Mood focused car enhancement - driving experience coupled with technological sense

 Among the many vehicle functions that intelligent software increasingly performs for us, parking is certainly the one that the majority of us would be most willing to leave to algorithms. While a ride on the highway can be seamless or a long road trip can be smooth, it is also the moment when the engine slows down and the search for a parking space, for a significant number of drivers, becomes a real test of skills. How about getting it automated? This would be beneficial not only for the driver but also for OEM-s, who can use such technology in factories and when loading and unloading vehicles onto ships or trains. Automated Valet Parking developed in BMW iX shows that this process has already started.

Parking difficulties are influenced not only by the dynamically changing circumstances of each parking operation and the large number of factors that must be monitored but also by overloaded parking lots and the endless chase for a time. According to statistics, it is in parking lots that the highest number of collisions and accidents occur, and it is this element that drivers often point out as causing them the most trouble.

According to the National Safety Council statistics, over 60,000 people are injured in parking lots every year. What is more, there are more than 50,000 crashes in parking lots and garages annually. In contrast, according to insurer Moneybarn, 60 percent of drivers found parallel parking to be stressful.

Leaving security in the hands of technology

It's no wonder that car companies around the world are looking for a foothold in exactly this part of automation, which could allow them to convince users to place their confidence in fully autonomous vehicles.

Increased safety - which can definitely be influenced by the introduction of such solutions - has always been at the forefront of all ratings showing driver approval of  SVD (software-defined vehicle) technology . With automatic parking, the driver additionally receives  time-savings, convenience, and reduced stress, because they do not have to waste energy on searching for a free spot, nor think about where they parked their vehicle. An algorithm and a system of networked sensors make the parking decisions for the driver. All the driver has to do is leave the car in a special drop-off/pick-up zone and confirm parking in the application. After shopping at the mall or a meeting, the user again confirms the vehicle pick-up in the app and proceeds to the zone where their vehicle is already parked.

This stress-free handover of the car into the trusted hands of a "digital butler", opens up new service opportunities also for OEMs and  companies cooperating with the automotive industry . While the driver can go shopping or go to the movies in peace, the vehicle can be serviced during this time. Among the potential applications are services such as:

•  automatic charging in the case of an electric vehicle;
•  OTA-based software upgrade;
•  vehicle washing and waxing
•  changing summer/winter tires;
•  minor repair work - such as replacing lights or wipers.

Let's take a look at two of the most impressive use cases in this area that have appeared on the market recently. The first one is the Automated Valet Parking project, implemented in partnership with top car manufacturers and technology providers, with BMW leading the way. The second one is the offer of Nvidia, which managed to start cooperation with Mercedes-Benz in this field.

BMW Autonomous Valet Parking

Futurists of the 20th century predicted that the next century would bring us an era of robots able to perform most daily human activities on their own, in an intelligent, autonomous, and efficient way. Although this vision was a gross exaggeration, today on the market there are solutions that can clearly be described as innovative or ahead of their time.

An example? BMW and their all-electric flagship SUV, BMW iX, which communicates with external infrastructure and parks 100 percent without the driver’s input. The owner of the vehicle simply steps out of the car, handing it over to the "technological guardian".

The data exchange here takes place in three tracks: vehicle, smartphone app, and underground garage parts (cameras + sensors). The driver activates the Autonomous Valet Parking (AVP) option in the application, thanks to which the vehicle is able to maneuver independently around the garage without his participation. And all this with maximum safety, both in terms of collision-avoidance and protection of expensive items inside the vehicle.

This project would be much harder without the modern 5G network equipment provided by Deutsche Telekom. Why a fifth-generation network? Because compared to traditional WLAN solutions, it allows to dynamically enable, disable and update capabilities through API.

The flexible configuration and very low latency allow to shape the bandwidth and prioritize the vehicle connectivity traffic, making the connection stable, fast and reliable. This is one of the key requirements for any  Connected Car system which is coupled with Autonomous Vehicle capabilities - if the connection is not reliable, latency is too high, or another device takes over the bandwidth, it may result in jerk, stuttering ride, as the data from external sensors is transferred late.

