And who will begin to do it on a large scale.  The answer is not straightforward.

From the very outset, cities are faced with the difficult task of making significant infrastructure investments. For this, funds have to be obtained at some point, especially since once implemented solutions still have to be sustained. Certainly, though,  the benefits associated with V2X are well worth the funds expended on this technology.

On the other hand, we have OEMs that need a trigger to push their products forward. This must be fostered by the right market environment (a sufficient number of vehicles with V2X capabilities) and the commitment of the authorities responsible for maintaining public infrastructure. At this point, there are also constraints related to consumer reluctance, e.g. in the face of excessively high vehicle data penetration rates.

So, it all boils down to goodwill, openness to change, and the fact that certain technologies need to mature on the market.

 The automotive industry is changing right before our very eyes. Today, services based on the CASE model are looming on the horizon. They are capturing an increasing market share and gaining more and more each year in total dollar value. What's in store for the automotive sector and how automotive enterprises can seize these opportunities?

New mobility services are emerging rapidly

By 2030, over 30 percent of the projected increase in vehicle sales due to urbanization and macroeconomic growth will be unlikely to happen owing to the shared mobility expansion.

In China, the European Union, and the United States, which are countries supporting  shared mobility solutions , the mobility market  could reach 28 percent annual growth from 2015 to 2030 . Of course- this would be the most optimistic scenario. FutureBridge specialists expect the shared mobility market to grow significantly over the next five to seven years at a CAGR of 16 percent from 2018, reaching  180 billion dollars by 2025 . How can the growing demand for new mobility services be explained?

On the one hand,  the automotive industry deals with  changing consumer preferences . One travels by car covering shorter distances, but much more frequently. And it doesn’t have to be by car at all, as new means of transportation are becoming more accessible.

On the other hand, soaring car prices (though cars lose their value a few months after the purchase) prompt us to search for other, cheaper alternatives that provide optimal driving comfort anyway.

How will companies relying on the traditional car ownership model respond to this trend? They will provide new services such as substitution models, in which, for a once-off monthly payment, you can have a new car with insurance, maintenance, roadside assistance, etc.  Subscriptions will soon account for about 15% of new car sales and should have risen to 25% by 2025. In this context, new mobility in the form of rental and ride-sharing services, which are also part of the transformation on the roads, also becomes significant.

The third thing is growing technology,  based on the CASE model(Connectivity, Autonomous driving, Shared mobility, Electrification,) that empowers the development of new mobility services on an unprecedented scale. According to Microsoft experts, by 2030 virtually all new cars will have been connected devices, functioning as data centers on wheels.

6 leading new mobility services

Carsharing

A short-term car rental model that allows users to choose a vehicle and pick-up/drop-off location. Users can determine vehicles and flexible rent times. Operators gain high ROI with high utilization and minimal staffing.

 Examples: citybee, E-VAI, fetch

Ride-hailing

A form of cab rental in which the drivers are usually contractors using their private vehicles rather than direct employees. The user has immediate availability and payment is handled through the operator. The benefits are also the ability to track and monitor journeys. For operators instead, traditional fleet costs must be handled by the drivers. It’s an easily scalable service.

 Examples: Uber, Lyft, Bolt, marcel, OLA

P2P Sharing

this service allows vehicle owners to rent their vehicles when they are not currently in use. BMW-run ReachNow is piloting a version of this type of service, which allows Mini owners to offer their currently unused vehicles for rent. The benefits for users are the lower costs than traditional vehicle rental. Meanwhile, the operator has no fleet to manage and gets access to an easily scalable model of business.

 Examples: HoppyGo, SnappCar

Carpooling

Allows users to join an already scheduled trip. The operating company acts as an "intermediary" through which rides can be announced and joined. Carpooling can apply both to people taking a trip alone and to those who want to share rides to reduce the total cost of the trip for a single passenger. It’s a cheap and environmentally friendly service. What is more, the operator has a higher margin per ride and no fleet to manage.

 Examples: BlaBlaCar, GoMore, liftshare

Car rental

The evolution of the traditional car rental by the day, allowing users to rent cars for different periods without the traditional hassle associated with this type of service. From the user's point of view, such new services enable an easier and quicker process of vehicle rental. Also, it’s possible to choose a vehicle before finalizing the rental. In turn, the operator has less staffing than a traditional rental and can utilize already existing fleets.

 Examples: Audi Silvercar, Hertz, Sixt, PORSCHE DRIVE, UBEEQO

Multimodal

An integrator of public transport mobility services, as well as other modes of transportation, such as public transportation, rail networks, and even cabs. The goal of such services is to get people from their starting point to their destination in the fastest, cheapest, or most efficient way, depending on individual needs. In this model, the operator gets access to additional potential users and has relatively low costs of deployment due to a lack of physical assets.

 Examples: FREE2MOVE, whim, Google Maps

Which new mobility services are growing the fastest?

Of the 55 providers of the aforementioned new mobility services operating in European countries, the most popular are those in the area of  carsharing  (51%) . The second most popular are  car rental services  (20%) , followed by  P2P sharing  (13%) .

In terms of ownership, most new mobility services were OEM owned (over 36%), although many of them were independent (over 38%). Also included were OEM invested services (31%).

Technologies and functionalities fueling the development of new mobility services

Mobility services are based on advanced software that uses, at least, the Internet of Things, to transfer data from the vehicle to the cloud. Then the individual information is available on the user's mobile application.

For services based on unmanned vehicle rental, modern security features have been considered when it comes to opening and closing the car.

With a view to minimizing possible problems, the developers of digital new mobility services are also introducing a  fault reporting option.

 Below is a selection of the most common functionalities and technologies in detail for each new mobility service in Europe.

All of these and other options provide guidance and a certain pattern of behavior for future developing OEMs.

Key factors crucial for the development of new mobility services

CASE trends provide new opportunities for the vehicles of the future. However, the interrelationships between software, in-car sensors, and electronic systems require a  huge amount of resources , especially when we are talking about reliable operations that translate into a competitive advantage of new mobility services and popularity among potential users.

 Therefore, if you want to develop in this area, consider at least these few factors.

1.     Cybersecurity    . In addition to creating huge amounts of code, what also matters is that your user data tracking processes comply with the standards and regulations that apply in your geographic region.

2.  Careful listening to user needs. In order to compete with technology start-ups, OEMs should focus on innovative digital solutions oriented towards     actual consumer expectations    . What matters is flexibility, when it comes to the portfolio of functionalities.

3.  Certainly,     emotion    is a factor that must be taken into account. Solution providers should care about providing unique experiences and sensations that will make the user eager to re-use a particular service, and in the process, spread it to their community.

4.     Flexibility and scalability    . You need to be prepared not only to meet the changing expectations of customers who come in with feedback but also to expand functionality to include those that competitors already have (or to offer completely innovative solutions).

5.     Being ready to expand the offering    . For example, with new types of vehicles: not only internal combustion but also hybrid, electric; not only cars but also city scooters, etc.

If you want to deal with the challenges that come with developing new mobility services and are considering the above and other growth factors, contact Grape Up. We can help you expand your business in terms of features and values appreciated by today's conscious consumers.

Do you consider unit testing as not enough solution for keeping the application's reliability and stability? Are you afraid that somehow or somewhere there is a potential bug hiding in the assumption that unit tests should cover all cases? And also is mocking Kafka not enough for project requirements? If even one answer is ‘yes’, then welcome to a nice and easy guide on how to set up Integration Tests for Kafka using TestContainers and Embedded Kafka for Spring!

What is TestContainers?

TestContainers is an open-source Java library specialized in providing all needed solutions for the integration and testing of external sources. It means that we are able to mimic an actual database, web server, or even an event bus environment and treat that as a reliable place to test app functionality. All these fancy features are hooked into docker images, defined as containers. Do we need to test the database layer with actual MongoDB? No worries, we have a test container for that. We can not also forget about UI tests - Selenium Container will do anything that we actually need.
In our case, we will focus on Kafka Testcontainer.

What is Embedded Kafka?

As the name suggests, we are going to deal with an in-memory Kafka instance, ready to be used as a normal broker with full functionality. It allows us to work with producers and consumers, as usual, making our integration tests lightweight.

Before we start

The concept for our test is simple - I would like to test Kafka consumer and producer using two different approaches and check how we can utilize them in actual cases.

Kafka Messages are serialized using Avro schemas.

Embedded Kafka - Producer Test

The concept is easy - let's create a simple project with the controller, which invokes a service method to push a Kafka Avro serialized message.

Dependencies:

dependencies {

implementation "org.apache.avro:avro:1.10.1"

implementation("io.confluent:kafka-avro-serializer:6.1.0")

implementation 'org.springframework.boot:spring-boot-starter-validation'

implementation 'org.springframework.kafka:spring-kafka'

implementation('org.springframework.cloud:spring-cloud-stream:3.1.1')

implementation('org.springframework.cloud:spring-cloud-stream-binder-kafka:3.1.1')



implementation('org.springframework.boot:spring-boot-starter-web:2.4.3')

implementation 'org.projectlombok:lombok:1.18.16'



compileOnly 'org.projectlombok:lombok'

annotationProcessor 'org.projectlombok:lombok'

testImplementation('org.springframework.cloud:spring-cloud-stream-test-support:3.1.1')

testImplementation 'org.springframework.boot:spring-boot-starter-test'

testImplementation 'org.springframework.kafka:spring-kafka-test'

}

Also worth mentioning fantastic plugin for Avro. Here plugins section:

plugins {

id 'org.springframework.boot' version '2.6.8'

id 'io.spring.dependency-management' version '1.0.11.RELEASE'

id 'java'

id "com.github.davidmc24.gradle.plugin.avro" version "1.3.0"

}

Avro Plugin supports schema auto-generating. This is a must-have.

