Thinking out loud

 Cities of the future are spaces that are comfortable to live in, eco-friendly, safe, and intelligently managed. It's hard to imagine such a futuristic scenario without the use of advanced technology. Preferably one that combines various elements within one coherent data processing system. Especially great potential lies in solutions at the crossroads of automotive, telematics, and AI.  Let's dive into the transport in cities of the future.

GPS data

GPS technology gives developers the ability to monitor the vehicle position in real-time, and on top of that generate the data on parameters such as speed, distance, and travel time. Using this kind of  telemetry information , combined with fuel level, speed limits, traffic information, and the estimated time of arrival, the urban transportation passengers can be instantly alerted to ongoing, but also predicted delays and problems on route.

An advanced version of this system can also propose different routes, to avoid building up traffic in congested areas and reduce the average travel time of the passengers, making everyone happy.

Sensors and detectors

Installed along roads, such elements collect data on traffic volumes, vehicle speeds, and which lanes are being occupied. Sensors embedded in roads are used today by about 25% of smart city stakeholders in the United States (Otonomo study).

Additionally, the so-called agglomerative clustering algorithm helps to identify clusters of places or destinations.

Connected Vehicles (CV)

The latest generation of intelligent transportation systems works closely with the Internet of Things (IoT), specifically the  Internet of Vehicles(IoV). This allows for increased efficiency, mobility, and safety of autonomous cars.

 Wireless connectivity provides communication in indoor and outdoor environments . There is possible interaction:

•  Vehicle-to-vehicle (V2V),
•  Vehicle to Vehicle Sensor (V2S),
•  Vehicle-road infrastructure (V2R),
•  Vehicle-Internet (V2I).

In the latter case,  the vehicle couples with ITS infrastructure: traffic signs, traffic lights, and road sensors.

Digital twin (case study: Antwerp)

A  digital twin is a kind of bridge between the digital and physical worlds . It supports decision-makers in their complex decisions about the quality of life in the city, allowing them to budget even more effectively.

In the Belgian city of Antwerp a digital twin, a 3D digital replica of the city, was launched in 2018. The model  features real-time values from air quality and traffic sensors.

The city authorities can see exactly  what the concentration of CO2 emissions and noise levels are in the city center. They also notice to what extent limited car traffic in certain city areas affects traffic emissions.

Automated Highway Systems (AHS)

Connected vehicles have opened the way for further innovations. Automated highway systems will be among them. Fully autonomous cars will move along designated lanes. The flow of cars will be controlled by a central city system.

The new solution will  allow the causes of congestion on highways to be pinpointed and reduces the likelihood of collisions.

Traffic zone division

Traditional traffic zoning takes into account the social and economic factors of an area. Whereas today, this can be based on much better data downloaded in real-time from smartphones. You can see exactly where most vehicles are accumulating at any given time. These are not always "obvious" places, because, for instance, at 3 p.m. there may be heavier traffic on a small street near a popular corporation than on an exit road in the city center.

This modern categorization simplifies the city's complex road network, enabling  more efficient traffic planning without artificial division into administrative boundaries.

Possibilities vs. practice

There are some interesting findings from a study conducted in 2021 by the analytics firm Lead to Market. It aimed to determine how cities are using vehicle data to enhance urban life. Today, these are being used for:

•  alleviating bottlenecks in cities for business travelers and tourists (36%),
•  better management of roads and infrastructure (18%),
•  spatial and urban planning (18%),
•  managing accident scenes (14%),
•  improving parking (6%),
•  mitigating environmental impacts (2%).

Surprisingly, however, only 22% of respondents use vehicle data for real-time daily traffic management. What could be the reason behind this? Ben Wolkow, CEO of Otonomo, points to one main reason: data dispersion. Today, it comes from a variety of sources. Meanwhile:  for connected vehicle data to power smart city development in a meaningful way, they need to shift to a single connected data source .

It's good to know that  data from connected vehicles currently account for less than one-tenth of smart city analysis. But experts agree that this will be changing in favor of new solutions.

Technology that shapes the city

Vehicles are becoming increasingly intelligent and connected. Hardware, software, and sensors can now be fully integrated into the digital infrastructure. On top of that, full communication between vehicles and sensors on and off the road is made possible. Wireless connectivity,  AI , edge computing, and IoT are supporting predictive and analytical processes in larger metropolitan areas.

The biggest challenge, however, is the skillful use of data and its uninterrupted retrieval. It is, therefore, crucial  to find a partner with whom you can co-develop, e.g. some reliable traffic analysis software.

 There are many indications that the future lies in technology. Specifically, it belongs to connected and autonomous vehicles(CAVs), which, combined with 5G, AI, and machine learning, will form the backbone of the smart cities of the future. How will data from vehicles revolutionize city life as we've known it so far?