However, these are not all the surprises that the BWM Group has in store for their customers. In addition to parking, the driver can also benefit from other automated service functions such as washing or intelligent refueling. The solution is universal and can also be used by other OEMs.

  https://youtu.be/iz_yKaa8QgM

Nvidia cooperate with Mercedes-Benz

There are many indications that Voice Assistant will be growing. For example, in 2020 in the U.S. alone, about 20 million people will make purchases via smartphone using voice-activated features [statista.com]. This trend isn't sparing the automotive industry, either, with technology providers racing to create software that would revolutionize such cumbersome tasks as parking. One of the forerunners is the semiconductor giant Nvidia, which created the  Nvidia Drive Concierge service . It's an artificial intelligence-based software assistant that - literally - gives the floor to the driver, but also lets technology come to the fore.

"Hey Nvidia!" What does this voice command remind you of? Most often it is associated with another conversational voice assistance system, namely Siri. You are on the right track, because NDC works on a similar principle. The driver gives a command, and the assistant is able to recognize a specific voice, assign it to the vehicle owner and respond.
By far the most interesting functionality is the ability to integrate the software with Nvidia Drive AV autonomous technology, or on-demand parking. This works in a very intuitive way. All you have to do is get out of the vehicle, activate the function and watch as the "four wheels" steer themselves towards a parking space. And they do it in a collision-free manner, regardless of whether it's parallel, perpendicular or angled parking. It will work the same way in the reverse direction. If you want to leave a parking space, you simply hail the car, it pulls up on its own and is ready to continue its journey.

Sounds like total abstraction? It's already happening. Nvidia has teamed up with one of the world's leading OEMs, Mercedes-Benz. Starting in 2024, all next-generation Benz vehicles will be powered by Nvidia Drive AGX Orin technology, along with sensors and software. For the German company, automated parking services will therefore soon become common knowledge.

This is what Jensen Huang, founder and CEO of Nvidia, said about the collaboration:  Together, we're going to revolutionize the car ownership experience, making the vehicle software programmable and continuously upgradable via over-the-air updates. Every future Mercedes-Benz with the Nvidia Drive system will come with a team of expert AI and software engineers continuously developing, refining and enhancing the car over its lifetime.

Automated Valet Parking: innovation at the cutting edge of technology

Vehicle automation and the resulting cooperation between OEMs and suppliers of new technologies is now entering new dimensions. Also in this area that many drivers associate with something very cumbersome, which often generates anxiety.

The integration of Nvidia Orin systems at Mercedes-Benz or the comprehensive AVP at BMW are prime examples of how new solutions at the intersection of  AI , IoT, and 5G are becoming, to some extent, guardians of safety and guarantors of comfort from start to finish. It's also a good springboard to talk about fully automated vehicles.

 Accidents, traffic congestion, lack of parking lots and poor state of roads. These are the 4 Horsemen of the Road Apocalypse that on occasion haunt cities around the globe. Have they come to settle in the largest agglomerations for good? Can AI in transportation combat them and make mobility smoother, more comfortable, and safer? Practical solutions introduced by the biggest transport companies from all over the world show that it is possible. And we do not have to wait for fully self-driving cars to use the advantages of AI. The changes are happening right before our eyes.

In 1900, the number of vehicles in the USA - the only country that produced cars at the time - reached 4192 vehicles. Today, the number of motor cars is estimated to be around 600 million, and with the current growth in production, this number is expected to double in the next 30 years. Our cities are congested, polluted and in many places getting around in a car during rush hour borders on the miraculous. Not to mention the real endurance test that drivers' nerves are put to.