Link to plugin: https://github.com/davidmc24/gradle-avro-plugin

Now let's define the Avro schema:

{

"namespace": "com.grapeup.myawesome.myawesomeproducer",

"type": "record",

"name": "RegisterRequest",

"fields": [

{"name": "id", "type": "long"},

{"name": "address", "type": "string", "avro.java.string": "String"

}



]

}

Our ProducerService will be focused only on sending messages to Kafka using a template, nothing exciting about that part. Main functionality can be done just using this line:

ListenableFuture<SendResult<String, RegisterRequest>> future = this.kafkaTemplate.send("register-request", kafkaMessage);

We can’t forget about test properties:

spring:

main:

allow-bean-definition-overriding: true

kafka:

consumer:

group-id: group_id

auto-offset-reset: earliest

key-deserializer: org.apache.kafka.common.serialization.StringDeserializer

value-deserializer: com.grapeup.myawesome.myawesomeconsumer.common.CustomKafkaAvroDeserializer

producer:

auto.register.schemas: true

key-serializer: org.apache.kafka.common.serialization.StringSerializer

value-serializer: com.grapeup.myawesome.myawesomeconsumer.common.CustomKafkaAvroSerializer

properties:

specific.avro.reader: true

As we see in the mentioned test properties, we declare a custom deserializer/serializer for KafkaMessages. It is highly recommended to use Kafka with Avro - don't let JSONs maintain object structure, let's use civilized mapper and object definition like Avro.

Serializer:

public class CustomKafkaAvroSerializer extends KafkaAvroSerializer {

public CustomKafkaAvroSerializer() {

super();

super.schemaRegistry = new MockSchemaRegistryClient();

}



public CustomKafkaAvroSerializer(SchemaRegistryClient client) {

super(new MockSchemaRegistryClient());

}



public CustomKafkaAvroSerializer(SchemaRegistryClient client, Map<String, ?> props) {

super(new MockSchemaRegistryClient(), props);

}

}

Deserializer:

public class CustomKafkaAvroSerializer extends KafkaAvroSerializer {

public CustomKafkaAvroSerializer() {

super();

super.schemaRegistry = new MockSchemaRegistryClient();

}



public CustomKafkaAvroSerializer(SchemaRegistryClient client) {

super(new MockSchemaRegistryClient());

}



public CustomKafkaAvroSerializer(SchemaRegistryClient client, Map<String, ?> props) {

super(new MockSchemaRegistryClient(), props);

}

}

And we have everything to start writing our test.

@ExtendWith(SpringExtension.class)

@SpringBootTest

@AutoConfigureMockMvc

@TestInstance(TestInstance.Lifecycle.PER_CLASS)

@ActiveProfiles("test")

@EmbeddedKafka(partitions = 1, topics = {"register-request"})

class ProducerControllerTest {

All we need to do is add @EmbeddedKafka annotation with listed topics and partitions. Application Context will boot Kafka Broker with provided configuration just like that. Keep in mind that @TestInstance should be used with special consideration. Lifecycle.PER_CLASS will avoid creating the same objects/context for each test method. Worth checking if tests are too time-consuming.

Consumer<String, RegisterRequest> consumerServiceTest;@BeforeEach

void setUp() {

DefaultKafkaConsumerFactory<String, RegisterRequest> consumer = new DefaultKafkaConsumerFactory<>(kafkaProperties.buildConsumerProperties();



consumerServiceTest = consumer.createConsumer();

consumerServiceTest.subscribe(Collections.singletonList(TOPIC_NAME));

}

Here we can declare the test consumer, based on the Avro schema return type. All Kafka properties are already provided in the .yml file. That consumer will be used as a check if the producer actually pushed a message.

Here is the actual test method:

@Test

void whenValidInput_therReturns200() throws Exception {

RegisterRequestDto request = RegisterRequestDto.builder()

.id(12)

.address("tempAddress")

.build();



mockMvc.perform(

post("/register-request")

.contentType("application/json")

.content(objectMapper.writeValueAsBytes(request)))

.andExpect(status().isOk());



ConsumerRecord<String, RegisterRequest> consumedRegisterRequest = KafkaTestUtils.getSingleRecord(consumerServiceTest, TOPIC_NAME);



RegisterRequest valueReceived = consumedRegisterRequest.value();



assertEquals(12, valueReceived.getId());

assertEquals("tempAddress", valueReceived.getAddress());

}

First of all, we use MockMvc to perform an action on our endpoint. That endpoint uses ProducerService to push messages to Kafka. KafkaConsumer is used to verify if the producer worked as expected. And that’s it - we have a fully working test with embedded Kafka.

Test Containers - Consumer Test

TestContainers are nothing else like independent docker images ready for being dockerized. The following test scenario will be enhanced by a MongoDB image. Why not keep our data in the database right after anything happened in Kafka flow?

Dependencies are not much different than in the previous example. The following steps are needed for test containers:

testImplementation 'org.testcontainers:junit-jupiter'

testImplementation 'org.testcontainers:kafka'

testImplementation 'org.testcontainers:mongodb'



ext {

set('testcontainersVersion', "1.17.1")

}



dependencyManagement {

imports {

mavenBom "org.testcontainers:testcontainers-bom:${testcontainersVersion}"

}

}

Let's focus now on the Consumer part. The test case will be simple - one consumer service will be responsible for getting the Kafka message and storing the parsed payload in the MongoDB collection. All that we need to know about KafkaListeners, for now, is that annotation:

@KafkaListener(topics = "register-request")

By the functionality of the annotation processor, KafkaListenerContainerFactory will be responsible to create a listener on our method. From this moment our method will react to any upcoming Kafka message with the mentioned topic.

Avro serializer and deserializer configs are the same as in the previous test.

Regarding TestContainer, we should start with the following annotations:

@SpringBootTest

@ActiveProfiles("test")

@Testcontainers

public class AbstractIntegrationTest {

During startup, all configured TestContainers modules will be activated. It means that we will get access to the full operating environment of the selected source. As example:

@Autowired

private KafkaListenerEndpointRegistry kafkaListenerEndpointRegistry;



@Container

public static KafkaContainer kafkaContainer = new KafkaContainer(DockerImageName.parse("confluentinc/cp-kafka:6.2.1"));



@Container

static MongoDBContainer mongoDBContainer = new MongoDBContainer("mongo:4.4.2").withExposedPorts(27017);

As a result of booting the test, we can expect two docker containers to start with the provided configuration.

What is really important for the mongo container - it gives us full access to the database using just a simple connection uri. With such a feature, we are able to take a look what is the current state in our collections, even during debug mode and prepared breakpoints.
Take a look also at the Ryuk container - it works like overwatch and checks if our containers have started correctly.

And here is the last part of the configuration:

@DynamicPropertySource

static void dataSourceProperties(DynamicPropertyRegistry registry) {

registry.add("spring.kafka.bootstrap-servers", kafkaContainer::getBootstrapServers);

registry.add("spring.kafka.consumer.bootstrap-servers", kafkaContainer::getBootstrapServers);

registry.add("spring.kafka.producer.bootstrap-servers", kafkaContainer::getBootstrapServers);

registry.add("spring.data.mongodb.uri", mongoDBContainer::getReplicaSetUrl);

}



static {

kafkaContainer.start();

mongoDBContainer.start();



mongoDBContainer.waitingFor(Wait.forListeningPort()

.withStartupTimeout(Duration.ofSeconds(180L)));

}



@BeforeTestClass

public void beforeTest() {



kafkaListenerEndpointRegistry.getListenerContainers().forEach(

messageListenerContainer -> {

ContainerTestUtils

.waitForAssignment(messageListenerContainer, 1);



}

);

}



@AfterAll

static void tearDown() {

kafkaContainer.stop();

mongoDBContainer.stop();

}

DynamicPropertySource gives us the option to set all needed environment variables during the test lifecycle. Strongly needed for any config purposes for TestContainers. Also, beforeTestClass kafkaListenerEndpointRegistry waits for each listener to get expected partitions during container startup.

And the last part of the Kafka test containers journey - the main body of the test:

@Test

public void containerStartsAndPublicPortIsAvailable() throws Exception {

writeToTopic("register-request", RegisterRequest.newBuilder().setId(123).setAddress("dummyAddress").build());



//Wait for KafkaListener

TimeUnit.SECONDS.sleep(5);

Assertions.assertEquals(1, taxiRepository.findAll().size());



}



private KafkaProducer<String, RegisterRequest> createProducer() {

return new KafkaProducer<>(kafkaProperties.buildProducerProperties());

}



private void writeToTopic(String topicName, RegisterRequest... registerRequests) {



try (KafkaProducer<String, RegisterRequest> producer = createProducer()) {

Arrays.stream(registerRequests)

.forEach(registerRequest -> {

ProducerRecord<String, RegisterRequest> record = new ProducerRecord<>(topicName, registerRequest);

producer.send(record);

}

);

}

}

The custom producer is responsible for writing our message to KafkaBroker. Also, it is recommended to give some time for consumers to handle messages properly. As we see, the message was not just consumed by the listener, but also stored in the MongoDB collection.

Conclusions

As we can see, current solutions for integration tests are quite easy to implement and maintain in projects. There is no point in keeping just unit tests and counting on all lines covered as a sign of code/logic quality. Now the question is, should we use an Embedded solution or TestContainers? I suggest first of all focusing on the word “Embedded”. As a perfect integration test, we want to get an almost ideal copy of the production environment with all properties/features included. In-memory solutions are good, but mostly, not enough for large business projects. Definitely, the advantage of Embedded services is the easy way to implement such tests and maintain configuration, just when anything happens in memory.
TestContainers at the first sight might look like overkill, but they give us the most important feature, which is a separate environment. We don't have to even rely on existing docker images - if we want we can use custom ones. This is a huge improvement for potential test scenarios.
What about Jenkins? There is no reason to be afraid also to use TestContainers in Jenkins. I firmly recommend checking TestContainers documentation on how easily we can set up the configuration for Jenkins agents.
To sum up - if there is no blocker or any unwanted condition for using TestContainers, then don't hesitate. It is always good to keep all services managed and secured with integration test contracts.

 Android Automotive OS is getting more recognition as automotive companies are looking to provide their customers with a more tailored experience. Here we share our guide to building the first app for AAOS.

Before you start,  read our first article about AAOS and get to know our review to be aware of what to expect. Let’s try making a simple  Hello World app for android automotive. To get an IDE, go to  Android Studio Preview | Android Developers and get a canary build:

In the next step, prepare SDK, check and download the Automotive system image in SDK manager. You can get any from api32, Android 9, or Android 10, but I do not recommend the newest one as it is very laggy and crashes a lot right now. There are also Volvo and Polestar images.

For those you need to add links to SDK Update Sites:

 https://developer.volvocars.com/sdk/volvo-sys-img.xml

 https://developer.polestar.com/sdk/polestar2-sys-img.xml

Start a new project, go to  File> New Project and choose  automotive with  no activity

A nice and clean project should be created, without any classes: Go to  build.gradle and add the car app library into dependencies, refresh the project to make it get

our new dependency:

implementation "androidx.car.app:app-automotive:1.2.0-rc01"

Let's write some code, first our screen class. Name it as you want and make it extend Screen class from  android.car.app package and make it implement required methods:

public class GrapeAppScreen extends Screen {



  public GrapeAppScreen(@NonNull CarContext carContext) {

      super(carContext);

  }



  @NonNull

  @Override

  public Template onGetTemplate() {

      Row row = new Row.Builder()

.setTitle("Thats our Grape App!").build();



      return new PaneTemplate.Builder(

              new Pane.Builder()

                      .addRow(row)

                      .build()

      ).setHeaderAction(Action.APP_ICON).build();

  }

}

That should create a simple screen with our icon and title, now create another class extending  CarAppService from the same package and as well make it implement the required methods. From  createHostValidator() method return a static one that allows all hostnames for the purpose of this tutorial and return brand new session with our screen in  onCreateSession() , pass  CarContext using  Session class  getCarContext() method:

public class GrapeAppService extends CarAppService {



  public GrapeAppService() {}



  @NonNull

  @Override

  public HostValidator createHostValidator() {

      return HostValidator.ALLOW_ALL_HOSTS_VALIDATOR;

  }



  @NonNull

@Override

public Session onCreateSession() {

  return new Session() {

      @Override

      @NonNull

      public Screen onCreateScreen(@Nullable Intent intent) {

          return new GrapeAppScreen(getCarContext());

      }

  };

}



}

Next, move to  AndroidManifest and add various features inside the main manifest tag:

<uses-feature

  android:name="android.hardware.type.automotive"

  android:required="true" />

<uses-feature

  android:name="android.software.car.templates_host"

  android:required="true" />

<uses-feature

  android:name="android.hardware.wifi"

  android:required="false" />

<uses-feature

  android:name="android.hardware.screen.portrait"

  android:required="false" />

<uses-feature

  android:name="android.hardware.screen.landscape"

  android:required="false" />

Inside the Application tag add our service and activity, don’t forget  minCarApiLevel as lack of this will throw an exception on app start:

<application

  android:allowBackup="true"

  android:appCategory="audio"

  android:icon="@mipmap/ic_launcher"

  android:label="@string/app_name"

  android:roundIcon="@mipmap/ic_launcher_round"

  android:supportsRtl="true"

  android:theme="@style/Theme.GrapeApplication">



<meta-data android:name="androidx.car.app.minCarApiLevel"

  android:value="1"

/>



<service

      android:name="com.grapeup.grapeapplication.GrapeAppService"

      android:exported="true">

<intent-filter>

<action android:name="androidx.car.app.CarAppService" />

</intent-filter>

</service>



<activity

      android:name="androidx.car.app.activity.CarAppActivity"

      android:exported="true"

      android:label="GrapeApp Starter"

      android:launchMode="singleTask"

      android:theme="@android:style/Theme.DeviceDefault.NoActionBar">



<intent-filter>

<action android:name="android.intent.action.MAIN" />

<category android:name="android.intent.category.LAUNCHER" />

</intent-filter>

<meta-data

          android:name="distractionOptimized"

          android:value="true" />

</activity>

</application>

Now we can upload our application to the device, verify that you have an automotive emulator created, use automotive configuration, and hit run. The app is run in Google Automotive App Host, so if it is your first application on this device, it may require you to get to the play store and get it.

That’s how it looks:

The last thing, we’ll add a navigation button that will pop a  Toast . Modify  onGetTemplate() in  Screen class, add  Action and  ActionStrip :

Action action = new Action.Builder()

      .setOnClickListener(

              () -> CarToast.makeText(getCarContext(), "Hello!", CarToast.LENGTH_SHORT).show())

      .setTitle("Say hi!")

      .build();



ActionStrip actionStrip = new

Add it to  PaneTemplate:

return new PaneTemplate.Builder(

      new Pane.Builder()

              .addRow(row)

              .build()

)       .setActionStrip(actionStrip)

      .setHeaderAction(Action.APP_ICON)

      .build();

That’s our HelloWorld app:

Now you have the HelloWorld example app up and running using Car App Library. It takes care of displaying and arranging everything on the screen for us. The only responsibility is to add screens and actions we would like to have(and a bit of configuration). Check the Car app library to explore more of what can be done with it, play around with creating your app, and definitely check our blog soon for more AAOS app creation content.

 By 2023, there will be over     350 million connected cars on the road    . What can the insurance industry do about it?     It turns out that quite a bit, as automotive companies, introducing the latest technological advances, are enabling new ways to mix driver behavior. This is of great importance in the context of creating offers, but not only. At stake is to maintain the position and competitiveness in the field of motor insurance.  

The automotive and car insurance industries are changing

The automotive market is already experiencing changes driven by innovative technologies. More often than not, these are based on the  software-defined vehicle (SDV) trend.

If the vehicle is equipped with embedded connectivity, it is able to provide very detailed vehicle and driver behavior data, such as:

● sudden acceleration or braking,
● taking sharp turns,
● peak activity times (nighttime drivers are more vulnerable),
● average speed and acceleration,
● performing dangerous maneuvers.

BBI & UBI and ADAS

Behavior-based (pay-how-you-drive) and usage-based insurance – UBI – (pay-as-you-drive) are the future of  car insurance programs . Meanwhile, as vehicles become smarter, more connected, and automated, insurers evaluate not only the driver's behavior but also the car s/he is driving.  This evaluation takes into account, among other things, the amount of advanced driver assistance systems (ADAS) that affect the safety of the vehicle's occupants.

Autonomous vehicles

And  Deloitte analysts note that self-driving (AV) cars, which are an interesting novelty now but will in time be a standard on par with human-driven vehicles, are also likely to force fundamental changes in insurers' product ranges, as in the risk assessment, pricing, and business models.

Connected cars

Change is already happening, and it will become even more pronounced in the years ahead. IoT Analytics predicts that by 2025, the total  number of IoT devices worldwide will exceed 27 billion. Plus,  experts predict that there will be 7.2 billion active smartphones and more than 400 million connected vehicles on the road during the same period.

This all clearly shows that we are in an entirely different reality than we were just a few or a dozen years ago.  Car insurers need to understand this if they want to maintain their foothold.

Telematics technologies are an obvious step into the future of the insurance industry

Insurance companies have been offering  usage-based and behavior-based products for years based on data from either additional devices or mobile apps. This is a fast-growing product area since  the UBI market is predicted     to be worth more than $105 billion in 2027    , up 23.61% annually.

 The best position in this arena is attained by businesses that started investing in telematics technology early and now can take pride in well-developed telematics products.

We are talking about brands such as  State Farm®, Nationwide, Allstate, and Progressive. Yet at the same time, companies that deemed telematics a passing trend and therefore didn't invest in it lost a very large amount of market share. The result? Now they have to catch up and race to keep up with the competition.

TSPs understand the potential of connected vehicle data

Insuring companies are not the only ones who recognize the importance of implementing their telematics-based solutions.  Telematics services providers understand that value as well, so they invest in building out new capabilities of their products.

This is the case with  GEICO , the second-largest auto insurer in the U.S. (right after Progressive). As Ajit Jain, vice president of Insurance Operations at  Berkshire Hathaway claims :  GEICO had clearly missed the business and were late in terms of appreciating the value of telematics. They have woken up to the fact that telematics plays a big role in matching rate to risk. They have a number of initiatives, and, hopefully, they will see the light of day before, not too long, and that'll allow them to catch up with their competitors, in terms of the issue of matching rate to risk .

Telematics companies see potential in partnering with the insurance industry

Insurance companies are not the only ones who recognize the importance of implementing new data-driven technology solutions. The relationship is two-way, as telematics industry representatives, in turn,  are willing to invest in collaboration with insurers and put the customer from this market sector first.

For example, Cambridge Mobile Telematics (CMT), the world's largest telematics provider, has recently announced the expansion of its proprietary DriveWell® telematics platform to networked vehicles. Their flagship software has previously collected sensor data from millions of IoT devices, including smartphones, tags, in-car cameras, third-party devices, etc. From now on, that scope continues to expand by specifically including connected vehicles to create a unified view of driver and vehicle behavioral risk.

This synergy of all acquired data is mainly dedicated to customers in the auto insurance industry, who gain insight into what is happening on the road and behind the wheel.  As Hari Balakrishnan, CTO and founder of CMT explains :  There is a wave of innovative IoT data sources coming that will be critical to understanding driving risk and lowering crash rates. CMT fuses these disparate data sources to produce a unified view of driving .

Current UBI solutions can be flawed

Existing methods of data collection for insurers also rely on modern technologies, but these can be unreliable.  All three methods have their drawbacks: devices plugged into the On-Board Diagnostic (OBD) system, smartphone apps and tags stuck to the windshield.

The first method provides insight into the driver's precise behavior data, downloaded directly from the engine control module (ECM). Weaknesses?  The fact that OBD-II devices are limited to the data found in the ECM, for example, while those from other vehicle components remain inaccessible.

In this respect, mobile apps are certainly better, providing insurers with a simple way to launch their own  telematics-based program . . In addition, data is collected every time the user drives the vehicle. The disadvantage, however, is that the software does not connect directly to the vehicle's systems. Therefore, the data points are subject to a margin of error, and it also happens that the automatic driving recognition fails and includes in the scoring journeys as a passenger in another car, for example.