Government agencies and shipping companies must explore solutions that reduce the number of vehicles in cities and equip urban infrastructure and cars with tools that effectively offset the side effects of technological globalization. The  Internet of Things and artificial intelligence are coming to the rescue to facilitate a new class of intelligent transportation systems (ITS), not only for  automotive but also for rail, marine, and aircraft transportation.

By  analyzing massive amounts of data from vehicles and  connecting the road infrastructure into a seamless network of information exchange , many aspects of transportation can be successfully addressed. The benefits of using AI in this market area are not only for cities and drivers but also for transport companies, pedestrians, and the environment. The whole transport ecosystem benefits from it, not just one of its constituent parts. We should all care about the development of these technologies and the broadest possible use of them in transport.

Thanks to the above-mentioned technologies, new trends are developing, such as micro-mobility,  shared mobility or, especially in the Netherlands and Scandinavia, the idea of mobility-as-a-service (MaaS), which encourages drivers to give up their own vehicle and exchange it for one in which transport is provided as a service.

Benefits of introducing AI in transportation

According to Market Data Forecast, the global transportation AI market will be worth around $3.87 billion by 2026 and is estimated to grow at a CAGR of 15.8% between 2021 and 2026. And it's no wonder that more and more businesses are embracing these solutions. The benefits of using AI technology in transportation are truly far-reaching and, indeed, their future is looking bright. With the development of data analytics and more modern sensors gathering information, new and innovative applications are bound to emerge.

Today, key benefits of using AI in transportation include:

•  increasing transportation safety;
•  detecting market trends;
•  relieving traffic congestion;
•  reducing greenhouse gas emissions, air pollution, and noise;
•  improved transportation design and management;
•  better management of urban space and reclaiming specific urban areas for residents;
•  analyzing travel needs and pedestrian behavior.

9 use cases of AI in transportation

When talking about using AI in transportation, self-driving cars are the most often mentioned examples that stir the imagination. Although such solutions have already been tested on the city streets (e.g. Waymo and Cruise in California) and occasionally we hear news about reaching by the manufacturer the highest (5th) level of automation, we are still a little away from the dissemination of vehicles that do not need any attention of the driver.

The main challenges faced by autonomous driving remain unchanged. First,  detection of objects on the road and their categorization, and second,  making the right decisions by the neural network, decision tree, or, in most cases, complicated hybrid model.

In 95% of cases, the neural network controlling the vehicles is already behaving correctly and making the best possible decisions. But there is still a marginal 5%, and this level is the most difficult to achieve at the moment. It simply takes time and more data to "train" a neural network. With the dropping price of LIDARs [light detection and ranging sensors], high resolutions camera, and the computing power of the GPUs [graphic processing units] increasing, it is only a matter of the next few years before this barrier is overcome - first in limited controlled areas (e.g. factories and harbors), the form of autonomized truck transport, and then using city vehicles.

Meanwhile, there are already more than a dozen advanced technologies on the road today that are  taking advantage of the AI ‘’goodies’’ and changing the way we control vehicle flow, driver safety, and driving behavior. Let's take a closer look at them.

1.Traffic detection & traffic signs

If traffic regulations were boiled down to one simple rule that even a few-year-old child could understand, red and green lights would definitely be second to none. Meanwhile, there are hundreds of road accidents each year related to running the red light and not stopping the vehicle at the right moment. Many factors contribute to this, such as driver fatigue, inclement weather, misuse of cell phones while driving, or simply rushing and time pressure.

People make mistakes and always will, these cannot be avoided. However, we have started to teach the machine to recognize traffic lights and eradicate such mistakes (the first attempts were made by BMW and Mercedes). With this technology, the braking system will react automatically when the driver tries to run a red light, and thus we can prevent disaster.

2. Pedestrian detection

The unpredictability of pedestrians and their different behavior on the road is one of the main factors holding back the mass introduction of autonomous cars. Thanks to computer vision,  AI already recognizes trees, unusual objects, and pedestrians without much of a struggle, and can warn drivers of a human approaching the roadway. The problem arises when a pedestrian is carrying groceries, holding a dog on a lead, or is in a wheelchair. Their unusual shape increases the difficulty for the machine to properly identify a human. Although it must be admitted that by using various object detection functions - based on motion, textures, shapes, or gradients - it is practically 100% successful.

However, the pedestrian's intention still remains a great challenge. Will he or she step onto the road or not? Are they only walking by the side of the road, or do they intend to cross it? These elements are always ambiguous and a neural network is needed to predict them effectively. To this end, the human pose estimation method comes in handy. It is based on the dynamics of the human skeleton and is capable of predicting human intentions in real-time.

3.Traffic Flow Analysis

Noise, smog, clogged city arteries,  stressed drivers, economic losses, greenhouse gas emissions - traffic congestion and vehicle crowding in cities give rise to numerous undesirable phenomena. AI can effectively help counteract all of them and make transportation much more efficient and convenient.

By relying on in-vehicle sensors, municipal CCTV cameras, and even drones to monitor vehicle flow, the algorithms can watch and keep track of the traffic both on highways and in the city. This allows them to warn drivers of potential congestion or accidents and direct the flow of vehicles in an efficient manner. It is also invariably useful for the town and urban planners involved in constructing new roads and improving the city's infrastructure. With prior traffic analysis and the vast amount of data available,  AI can identify the best planning solutions and help reduce undesirable situations right at the planning stage.

4.Inspection of dangerous turns, traffic circles and bike lanes

On a macro scale AI can help us change the entire road network, and on a micro-scale- a single intersection or traffic circle that needs repair. The analysis of the material provided by intelligent algorithms can calculate the trajectory of vehicles entering the bend, analyze the risk of potential conflicts between vehicles - pedestrians - cyclists, the speed at which vehicles enter the bend, or the waiting time at the traffic lights. The analysis of all this invaluable information can help optimize a given road section, and improve the safety and convenience of transport.

5.Computer Vision-Powered Parking Management

Entering the city center by car and finding a parking lot is often a struggle. If we connect the city's parking lots into an efficient network of sensors that monitor available spaces, the length of time vehicles are parked, and the hours when vehicles are most heavily congested, this key aspect of traffic can be greatly enhanced. With maps embedded in vehicles, AI can facilitate finding free parking spots, alert you to potential parking overcrowding, and - something actually pretty common - allow you to find your car when you forget where you parked it.

Such solutions are particularly useful in places such as airports, sports stadiums or arenas, where traffic must be smooth, and a high volume of visitors may pose a threat to safety.

6. Automated license plate recognition

A useful application of AI and computer vision is car license plate recognition. This type of technology is often used when entering highways, tunnels, ferries, or restricted areas constrained by gates or barriers. AI helps verify whether a given vehicle is on the list of registrations that, due to the fee paid or the drivers' status, are allowed to access a given area.

License plate recognition by algorithms is also a well-proven tool in the hands of the police and security services, who in this way are able to pinpoint the route of a particular vehicle or verify the driver's alibi.

7. Road condition monitoring

Each year potholes cause $3,000,000,000 worth of damage to vehicles in the U.S. alone. Intelligent algorithms can warn drivers of surprises lurking on the roads and monitor the condition of the road surface, so they can notify the authorities in advance of potential spots that will soon need fixing. This is enabled by linking the camera to ADAS, which applies machine learning to gather real-time information from the road surface where it is moving.

In this way, the driver can be warned not only of roadway damage but also of wet surfaces, ice, potholes or dangerous road debris. All of this improves safety for travelers, prevents accidents, and saves money - both in terms of drivers' finances and city funds.

8. Automatic Traffic Incident Detection

Video surveillance has been with us on the roads for ages, but it wasn't until the system was supported by AI solutions that it became possible to detect traffic incidents more efficiently, respond faster and provide information to traffic users practically in real-time.