Bluetooth-based tags, which is the last solution described here, are installed on the vehicle's windshield or rear window. Like mobile apps, the tags have no direct connection to the vehicle's systems and are therefore prone to bugs.

The conclusions are obvious

 Thus, there is a lot to suggest that if an insurer is looking for truly reliable technology, it should opt to use embedded telematics, or data. This is what enables dynamic and, above all, unconditional data collection to reliably assess the risk associated with individual clients.

 The data sent by connected cars is more accurate, more detailed, and in much larger quantities compared to other solutions. And this allows  insurance companies to better understand customers and their behavior and, based on this information, offer products that are better suited to their needs, as well as more profitable.

Industry insiders don't need much convincing about the advantages of telematics and connected cars over other driver data collection solutions.  Data from cars connected to the network are instantly obtainable. Of course, you can enrich it and give it context by using information from smartphones, but in most cases, it is not even necessary.  So why invest in something unreliable, which by definition has vulnerabilities and does not meet 100 percent of your needs, when you can opt for a more comprehensive technology that offers more features right from the start.

Considerable importance of connected car data for the insurance industry

Connected car data is the subsequent step in building the ultimate telematics-based products. It is acquired without the need to install additional components. All it takes is a vehicle user's consent to use the data, and then the insurance company obtains the data directly from the OEM.

The information obtained from UBI vehicles can be used successfully and all stakeholders benefit: insurers, as they gain a better understanding of their customers and can better assess risk; OEMs, as it allows them to monetize the data; and finally consumers, who receive a better, more personalized offer this way. J.D. Power points out that 83% of policyholders who had positive claims experience renewed their policies, compared to only  10% who gave negative reviews .

In addition, such reliable data serves not only to improve the profitability of an insurance portfolio, but also to improve road safety. Insurers can offer incentives that will encourage their customers to continuously improve their driving style and increase their care for themselves and other road users.

Even now, market leaders who understand the value of investing in innovation are offering their customers the opportunity to share data from connected cars for UBI/BBI purposes. One example is the  State Farm® brand, which offers discounts based on driving behavior. The driver's on-the-road behavior ( sharp braking or no braking, rapid acceleration, swift turns) and driving mileage are automatically sent to the data manager after each trip, so be sure to enable data sharing and location services on your saved vehicle. This information is used to update your Drive Safe & Save discount each time you renew your policy.  The safer you drive, the more you can save .

Likewise, Ford Motor Company is increasingly shifting toward using driver data in UBI programs based on connected vehicles.  To that end, the automotive giant has partnered with a mobility and analytics brand. Their joint project is expected to empower drivers with more control over how much they pay for their car insurance. Drivers can voluntarily share their driving data from activated Ford vehicles with Arity's centralized telematics platform, and it will then be delivered via Arity's API. Drivesight® to insurers.  The obtained risk index can be used to price auto insurance by     any participating insurer    .

Currently, connected cars are only one option, as many insurance companies are still using, for example, mobile applications in parallel. However, we can already see that the trend of using CC data is present on the market and the number of companies offering such an option to their clients will grow. This is something to be reckoned with.

Significant benefits

For insurers, the benefits are tangible. According to Swiss Re,  with 20,000 claims handled per year, the average savings after implementing the above technologies     amounted to 10-30 USD per claim    .

Telematics also helps to curb so-called claims inflation. Increasingly advanced vehicles are equipped with complex components, which can be costly to replace. Fortunately, today's insurer has the ability to create its own strategy based on the changing cost of spare parts and damage history for major car models.  This enables them to develop new pricing that includes inflated compensation costs.

The sooner, the better

Leveraging data and analytics based on artificial intelligence is guaranteed to drive growth. Expanded sources of information  improve the customer experience and help streamline operational processes. The benefits are thus evident across the entire value chain.  We can confidently say that never before in history has technology been so intertwined with the insurance industry.

That's why all insurance companies should start working on incorporating connected car data into their programs now. The sooner they do, the better positioned they will be when such vehicles become mainstream on the road. After all,  the share of new vehicles with built-in connectivity     will reach 96% in 2030    .

That's what  Evangelos Avramakis, Head Digital Ecosystems R&D, Swiss Re Institute Research & Engagement advises insurance companies to do:  Starting small then scaling fast might be a good strategy (...) There is so much you can do with data. But you need to take a different approach, depending on whether you want to improve claims processing or create new products. Conversely, this is what Nelson Tham, eAdmin Expert Asia, P&C Business Management, thinks about implementations:  Whenever an SME thinks about digitalization, it intimidates them. But it need not be the case if we start small. They can begin by reviewing their internal processes, see how data flows, turn that into structured data, then analyze this data for more meaningful insights .

How the insurance industry should approach the subject?

Insurers should start by answering key questions like: where connected car data will deliver the most value for my business? What internal capabilities do we have and need? Do we have the required infrastructure, process and skills to leverage connected car data? What investments in technology are necessary to deliver on our goals?

Lastly, they need to consider whether they can better and faster achieve those goals by building required capabilities in-house or working with partners.

A good business and technology partner for the insurance industry is fundamental

Using  connected car data is not that straightforward. It requires know-how and the right technology background, as well as finding the right partner to collaborate with.

 A well-matched partner will help change the current operating model, by combining automotive and technology competencies and at the same time understanding the specifics of the insurance industry. Some processes simply have to be carried out in a comprehensive and holistic way.

 At GrapeUp, we help implement new approaches to an existing strategy. Operating at the intersection of automotive and insurance, we specialize in the technologies of tomorrow. Contact us if you want to boost your business performance.

 Quite a new droid around. The operating system has been in the market for a while, still missing a lot, but it is out, already implemented in cars, and coming for more. Polestar and Volvo were the first to bring Android Automotive OS to their Polestar 2 and XC40 Recharge.

Other car manufacturers like PSA, Ford, Honda, GM, and more announced that they are going to bring Android Automotive OS to their cars or just hinted about cooperating with Google Mobile Services. Part of implementations coming with Google Automotive Services(GAS): Play Store, Google Maps, Google Assistant, another part without, own app stores, assistants. What's most interesting for now is to bring your application to the store.

Building apps for the Android Automotive Operating System

Creating an android app for automotive doesn't differ that much from mobile and is similar to android auto. Starting in an android studio, setting it up for canary releases to get the emulators. The first issue is that Android Automotive OS emulation needs an Intel CPU right now and doesn't support Apple M1 or AMD. Available emulators start on Android 9(Pie), with Google and a custom one for Polestar 2, Android 10(Q) also with Volvo, skinned to look like XC40 cockpit, Android 11 and freshly released Android 12(API 32) emulators are Google only. To get your hands on custom versions for Volvo or Polestar 2,  you need to add links to  SDK update sites .

Challenges with Google Automotive Services

Lack of documentation and communication

Diving into the details of development and Android Automotive Operating System in general, the main thing you are going to spot is a problem with documentation and communication with Google, as the Android Automotive car feels like it is lacking options and solutions.

Developers and mobile groups are complaining about it, some of them trying to establish a communication channel and get Google on the other side. Google is not providing a clear roadmap for AAOS, and it is risky or at least could be expensive to develop applications right now. Some parts of the Operating System code hint at certain features, but documentation is silent about them.

Limited options to improve AAOS user experience

 Automotive applications are run in a shell (Google Automotive App Host) similar to those for Android Auto, and they do not have Activity thus UI can't be changed. Apps are automatically rendered, and all of them look similar.

There is still an option to install a regular application through ADB, but this might sound easy only for an emulator. Options for app developers to brand their applications are very limited, actually it is just an app icon at the top side of the screen and a color of progress bars, like those showing how much of a podcast or song you listened to already.

Car manufacturers and automotive OEMs have more options to reflect their brand and style of an interior. They can customize colors, typography, layouts, and more. There is still a requirement to follow design patterns for automotive, and Google is providing a whole  design system page .

Mandatory review

Applications submitted to the store are mandatory for an additional review. Reviewers have to be able to perform a full check, logins, payments, etc., so they need to be provided with all required data and accounts. That adds additional uncertainty with innovation and going beyond what is expected, as the reviewer has to agree that our app meets the requirements.

Focus on an infotainment system

Right now, the documentation states that  supported categories for Android Automotive OS apps are focused on in-vehicle infotainment experience: Media, Navigation, Point of Interest, and Video. Compared to Android Auto, it is missing Messaging category and adds Video. Requirements are in place for all apps in general or specific categories and most of those requirements follow the principle to make the app very simple and not distract the driver.

How does it work? If you don't have a payment option set on your account, it should ask you to add it on another device. You can't ask a user to agree to recurring payments or purchase multiple items at once. It is not allowed even if you are not driving, and that appears to be inconsistent with the video app category. For example, it is not allowed to work at all during driving, but can display video normally when stopped.

Play Store right now presents a handful of applications, fairly easy to count all of them, most of them being  in-vehicle infotainment systems : media(music and podcasts) and navigation apps. Nothing is stated about mixing categories, and none of the existing apps seems to cover more than one category.

Sensor data

Android Automotive Operating System being an integral part of the car, brings ideas about controlling features of a car, or at least reading them and reacting within an application accordingly. Emulation provides just a few options to simulate car state, ignition, speed, gear, parking brake, low fuel level, night mode, and environment sensors(temperature, pressure, etc.). There is an option to load a recording of sensor reads.

There are definitely more sensors that we are missing here that could have come in handy, and there is an extensive list of vehicle property ids to be read, with possible extensions from a car manufacturer and an option to subscribe for a callback informing us that property changed.

Managing car features

Coming to controlling a car's features leaves us with scarce information. The first thing that came to my mind was getting all the permissions through ADB, and it brought joy when permissions like car climate change appeared, but no service or anything is provided to control those features. Documentation reveals that there is a superuser responsible for running OEM apps that are controlling e.g. air-conditioning, but for now, there is no option for a dev to make your own app that will open a window for you.

The infotainment system should be possible to make and bring all the information you can get on a car screen(worth mentioning Android Automotive Operating System should be able to control the display behind the steering wheel, that is missed in documentation as well), but do not forget that there is no such category and possibly won't get through mandatory check.