By linking cameras within an ITS system, using computer vision technology, and equipping vehicles with intelligent sensors, we can detect different types of accidents. Intelligent algorithms save lives, prevent serious accidents and warn road users of hazardous situations by recommending safer travel options.

 The most commonly detected traffic incidents include:

•  pedestrians or animals entering on the road;
•  vehicles moving too fast or too slowly;
•  vehicles blocking the passage;
•  detection of debris on the road;
•  identification of vehicles moving in the wrong direction

9. Driver Monitoring

Finally, there is a full category of artificial intelligence solutions that apply directly in the interior of the car and affect the drivers themselves (we covered this in more depth  in this article ). Among them, three are particularly noteworthy:

•     driver’s fatigue monitoring    - by detecting the driver's face and estimating the position of the head, the system can detect drowsiness and emotions of the driver and thus prevent an accident.
•     alerts when the driver gets distracted    - for instance, when they reach for their cell phone, veer out of their lane, or turn around in the back seat to talk to fellow passengers.
•     emergency assist systems    - when the driver is not responsive and does not operate the vehicle, the car first tries to wake the driver by braking and pulling safety belts, and if it fails pulls over and calls emergency.

AI in transportation: setting the course for change

Given the speed at which computer processing power is changing and the number of sensors from which  data is being collected , fully automated cars on city roads are likely to be a question of the nearest 5-10 years. Change is happening at an exponential rate and today's applications of AI in transportation are just the first glimpse of the possibilities offered by intelligent algorithms. Change is essential and inevitable, e.g. due to the challenges facing the global community when it comes to global warming.

An increasing number of people live in cities, own not one but two vehicles, and want to travel to work or do their shopping in comfort. Transport companies and city managers must join forces with IT companies to fully tap into the potential of AI and change transport to be more efficient, environmentally friendly and suited to the way we want to use our cities. This is the only way we can make transportation sustainable and remove obstacles on the way to a zero-carbon economy and smart cities. Otherwise, we may face a vision of the future in which scientists predict traffic congestion 10 times worse than we experience today.

 Automotive is transforming into a hyper-connected, software-driven industry that goes far beyond the driving experience. How to build applications in such an innovative environment? What are the main challenges of providing software for connected cars and how to deal with them? Let’s dive into the process of utilizing the capabilities of the cloud to move automotive forward.

People have always aimed for the clouds. From Icarus in Greek mythology, first airplanes and spaceships to dreams about flying cars – our culture and history of technology development express a strong desire to go beyond our limits. Although the vision from Back to the Future and other Sci-Fi movies didn’t come true and our cars cannot be used as flying vehicles, our cars actually are in the cloud.

Meanwhile, the idea of the Internet of Things came true; our  devices are connected to the Internet . We have smartphones, smartwatches, smart homes and, as it turns out, smart cars. We are able to communicate with them to gather data or even remotely control them. The possibilities are only limited by hardware, but even it is constantly improving to follow the pace of rapid changes triggered by software development.

Offerings on the automotive market are developing rapidly with numerous features and promised experiences to the end customer. By using cutting-edge technologies, utilizing cloud platforms, and working with innovative software developers,  automakers provide solutions to even the most demanding needs . And while our user experience is improving at an accelerated pace, there is still a broad list of challenges to tackle.

In this article, we dive into the technology behind the latest trends, take into account the most demanding areas of developing software in the cloud, and explain how proper solution empowers the change that affects us all.

Challenging determinants of the cloud revolution in automotive

Connecting with your car through a smartphone or utilizing information about traffic provided to your vehicle thanks to the platforms that accumulate data registered by other drivers is extremely useful.

Those innovative changes wouldn’t be possible without  cloud infrastructure . And as there is no way back from moving to the cloud, the transition creates challenges in various areas:  safety, security, responsiveness, integrity , and more.

Safety in the automotive sector

How to create a solution that doesn’t affect the safety of a driver? When developing new services, you cannot forget about the basics. Infotainment provided to vehicles is more advanced for every new release of a car and can be really engaging. The amount of delivered information combined with increasingly larger displays may lead to distraction and create dangerous situations. It’s worth mentioning that some of the colors may even impair the driver’s vision!

Integration with the cloud usually enables some of the remote commands. When implementing them, there are a lot of restrictions that need to be kept in mind. Some of them are obvious, such as you don’t want to disable the engine when a car is being driven 100km/h, but others may be much more complicated and unseen at first.

Providing security for car owners

Enabling services for your vehicle in the cloud, despite being extremely helpful to improve your experience, creates another way to break into your car. Everyone would like to open a car without using keys, but using a mobile phone, voice, or a fingerprint instead. And as these solutions seem modern and fancy, there is a big responsibility on the software side to do it securely.

Responsiveness enabling the seamless user experience

 Customer-facing services need to deliver a seamless experience to the end-user. The customer doesn’t want to wait a minute or even ten seconds for unlocking a car door. These services need to do it immediately or not at all, as an issue with opening the doors just because the system had a ‘lag’ is not acceptable behavior.

Data integrity is a must

Another very important concept associated with providing solutions utilizing cloud technologies is data integrity.  Information collected by your vehicle should be useful and up to date. You don’t want a situation when the mobile application says that the car has a range of 100km, but in the morning, it turns out that the tank is almost empty, and you need to refuel it before going to work.

How to integrate and utilize mobile devices to connect with your vehicle?

When discussing how to use mobile phones to control cars, a very important question occurs; how to communicate with the car? There is no simple answer, as it all depends on what model and version of a car it is, as depending on a provider, the vehicles are equipped with various technologies. Some of them are equipped with BLE, Wi-Fi Hotspots, or RFID tags, while others don’t offer a direct connection to the car, and the only way is to go through the backend side. Most of the manufacturers will expose some API over the Internet without providing a direct connection from mobile to the car. In such cases, usually, it’s a good practice to create your own backend which handles all API flaws. To do so, your system will need a platform to have a reliable solution.

When the limitation of hardware is met, there is always an option to equip the car with a custom device, which will expose a proper communication channel and will be integrated with the vehicle. To do so, it may use the OBD protocol. It gives us full control over the communication part, however, it’s expensive and hard to maintain the solution.

Building a platform to solve the challenges

There is no simple answer on how to solve the mentioned challenges and implement a resilient system that will deliver all necessary functionalities with the highest quality. However, it’s very important to remember that such a solution should be scalable and utilize cloud-native patterns. When designing a system for connected cars, the natural choice is to go with the microservice architecture. The implementation of the system is one thing, and partly this topic was covered in the     previous article   , but on the other hand, the very important aspect is a runtime, the platform. Choosing the wrong setup of virtual machines or having to deploy everything manually can lead to downtime of the system. Having a system that isn’t available for the customer constantly can damage your business.

Kubernetes to the rescue! As probably you know, Kubernetes is a container orchestration platform, which allows running workload in pods. The platform itself helped us to deliver many features faster and with ease to our clients. Nowadays, Kubernetes is so easily accessible that you can spin up a cluster in minutes using existing service providers like AWS or Azure. It allows you to increase the speed of delivery of new features, as they may be deployed immediately! What’s very important with Kubernetes, is its abstraction from infrastructure. The development team with expertise in Kubernetes is able to work on any cloud provider. Furthermore,     mission-critical systems can successfully implement Kubernetes   for their use cases as well.

Automotive cloud beyond car manufacturers

 Automotive cloud is not only a domain of car manufacturers. As mentioned earlier, they offer digital services to integrate with their cars, but numerous mobility service providers integrate with these APIs to implement their own use cases.

•  Live notifications
•  Online diagnostics
•  Fleet management
•  Vehicle recovery
•  Remote access
•  Car sharing
•  Car rental

The best practices of providing cloud-native software for the automotive industry

Working with  the leading auto motive brands and being engaged in numerous projects meant to deliver innovative applications. Our team have collected a group of helpful practices which make development easier and improve user experience. There are some must-have practices when it comes to delivering high-quality software, such as CI/CD, Agile, DevOps, etc., – they are crucial yet well-known for the experienced development team and we don’t focus on them in this article. Here we share tips dedicated for teams working with app delivery for automotive.