What to look forward to in the upcoming future

After all, AAOS is here to standardize what we will see in our cars. It brings our most used applications, without plugging in the phone. We can choose our favorite navigation application and make shortcut icons for the most visited places. Our vehicle will remember where we were with our podcast and what playlist was on.

Looks like the system releases are becoming more frequent, Google is adding features that are necessary to control everything correctly from different cars. We should see it in more and more cars as this cuts costs for manufacturers and saves on developing applications. Custom skins and customizations for the screens can bring a bit of your style to your car.

Android Automotive Operating System summed up

That summary of what is going on in Automotive Android Operating System and Google Automotive Services might show there is a slight mess, both around code and documentation. That seems to be the feeling of most of the devs sharing their experiences. It is risky to develop apps without having a clear understanding of which way is the new droid going and without any board or support medium, at least to gather developers together.

That being said, it is a great time to put your app in the store and be there first. Explore what could get through the check and how far they let apps develop. We would love to get in the car at some point over a phone over an NFC spot and let it quickly adjust everything for you, with your key apps.

Do you want to start building apps for AAOS? Here is  our guide to help you create AAOS Hello World .

 64% of truck industry CEOs say the future success of their organization hinges upon the digital revolution. This should come as no surprise, as transportation as we knew it a decade or two ago is slowly fading into obscurity.

Operating standards in the industry are improving, and values such as speed, efficiency, eco-friendliness, and safety are reverberating in announcements at industry conferences and in truck industry reports.

Self-driving, fully autonomous vehicles, mainly electrically powered and  based on AI and the Internet of things , are transforming 21st-century transportation. It is well worth taking a look at examples of solutions implemented by innovators with substantial development capital.

Solutions that translate into safety and driving performance of larger vehicles

Some innovations, in particular, are shifting the industry forward. And they are literally doing so. Developments like autonomous vehicles, electric-powered trucks,  Big Data , and  cloud computing have modified the way goods and people are transported. Smart analytics allows for more efficient supply chains, but not only that.  It also enhances driving safety and the experience of traveling long distances.

High-tech trucks break down less frequently and cause fewer accidents. And self-driving technologies, which are still being developed, enable you to save time and money.

AI, Big Data, Internet of things

Better location tracking, improved ambient sensing, and  enhanced fleet management . All these benefits can be achieved by implementing IoT solutions.

Composed of devices and detectors in the vehicle and in the road infrastructure,  the network is a space for the continuous exchange of data in real-time . It provides information about the conditions on the route, but also whether the cargo is stable (tilt at the level of the pallet or package), and whether the pressure in the tires is at the right level. This facilitates the work of drivers, shippers, and management.

This solution is applied, for instance, at one of the globally leading logistics companies,  Kuehne + Nagel. The company uses IoT sensors and a cloud-based platform in its daily work. It simply works.

The use of artificial intelligence algorithms is equally important.     Advanced Big Data analytics    , coupled with AI, allows companies to make decisions based on accurate, quality data. According to Supply Chain Management World research, 64 percent of executives believe that big data and coupled technologies will empower and change the industry forever. This is because it will improve performance forecasting and goal formulation even further.

Performance indicators are measured in this way by the logistics company  Geodis. With their proprietary Neptune platform, they leverage real-time coordination of transportation activities. With one app and a few clicks, carriers and customers can manage all activities during transport.

Failure prevention

Software-based solutions in the trucking industry are eradicating a number of issues that have previously been the bane of the industry. These include breakdowns, which sometimes take a fleet's operationally significant "arsenal" out of circulation. You can find out about such incidents even before they happen.

 Drivers of the new Mercedes-Benz eActros, for example, have recently been able to make use of the intelligent Mercedes-Benz Uptime system. This service is based on more than 100 specific rules that continuously monitor processes such as charging. On top of that, they control the voltage history associated with the high-voltage battery.

All information required in terms of reliability is available to customers via a special portal in the cloud. In this way, the German manufacturer wants to keep unexpected faults to a minimum and facilitate the planning of maintenance work for the fleet.

Self-driving vehicles

Automated trucks equipped with short and long-distance radars, sensors, cameras, 3D mapping, and laser detection are poised to revolutionize the industry. They are also a solution to the problem of the driver shortage, though, as a matter of fact, we still have to wait a while for fully autonomous trucks.

However, there are many indications that there will be increased investment in such solutions. Just take a look at the proposals from tech giants in the US like  Tesla, Uber, Cruise, and Waymo.

The latter offers the original Waymo VIA solution, promising van and bus drivers an unparalleled autonomous driving experience. Waymo Driver's intelligent driving assistant, based on simulations with the most challenging driving scenarios, is capable of making accurate decisions already in the natural road environment. WD sees and detects what's happening on the road, in addition to being able to handle complex tasks of accelerating, braking, and navigating a wide turning circle.

Sustainable drive

The sustainability trend is now powering multiple industries, with the truck industry being no exception. So it should come as no surprise that a rising number of large transport vehicles are being electrified.

Tesla is investing in electric trucks, and doubly so, because in addition to making their  Semi Truck an electric vehicle, Elon Musk's brand has additionally created its own charging infrastructure - a network of superchargers under the brand, the Tesla Supercharger Network.  As a result, ST trucks are able to drive 800 km on full batteries, and an additional 600 km of range can be attained after 30 minutes of charging.

Another giant,  Volkswagen , is also following a similar approach. It is investing in electric trucks with solid-state batteries that, unlike lithium-ion batteries, provide greater safety and an improved quick-charging capability. In the long run, this is intended to lead to an increase of up to 250% in the range of kilometers covered.

The mission to reduce CO2 emissions in truck transport is also being actively promoted by  VOLTA. Their all-electric trucks are designed to reduce exhaust tailpipe emissions to 1,191,000 tonnes by 2025. A slightly smaller, but still impressive goal has been set by England's  Tevva Electric Trucks. Their vehicles are expected to reduce CO2 emissions by 10 million tons by the next decade.

Giants already know what's at stake

Companies like Tesla, Nikola Corporation, Einride, Daimler, and Volkswagen already understand the need to enter the electric vehicle market with bold proposals. Major players in the automotive market are also targeting synergistic collaborations. For instance, BMW, Daimler, Ford, and Volkswagen are teaming up to build a high-powered European charging network. Each charging point will be 350 kW and use the Combined Charging System (CCS) standard to work with most electric vehicles, including trucks.

Another major collaboration involves Volkswagen Group Research and the American company QuantumScape. The latter is conducting research on solid-state lithium metal batteries for large electric cars. This partnership is expected to enable the production of solid-state batteries on an industrial level.

Smooth energy management

Truck electrification is not all that is needed. It is also essential that electric vehicles have an adequate range and unhindered access to charging infrastructure. In addition, optimizing consumption and increasing energy efficiency is also one of the challenges.

It is with these needs in mind that Proterra has developed special  Proterra APEX  connected vehicle intelligence telematics software to assist electric fleets with real-time energy management. Electric batteries are constantly monitored and real-time alerts appear on dashboards. Fleet managers also have access to configurable reports.

Meanwhile, the Fleetboard Charge Management developed by  Mercedes offers a comprehensive view of all interactions between e-trucks and the company's charging stations. Users can see what the charging time is and monitor the current battery status. Beyond that, they can view the history of previous events. They can also adjust individual settings such as departure times and final expected battery status.

Truck Platooning

More technologically advanced trucks can be linked together.  Platooning, or interconnected lines of vehicles traveling in a single formation allows for substantial savings. Instead of multiple trucks "scattered" on the road, the idea is to have a single, predictable in many ways string of vehicles moving in a highly efficient and low-emission manner.

How is this possible? The answer is simple: telematics.  Telecommunication devices enable the seamless sending, receiving, and storing of information. Josh Switkes, a founder of Peloton, a leader in automated vehicles, explains how the system functions: We’re sending information directly from the front truck to the rear truck, information like engine torque, vehicle speed, and  brake application .

Although platooning is not yet widespread, it may soon become a permanent fixture on European roads thanks to  Ensemble . As part of this project, specialists, working with brands such as  DAF, DAIMLER, MAN, IVECO, SCANIA, and VOLVO Group, are analyzing the impact of platooning on infrastructure, road safety, and traffic flow. However, the fuel savings alone are already said to be 4.5% for the leading truck and 10% for the truck following it.

Smart sensors

Developers of automotive and truck industry technologies are focusing particularly on safety issues. These can be aided by intelligent sensors that allow a self-driving vehicle to generate alerts and take proactive action. This is how VADA works. This is  Volvo ’s active driver assistance system, already being standard on the Volvo VNR and VNL models.

The advanced collision warning system, which combines radar sensors with a camera, alerts the driver seconds before an imminent collision. If you are too slow to react, the system can implement emergency braking automatically in order to avoid a crash.

Innovative design

Changes are also taking place at the design stage of large vehicles. This is particularly emphasized by the makers of these cutting-edge models. One of the leaders in this field is  VOLTA , which advertises its ability to create "the world's safest commercial vehicles".

Their Volta Zero model provides easy and low level boarding and alighting from either side directly on the sidewalk. That's possible because the vehicle doesn't have an internal combustion engine, so the engineers were able to overhaul previously established rules.

Dynamic route mapping and smart monitoring

While GPS is nothing new, the latest software uses the technology to a more advanced degree. For instance, for so-called dynamic route mapping, i.e. selecting the shortest, most convenient route, allowing for possible congestion. Importantly, this works flexibly, adapting not only to road conditions but also, for example, to unexpected increases in loading, etc.

Volta Zero also relies on the advanced route and vehicle monitoring. Using the Sibros OTA Deep Logger, you can receive up-to-date information on individual vehicles and the entire fleet.

Shipping is not like it used to be

Apart from the passenger car market changes, a similar revolution is underway in the truck and van industry. This transformation is called for as the problem is not only a shortage of professional drivers but also reducing the cost of transportation and increasing volume. So any loss-reduction initiative is of paramount value.

As for the solutions we have mentioned in this article, they will certainly not all be widely implemented in the next few years. For example, it is difficult to expect only electric-powered autonomous trucks to be on the road as early as 2027. What can be widely rolled out now is, for example, optimization of cargo loading (by predicting when the truck will arrive), better route finding (via advanced GPS), or predictive maintenance (early repair before it generates logistics costs). It is only the second step to progress toward full electrification and autonomization.

Regardless of how the truck industry evolves over the next few or so years, it is definite that the changes will be based on the idea of a digital revolution, advanced software, and smart components.

All this is geared to enhance mobility services, bringing aspects such as driving comfort, business efficiency, and safety to a new level. This is a fact well known to the big OEM players and to the  tech and automotive companies that year after year are competing with each other in innovations.

In the modern world, we are constantly faced with the need not only to develop applications but also to provide and maintain an environment for them. Writing scalable, fault-tolerant, and responsive programs is hard, and on top of that, you’re expected to know exactly how many servers, CPUs, and how much memory your code will need to run – especially when running in the Cloud. Also, developing cloud native applications and microservice architectures make our infrastructure more and more complicated every time.

So, how not worry about underlying infrastructure while deploying applications? How do get easy-to-use and manage computing services? The answer is in serverless applications and AWS Lambda in particular.

What you will find in this article:

1.  What is Serverless and what we can use that for?
2.  Introduction to AWS Lambda
3.  Role of AWS Lambda in Serverless applications
4.  Coding and managing AWS Lambda function
5.  Some tips about working with AWS Lambda function

 What is serverless?

Serverless computing is a cloud computing execution model in which the cloud provider allocates machine resources on-demand, taking care of the servers on behalf of their customers. Despite the name, it does not involve running code without servers, because code has to be executed somewhere eventually. The name “serverless computing” is used because the business or person that owns the system does not have to purchase, rent, or provision servers or virtual machines for the back-end code to run on. But with provided infrastructure and management you can focus on only writing code that serves your customers.

Software Engineers will not have to take care of operating system (OS) access control, OS patching, provisioning, right-sizing, scaling, and availability. By building your application on a serverless platform, the platform manages these responsibilities for you.

The main advantages of AWS Serverless tools are :

•  No server management – You don’t have to provision or maintain any servers. There is no software or runtime to install or maintain.

•  Flexible scaling – You can scale your application automatically.

•  High availability – Serverless applications have built-in availability and fault tolerance.

•  No idle capacity – You don't have to pay for idle capacity.

•  Major languages are supported out of the box - AWS Serverless tools can be used to run Java, Node.js, Python, C#, Go, and even PowerShell.

•  Out of the box security support

•  Easy orchestration - applications can be built and updated quickly.

•  Easy monitoring - you can write logs in your application and then import them to Log Management Tool.

Of course, using Serverless may also bring some drawbacks:

•  Vendor lock-in - Your application is completely dependent on a third-party provider. You do not have full control of your application. Most likely, you cannot change your platform or provider without making significant changes to your application.

•  Serverless (and microservice) architectures introduce additional overhead for function/microservice calls - There are no “local” operations; you cannot assume that two communicating functions are located on the same server.

•  Debugging is more difficult - Debugging serverless functions is possible, but it's not a simple task, and it can eat up lots of time and resources.

Despite all the shortcomings, the serverless approach is constantly growing and becoming capable of more and more tasks. AWS takes care of more and more development and distribution of serverless services and applications. For example, AWS now provides not only Lambda functions(computing service), but also API Gateway(Proxy), SNS(messaging service), SQS(queue service), EventBridge(event bus service), and DynamoDB(NoSql database).

Moreover, AWS provides Serverless Framework which makes it easy to build computing applications using AWS Lambda. It scaffolds the project structure and takes care of deploying functions, so you can get started with your Lambda extremely quickly.

Also, AWS provides the specific framework to build complex serverless applications - Serverless Application Model (SAM). It is an abstraction to support and combine different types of AWS tools - Lambda, DynamoDB API Gateway, etc.

The biggest difference is that Serverless is written to deploy AWS Lambda functions to different providers. SAM on the other hand is an abstraction layer specifically for AWS using not only Lambda but also DynamoDB for storage and API Gateway for creating a serverless HTTP endpoint. Another difference is that SAM Local allows you to run some services, including Lambda functions, locally.

 AWS Lambda concept

AWS Lambda is a Function-as-a-Service(FaaS) service from Amazon Web Services. It runs your code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring, and logging.

AWS Lambda has the following conceptual elements:

•  Function - A function is a resource that you can invoke to run your code in Lambda. A function has code to process the events that you pass into the function or that other AWS services send to the function. Also, you can add a qualifier to the function to specify a version or alias.

•  Execution Environment - Lambda invokes your function in an execution environment, which provides a secure and isolated runtime environment. The execution environment manages the resources required to run your function. The execution environment also provides lifecycle support for the function's runtime. At a high level, each execution environment contains a dedicated copy of function code, Lambda layers selected for your function, the function runtime, and minimal Linux userland based on Amazon Linux.

•  Deployment Package - You deploy your Lambda function code using a deployment package. AWS Lambda currently supports either a zip archive as a deployment package or a container image that is compatible with the Open Container Initiative (OCI) specification.

•  Layer - A Lambda layer is a .zip file archive that contains libraries, a custom runtime, or other dependencies. You can use a layer to distribute a dependency to multiple functions. With Lambda Layers, you can configure your Lambda function to import additional code without including it in your deployment package. It is especially useful if you have several AWS Lambda functions that use the same set of functions or libraries. For example, in a layer, you can put some common code about logging, exception handling, and security check. A Lambda function that needs the code in there, should be configured to use the layer. When a Lambda function runs, the contents of the layer are extracted into the     /opt    folder in the Lambda runtime environment. The layer need not be restricted to the language of the Lambda function. Layers also have some limitations: each Lambda function may have only up to 5 layers configured and layer size is not allowed to be bigger than 250MB.

•  Runtime - The runtime provides a language-specific environment that runs in an execution environment. The runtime relays invocation events, context information, and responses between Lambda and the function. AWS offers an increasing number of Lambda runtimes, which allow you to write your code in different versions of several programming languages. At the moment of this writing, AWS Lambda natively supports Java, Go, PowerShell, Node.js, C#, Python, and Ruby. You can use runtimes that Lambda provides, or build your own.

•  Extension - Lambda extensions enable you to augment your functions. For example, you can use extensions to integrate your functions with your preferred monitoring, observability, security, and governance tools.
•  Event - An event is a JSON-formatted document that contains data for a Lambda function to process. The runtime converts the event to an object and passes it to your function code.

•  Trigger - A trigger is a resource or configuration that invokes a Lambda function. This includes AWS services that you can configure to invoke a function, applications that you develop, or some event source.

So, what exactly is behind AWS Lambda?

From an infrastructure standpoint, every AWS Lambda is part of a container running Amazon Linux (referenced as Function Container). The code files and assets you create for your AWS Lambda are called Function Code Package and are stored on an S3 bucket managed by AWS. Whenever a Lambda function is triggered, the Function Code Package is downloaded from the S3 bucket to the Function container and installed on its Lambda runtime environment. This process can be easily scaled, and multiple calls for a specific Lambda function can be performed without any trouble by the AWS infrastructure.

The Lambda service is divided into two control planes. The  control plane is a master component responsible for making global decisions about provisioning, maintaining, and distributing a workload. A second plane is a data  plane that controls the Invoke API that runs Lambda functions. When a Lambda function is invoked, the data plane allocates an execution environment to that function, chooses an existing execution environment that has already been set up for that function, then runs the function code in that environment.

Each function runs in one or more dedicated execution environments that are used for the lifetime of the function and then destroyed. Each execution environment hosts one concurrent invocation but is reused in place across multiple serial invocations of the same function. Execution environments run on hardware virtualized virtual machines (microVMs). A micro VM is dedicated to an AWS account but can be reused by execution environments across functions within an account. MicroVMs are packed onto an AWS-owned and managed hardware platform (Lambda Workers). Execution environments are never shared across functions and microVMs are never shared across AWS accounts.

Even though Lambda execution environments are never reused across functions, a single execution environment can be reused for invoking the same function, potentially existing for hours before it is destroyed.

Each Lambda execution environment also includes a writeable file system, available at  /tmp . This storage is not accessible to other execution environments. As with the process state, files are written to  /tmp remain for the lifetime of the execution environment.

 Cold start VS Warm start

When you call a Lambda Function, it follows the steps described above and executes the code. After finishing the execution, the Lambda Container stays available for a few minutes, before being terminated. This is called a Cold Start.

If you call the same function and the Lambda Container is still available (haven’t been terminated yet), AWS uses this container to execute your new call. This process of using active function containers is called Warm Container and it increases the response speed of your Lambda.

 Role of AWS Lambda in serverless applications

There are a lot of use cases you can use AWS Lambda for, but there are killer cases for which Lambda is best suited:

1.     Operating serverless back-end  

The web frontend can send requests to Lambda functions via API Gateway HTTPS endpoints. Lambda can handle the application logic and persist data to a fully-managed database service (RDS for relational, or DynamoDB for a non-relational database).

2.     Working with external services  

If your application needs to request services from an external provider, there's generally no reason why the code for the site or the main application needs to handle the details of the request and the response. In fact, waiting for a response from an external source is one of the main causes of slowdowns in web-based services. If you hand requests for such things as credit authorization or inventory checks to an application running on AWS Lambda, your main program can continue with other elements of the transaction while it waits for a response from the Lambda function. This means that in many cases, a slow response from the provider will be hidden from your customers, since they will see the transaction proceeding, with the required data arriving and being processed before it closes.

3.     Near-realtime notifications  

Any type of notifications, but particularly real-time, will find a use case with serverless Lambda. Once you create an SNS, you can set triggers that fire under certain policies. You can easily build a Lambda function to check log files from Cloudtrail or Cloudwatch. Lambda can search in the logs looking for specific events or log entries as they occur and send out notifications via SNS. You can also easily implement custom notification hooks to Slack or another system by calling its API endpoint within Lambda.