Containerize your vehicle

One of the things we’ve learned     collaborating with Porsche   is that vehicles are equipped with ECUs and installing software on them isn’t easy. However, Kubernetes helps to mitigate that challenge, as we can mock the target ECU by docker image with specialized operating systems and install software directly in it. That’s a good approach to create an integration environment that shortens the feedback loop and helps deliver software faster and better.

Asynchronous API

In the IoT ecosystem, you can’t rely too much on your connection with edge devices. There are a lot of connectivity challenges, for example, a weak cellular range. You can’t guarantee when your command to the car will be delivered and if the car will respond in milliseconds or even at all. One of the best patterns here is to provide the asynchronous API. It doesn’t matter on which layer you’re building your software if it’s a connector between vehicle and cloud or a system communicating with the vehicle’s API provider. Asynchronous API allows you to limit your resource consumption and avoid timeouts that leave systems in an unknown state.

Let’s take a very simple example of a mobile application for locking the car remotely.

 Synchronous API scenario

1.  A customer presses a button on the application to lock the car.
2.  The request is sent and is waiting for a response.
3.  The request needs to be delegated to the car which may take some time.
4.  The backend component crashes and starts without any knowledge about the previous request.
5.     The application gets a timeout.  
6.  What now? Is the car locked? What should be displayed to the end-user?

 Asynchronous API scenario

1.  The customer presses a button on the application to lock the car.
2.     The request is sent and ended immediately.  
3.  The request needs to be delegated to the car which may take some time.
4.  The backend component crashes and starts without any knowledge about the previous request.
5.  The car sends a request with the command result through the backend to the application.
6.     Application displays: “Car is locked.”  

With asynchronous API, there’s always a way to resend the response. With synchronous API, after you lose connection, the system doesn’t know where to resend response out of the box. As you may see, the asynchronous pattern handles this case perfectly.

Digital Twin

DigDigital Twin is a virtual model of a process, a product or a service, in case of automotive – a digital cockpit of a car. This pattern helps to ensure the integrity of data and simplify the development of new systems by its abstraction over the vehicle. The concept is based on the fact that it stores the actual state of the vehicle in the cloud and constantly updates it based on data sent from a car. Every feature requiring some property of vehicle should be integrated with Digital Twin to limit direct integrations with a car and improve the execution time of operations.

Implementation of Digital Twin may be tricky though, as it all depends on the vehicle manufacturer and API it provides. Sometimes it doesn’t expose enough properties or doesn’t provide real-time updates. In such cases, it’s even impossible to implement this pattern.

Software for Connected Cars - Summary

We believe that the future will look more futuristic than we could have ever imagined. Autonomous cars, smart cars, smart homes, every device tries to make our lives easier. It’s not known when and how these solutions will fully utilize Artificial Intelligence to make this experience even better. Everything connects as numerous IoT devices are connected which provides us with unlimited possibilities.

T  he automotive industry is currently transforming, and it isn’t only focusing on the driving experience anymore. There is a serious focus on connected mobility and other customer-oriented services to enhance our daily routines and habits. However, as software providers, we should keep in mind that automotive is a mature industry. The first connected car solutions were built years ago, and it’s challenging to integrate with them. These best practices should help focus on customer experience. Unreliable systems won’t encourage anyone to use it, and bad reviews can easily destroy a brilliant idea.

The automotive industry is experiencing a challenging transformation. We can notice these changes with every new model of a car and with every new service released. However, to keep up with the pace of the changing world, the industry needs modern technologies and reliable solutions, such as Kubernetes. And on top of that cloud-native application,     software created with the best practices by experienced engineers   who use the customer-first approach.