4.     Scheduled tasks and automated backups  

Scheduled Lambda events are great for housekeeping within AWS accounts. Creating backups, checking for idle resources, generating reports, and other tasks which frequently occur can be implemented using AWS Lambda.

5.     Bulk real-time data processing  

There are some cases when your application may need to handle large volumes of streaming input data, and moving that data to temporary storage for later processing may not be an adequate solution.If you send the data stream to an AWS Lambda application designed to quickly pull and process the required information, you can handle the necessary real-time tasks.

6.     Processing uploaded S3 objects  

By using S3 object event notifications, you can immediately start processing your files by Lambda, once they land in S3 buckets. Image thumbnail generation with AWS Lambda is a great example for this use case, the solution will be cost-effective and you don’t need to worry about scaling up - Lambda will handle any load.

 AWS Lambda limitations

AWS Lambda is not a silver bullet for every use case. For example, it should not be used for anything that you need to control or manage at the infrastructure level, nor should it be used for a large monolithic application or suite of applications.

Lambda comes with a number of “limitations”, which is good to keep in mind when architecting a solution.

There are some “hard limitations” for the runtime environment: the disk space is limited to 500MB, memory can vary from 128MB to 3GB and the execution timeout for a function is 15 minutes. Package constraints like the size of the deployment package (250MB) and the number of file descriptors (1024) are also defined as hard limits.

Similarly, there are “limitations” for the requests served by Lambda: request and response body synchronous event payload can be a maximum of 6 MB while an asynchronous invocation payload can be up to 256KB. At the moment, the only soft “limitation”, which you can request to be increased, is the number of concurrent executions, which is a safety feature to prevent any accidental recursive or infinite loops from going wild in the code. This would throttle the number of parallel executions.

All these limitations come from defined architectural principles for the Lambda service:

•  If your Lambda function is running for hours, it should be moved to EC2 rather than Lambda.
•  If the deployment package jar is greater than 50 MB in size, it should be broken down into multiple packages and functions.
•  If the request payloads exceed the limits, you should break them up into multiple request endpoints.

It all comes down to preventing deploying monolithic applications as Lambda functions and designing stateless microservices as a collection of functions instead. Having this mindset, the “limitations” make complete sense.

 AWS Lambda examples

Let’s now take a look at some AWS Lambda examples. We will start with a dummy Java application and how to create, deploy and trigger AWS Lambda. We will use AWS Command Line Interface(AWS CLI) to manage functions and other AWS Lambda resources.

 Basic application

Let’s get started by creating the Lambda function and needed roles for Lambda execution.

This trust policy allows Lambda to use the role's permissions by giving the service principal lambda.amazonaws.com permission to call the AWS Security Token Service AssumeRole action. The content of  trust-policy.json is the following:

Then let’s attach some permissions to the created role. To add permissions to the role, use the  attach-policy-to-role command. Start by adding the  AWSLambdaBasicExecutionRole managed policy.

 Function code

As an example, we will create Java 11 application using Maven.

For Java AWS Lambda provides the following libraries:

•  com.amazonaws:aws-lambda-java-core – Defines handler method interfaces and the context object that the runtime passes to the handler. This is a required library.

•  com.amazonaws:aws-lambda-java-events – Different input types for events from services that invoke Lambda functions.

•  com.amazonaws:aws-lambda-java-log4j2 – An appender library for Apache Log4j 2 that you can use to add the request ID for the current invocation to your function logs.

Let’s add Java core library to Maven application:

Then we need to add a Handler class which will be an entry point for our function. For Java function this Handler class should implement  com.amazonaws.services.lambda.runtime.RequestHandler interface. It’s also possible to set generic input and output types.

Now let’s create a deployment package from the source code. For Lambda deployment package should be either .zip or .jar. To build a jar file with all dependencies let’s use  maven-shade-plugin .

After running  mvn package command, the resulting jar will be placed into  target folder. You can take this jar file and zip it.

Now let’s create Lambda function from the generated deployment package.

Once Lambda function is deployed we can test it. For that let’s use invoke-command.

 out.json means the filename where the content will be saved. After invoking Lambda you should be able to see a similar result in your  out.json :

 More complicated example

Now let’s take a look at a more complicated application that will show the integration between several AWS services. Also, we will show how Lambda Layers can be used in function code. Let’s create an application with API Gateway as a proxy, two Lambda functions as some back-end logic, and DynamoDB as data storage. One Lambda will be intended to save a new record into the database. The second Lambda will be used to retrieve an object from the database by its identifier.

Let’s start by creating a table in DynamoDB. For simplicity, we’ll add just a couple of fields to that table.

Now let’s create a Java module where some logic with database operations will be put. Dependencies to AWS DynamoDB SDK should be added to the module.

Now let’s add common classes and models to work with the database. This code will be reused in both lambdas.

Model entity object:

Adapter class to DynamoDB client.

Implementation of DAO interface to provide needed persistent operations.

Now let’s build this module and package it into a jar with dependencies. From that jar, a reusable Lambda Layer will be created. Compress fat jar file as a zip archive and publish it to S3. After doing that we will be able to create a Lambda Layer.

Layer usage permissions are managed on the resource. To configure a Lambda function with a layer, you need permission to call  GetLayerVersion on the layer version. For functions in your account, you can get this permission from your user policy or from the function's resource-based policy. To use a layer in another account, you need permission on your user policy, and the owner of the other account must grant your account permission with a resource-based policy.

 Function code

Now let’s add this shared dependency to both Lambda functions. To do that we need to define a provided dependency in  pom.xml.

After that, we can write Lambda handlers. The first one will be used to persist new objects into the database:

 NOTE : in case of subsequent calls AWS may reuse the old Lambda instance instead of creating a new one. This offers some performance advantages to both parties: Lambda gets to skip the container and language initialization, and you get to skip initialization in your code. That’s why it’s recommended not to put the creation and initialization of potentially reusable objects into the handler body, but to move it to some code blocks which will be executed once - on the initialization step only.

In the second Lambda function we will extract object identifiers from request parameters and fetch records from the database by id:

Now create Lambda functions as it was shown in the previous example. Then we need to configure layer usage for functions. To add layers to your function, use the  update-function-configuration command.

You must specify the version of each layer to use by providing the full Amazon Resource Name (ARN) of the layer version. While your function is running, it can access the content of the layer in the  /opt directory. Layers are applied in the order that's specified, merging any folders with the same name. If the same file appears in multiple layers, the version in the last applied layer is used.

After attaching the layer to Lambda we can deploy and run it.

Now let’s create and configure API Gateway as a proxy to Lambda functions.

This operation will return json with the identifier of created API. Save the API ID for use in further commands. You also need the ID of the API root resource. To get the ID, run the  get-resources command.

Now we need to create a resource that will be associated with Lambda to provide integration with functions.

Parameter  --integration-http-method is the method that API Gateway uses to communicate with AWS Lambda. Parameter  --uri is a unique identifier for the endpoint to which Amazon API Gateway can send requests.

Now let’s make similar operations for the second lambda(  get-by-id-function ) and deploy an API.

Note. Before testing API Gateway, you need to add permissions so that Amazon API Gateway can invoke your Lambda function when you send HTTP requests.

Now let’s test our API. First of all, we’ll try to add a new product record:

The result of this call will be like this:

Now we can retrieve created object by its identifier:

And you will get a similar result as after POST request. The same object will be returned in this example.

 AWS Lambda tips

 Debugging Lambda locally

You can use AWS SAM console with a number of AWS toolkits to test and debug your serverless applications locally. For example, you can perform step-through debugging of your Lambda functions. The commands  sam local invoke and  sam local start-api both support local step-through debugging of your Lambda functions. To run AWS SAM locally with step-through debugging support enabled, specify  --debug-port or  -d on the command line. For example:

Also for debugging purposes, you can use AWS toolkits which are plugins that provide you with the ability to perform many common debugging tasks, like setting breakpoints, executing code line by line, and inspecting the values of variables. Toolkits make it easier for you to develop, debug, and deploy serverless applications that are built using AWS.

 Configure CloudWatch monitoring and alerts

Lambda automatically monitors Lambda functions on your behalf and reports metrics through Amazon CloudWatch. To help you monitor your code when it runs, Lambda automatically tracks the number of requests, the invocation duration per request, and the number of requests that result in an error. Lambda also publishes the associated CloudWatch metrics. You can leverage these metrics to set CloudWatch custom alarms. The Lambda console provides a built-in monitoring dashboard for each of your functions and applications. Each time your function is invoked, Lambda records metrics for the request, the function's response, and the overall state of the function. You can use metrics to set alarms that are triggered when function performance degrades, or when you are close to hitting concurrency limits in the current AWS Region.

 Beware of concurrency limits

For those functions whose usage scales along with your application traffic, it’s important to note that AWS Lambda functions are subject to concurrency limits. When functions reach 1,000 concurrent executions, they are subject to AWS throttling rules. Future calls will be delayed until your concurrent execution averages are back below the threshold. This means that as your applications scale, your high-traffic functions are likely to see drastic reductions in throughput during the time you need them most. To work around this limit, simply request that AWS raise your concurrency limits for the functions that you expect to scale.

Also, there are some widespread issues you may face working with Lambda:

 Limitations while working with database

If you have a lot of reading/writing operations during one Lambda execution, you may probably face some failures due to Lambda limitations. Often the case is a timeout on Lambda execution. To investigate the problem you can temporarily increase the timeout limit on the function, but a common and highly recommended solution is to use batch operations while working with the database.

 Timeout issues on external calls

This case may occur if you call a remote API from Lambda that takes too long to respond or that is unreachable. Network issues can also cause retries and duplicated API requests. To prepare for these occurrences, your Lambda function must always be idempotent. If you make an API call using an AWS SDK and the call fails, the SDK automatically retries the call. How long and how many times the SDK retries is determined by settings that vary among each SDK. To fix the retry and timeout issues, review the logs of the API call to find the problem. Then, change the retry count and timeout settings of the SDK as needed for each use case. To allow enough time for a response to the API call, you can even add time to the Lambda function timeout setting.

 VPC connection issues

Lambda functions always operate from an AWS-owned VPC. By default, your function has full ability to make network requests to any public internet address — this includes access to any of the public AWS APIs. You should configure your functions for VPC access when you need to interact with a private resource located in a private subnet. When you connect a function to a VPC, all outbound requests go through your VPC. To connect to the internet, configure your VPC to send outbound traffic from the function's subnet to a NAT gateway in a public subnet.

 With the development of artificial intelligence, the Internet of Things, and cloud solutions, the amount of data we can retrieve from a vehicle is expanding every year. Manufacturers improve efficiency in converting this data into new services and enhance their own offerings based on the information received from connected car systems. Can software-defined vehicle solutions be successfully applied to enabling fleet management systems for hundreds or even thousands of models? Of course, it can, and even should! This is what today's market, which is becoming steadily more car-sharing and micro mobility-based, expects and needs.

Netflix, Spotify, Glovo, and Revolut have taught us that entertainment, ordering food, or banking is now literally at our fingertips, available here and now, whenever we need or want it. Contactless, mobile-first processes, that reduce queues and provide flexibility, are now entering every area of the economy, including  transportation and the automotive industry .

Three things: saving time, sparing money, and ecological trends dramatically change the attitude toward owning a car or choosing means of transport. Companies such as Uber, Lyft, or Bird cater to the needs of the younger generation, preferring renting over ownership.

The data-driven approach has become a cornerstone for automotive companies - both new, emerging startups and older, decades-old business models, such as car rental companies. None of the companies operating in this market can exist without a secure and well-thought-out IT platform for fleet management. At least if they want to stay relevant and compete.

It is the software - on an equal footing, or even first before the unique offer - that determines the success of such a company and allows it to  manage a fleet of vehicles , which sometimes includes hundreds, if not thousands of models.

Depending on the purpose of the vehicles, the business model, and the scale of operations, solutions based on software will obviously vary, but they will be beneficial to both the fleet manager and the vehicle renter. They allow you to have an overall view of the situation,  extract more useful information from received data and reasonably scale costs.

Among the potential entities that should be interested in improvements in this matter, the following types of fleets can be specifically mentioned:

•  city e-scooters, bicycles, and scooters;
•  car rentals;
•  city bus fleets;
•  tour operators;
•  transport and logistics companies;
•  cabs;
•  public utility vehicles (e.g., fire departments, ambulances, or police cars) and government limousines;
•  automobile mechanics;
•  small private fleets (e.g., construction or haulage companies)
•  insurers' fleets;
•  automobile manufacturers' fleets (e.g., replacement or test vehicles).

The benefits of managing your fleet with cloud software and the Internet of Things (IoT)

Real-time vehicle monitoring (GPS)

A sizeable fleet implies a lot of responsibility and potentially a ton of problems. That's why it's so important  to promptly locate each vehicle included and monitor it in real-time:

•  the distance along the route,
•  the place where the car was parked,
•  place of breakdown.

This is especially useful in the context of a bus fleet, but also in the  sharing-economy group of vehicles : city e-scooters, bicycles, and scooters. In doing so, the business owner can react quickly to problems.

Recovering lost or stolen vehicles

The real-time updated location, working due to  IoT and wireless connectivity , also enables operations in emergency cases. This is because it allows you to  recover a stolen or abandoned vehicle.

These benefits will be appreciated, for example, by people in charge of logistics transport fleets. After all, vehicles can be stolen in overnight parking lots. In turn, the fight against abandoned electric 2-wheelers will certainly be of interest to owners of the startups, which often receive complaints about scooters abandoned outside the zone, in unusual places, such as in fields or ditches in areas where there is no longer a sidewalk.

Predictive maintenance

We should also mention  advanced predictive analytics for parts and components such as brakes, tires, and engines. The strength of such solutions is that you receive a warning (vehicle health alerts) even before a failure occurs.

The result? Reduced downtime, better resource planning, and streamlined decision-making. According to estimates, these are savings of $2,000 per vehicle per year.

More convenient vehicle upgrades - comprehensive OTA (Over-the-Air)

Over-the-Air (OTA) car updates are vital for safety and usability. Interconnected and networked vehicles  can be updated in one go , simultaneously. This saves the time otherwise required to manually configure each system one by one. In addition, operations can also be performed on vehicles that happen to be out of the country.

Such a facility applies to virtually all industries relying on extensive fleets, especially in the logistics, transportation, and tourism sectors.

Intermediation in renting

A growing number of services are focusing on  service that is fast, simplified, and preferably remote. For instance, many rooms or apartment rentals on Airbnb rely on self-service check-in and check-out, using special lockups and codes.

Similar features are offered by  software-defined vehicles , which can now be rented "off the street", without the need for service staff. The customer simply selects a vehicle and, via a smartphone app, unlocks access to it. Quick, easy, and instant.

Loyalty scheme for large fleets

Vehicle and software providers are well aware that new technology comes with great benefits, but also with a degree of investment. In order to make such commitments easier to decide upon, attractive loyalty schemes are being rolled out for larger fleets.

So as a business owner you reap double benefits. And at the same time you test, on lucrative terms, which solutions work best for you.

Improved fleet utilization

Cloud and IoT software enables more practical use of the entire fleet of available vehicles and accurately pinpoints bottlenecks or areas where the most downtime occurs.

This is an invaluable asset in the context of productivity-driven businesses, where even a few hours of delay can result in significant losses.

By contrast,     artificial intelligence(AI)-based predictions   (for example, information about an impending failure)  offered to commercial fleets provide fleet managers with more anticipatory data , which can significantly cut business costs. Other benefits include improved emissions control or higher environmental standards.

Increasing safety

Minimized almost to zero danger of hacking into the system contributes to the security of the fleet-based business.

    Case study: Ford Pro™ Telematics  

Revenues based on software and digital services is not a bad deal for all informed participants in the business environment. Some big players like Ford have based their entire business model, on this idea.  With their Ford Pro™ series of solutions, they want to become an accelerator for highly efficient and sustainable business. Their offering is based on market-ready commercial vehicles to suit almost any business needs and on all-electric trucks and vans. They are developing telematics in particular.

 Ford Chief Executive Jim Farley puts it bluntly: We are the Tesla of this industry.

Bold assumptions? Yes, but also an equally bold implementation. Created in May 2021, a standalone Ford Pro™ unit is to focus exclusively on commercial and government customers. The new model also serves as a prelude to expanding digital service offers for retail customers.

The objective is to increase Ford Pro's annual revenue to $45 billion by 2025, up 67% from 2019.

Streamlined vehicle repairs

Managing a large group of vehicles also necessitates regular inspections and repairs, and at different times for different vehicles. This entails the need to control each unit individually.

The risk this poses is that information about the problem may not reach decision-makers in time, and besides, instead of the service and product, the executive is constantly focused on responding to anomalies. New technologies partially eliminate this problem.

As part of the Ford Pro Telematics Essentials package, vehicle owners receive real-time alerts on vehicle status in the form of engine diagnostic codes, vehicle recalls, and more. There's also a  scheduled service tracking feature and, in the near future, remote locking/unlocking, which will further enhance fleet management.

Driver behavior insights

Human-centered technology can help improve driver performance and road safety. Various sensors and detectors inside Ford vehicles provide a lot of interesting  information about the driver's behavior. They monitor the frequency and suddenness of actions such as braking or accelerating. Knowledge of this type of behavior allows for better fleet planning and improved driver safety.

Fuel efficiency analysis

Fuel is one of the major business costs for companies managing a large number of vehicles. Ford Pro™ Telematics, therefore, approaches customers with a solution to  monitor fuel consumption and engine idle time.

This functionality is designed to  optimize performance and reduce expenses. Better exhaust control also indirectly lowers operating costs.

Manage all-electric vehicle charging with E-Telematics

Telematics also provides an efficient way to manage a fleet consisting of electric vehicles. There are many indications that due to increasingly stringent environmental standards, they will form the backbone of various operations.

That's why Ford has developed its own E-Telematics software. It enables  comprehensive monitoring of the charging status of the electric vehicle fleet. In addition, it helps drivers find and pay for public charging points and facilitates reimbursement for charging at home.

The system also offers the ability to accurately compare the efficiency and economic benefits of electric vehicles versus gas-powered ones.

Better cooperation with insurers

Cloud-based advanced telematics software not only provides a better customer experience. What also counts is a streamlined collaboration with insurance providers and the delivery of vehicle rental services to clients of such companies.

This, of course, requires a special tool that enables:

•  remote processing of the case reported by the customer,
•  making the information available to the rental company,
•  allowing rental company personnel to provide a vehicle that meets the driver's needs.

The goal is to provide  replacement cars for the customers of partnering insurers .

Touchless and counter-less experience

It includes  verifying a customer and unlocking a car using a mobile app . This translates into greater customer satisfaction and the introduction of new business models. With the introduction of mobile apps in app stores,  queues can be shortened. This results in a simplified rental process. From now on, it is more intuitive and focused on user experience and benefits. Because nowadays customers expect mobile and contactless service.

    Case study: car rental  

The leading rental enterprise teamed up with Grape Up to  provide counter-less rental services and a touchless experience for their customers . By leveraging a powerful touchless platform and telematics system used by the rental enterprise, the company was able to build a more customer-friendly solution and tackle more business challenges, such as efficient stolen car recovery and car insurance replacement.

Software-defined vehicle solutions in vehicle fleets. How do implement them sensibly?

Technological changes that we are experiencing in the entertainment industry or e-commerce have also made their way into the automotive sector as well as micro-mobility and car rentals. There are many indications that there is no turning back.

Solutions such as real-time tracking, predictive maintenance, and driverless rental are the future. They help manufacturers execute their key processes more efficiently and track and manage their fleets effectively. In turn, the end customer receives an intuitive and convenient tool that fosters brand loyalty and makes life easier.

Of course, they need to be implemented properly. A large role is played by the quality of software. The key is the efficient flow of data and their cooperation with devices inside the vehicle. That is why it is worth choosing for business cooperation such a company that not only has the appropriate technological competence, but also the knowledge and experience gained during other such projects and implementations for the automotive industry.