Data is "fuel" for the modern, smart cities

The UN estimates that  by 2050, about 68 percent of the global population will live in urban areas. This raises challenges that we are already trying to address as a society.

Technology will significantly support  connected, secure, and intelligent vehicle communication using vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) protocols. All of this is intended to promote better management of city transport, fewer delays, and environmental protection.

This is not just empty talk, because  in the next few years 75% of all cars will be connected to the internet, generating massive amounts of data. One could even say that data will be a kind of "fuel" for  mobility in the modern city . It is worth tapping into this potential. There is much to suggest that cities and municipalities will find that such innovative traffic management, routing, and congestion reduction will translate into better accessibility and increased safety.  In this way, many potential crises related to overpopulation and excess of cars in agglomerations will be counteracted.

What can smart cities use connected car data for?

Traffic estimation and prediction systems

Data from connected cars, in conjunction with Internet of Things (IoT) sensor networks, will help forecast traffic volumes. Alerts about traffic congestion and road conditions can also be released based on this.

Parking and signalization management

High-Performance Computing and high-speed transmission platforms with industrial AI /5G/  edge computing technologies help, among other things, to efficiently control traffic lights and identify parking spaces,  reducing the vehicle’s circling time in search of a space and fuel being wasted.

Responding to accidents and collisions

Real-time processed data can also be used  to save the lives and health of city traffic users. Based on  data from connected cars , accident detection systems can determine what action needs to be taken (repair, call an ambulance, block traffic). In addition, GPS coordinates can be sent immediately to emergency services, without delays or telephone miscommunication.

Such solutions are already being used by European warning systems, with the recently launched eCall system being one example. It works in vehicles across the EU and, in the case of a serious accident, will automatically connect to the nearest emergency network, allowing data (e.g. exact location, time of the accident, vehicle registration number, and direction of travel) to be transmitted, and then dial the free 112 emergency number. This enables the emergency services to assess the situation and take appropriate action. In case of eCall failure, a warning is displayed.

Reducing emissions

Less or more sustainable car traffic  equals less harmful emissions into the atmosphere. Besides, data-driven simulations enable short- and long-term planning, which is essential for low-carbon strategies.

Improved throughput and reduced travel time

Research clearly shows that connected and automated vehicles add to the comfort of driving.  The more such cars on the streets, the better the road capacity on highways.

As this happens, the travel time also decreases. By a specific amount, about 17-20 percent. No congestion means that fewer minutes have to be spent in traffic jams. Of course, this generates savings (less fuel consumption), and also for the environment (lower emissions).

Traffic management (case studies: Hangzhou and Tallinn)

Intelligent traffic management systems (ITS) today benefit from  AI . This is apparent in the Chinese city of Hangzhou, which prior to the technology-transportation revolution ranked fifth among the most congested cities in the Middle Kingdom.

 Data from connected vehicles there helps to efficiently manage traffic and reduce congestion in the city's most vulnerable districts. They also notify local authorities of traffic violations, such as running red lights. All this without investing in costly municipal infrastructure over a large area. Plus, built-in  vehicle telematics requires no maintenance, which also reduces operating costs.

A similar model was compiled in Estonia by Tallinn Transport Authority in conjunction with German software company PTV Group. A continuously updated map illustrates, among other things, the road network and traffic frequency in the city.

Predictive maintenance in public transport

 Estimated downtime costs for high-utilization fleets, such as buses and trucks, range from     $448 to $760 daily    . Just realize the problem of one bus breaking down in a city. All of a sudden, you find that delays affect not just one line, but many. Chaos is created and there are stoppages.
Fortunately, with the trend to equip more and more vehicles with telematics systems, predictive maintenance will be easier to implement.  This will significantly increase the usability and safety of networked buses . Meanwhile, maintenance time and costs will drop.

Creating smart cities that are ahead of their time

 Connected vehicle data not only make smart cities much smarter, but when leveraged for real-time safety, emergency planning, and reducing congestion, it saves countless lives and enables a better, cleaner urban experience – said Ben Wolkow, CEO of Otonomo.

The  digitization of the automotive sector is accelerating the trend of smart and automated city traffic management.  A digital transport model can forecast and analyze the city’s mobility needs to improve urban planning.

 The car insurance industry is experiencing a real revolution today. Insurers are more and more carefully targeting their offers using AI and machine learning features. Such innovations significantly enhance business efficiency, eliminate the risk of accidents and their consequences, and enable adaptation to modern realities.

Changes are needed today

Approximately $25 billion is "frozen" with insurers annually due to problems such as fraud, claims adjustment, delays in service garages, etc. However, customers are not always happy with the insurance amounts they receive and the fact that they often have to accept undervalued rates. The reason for this is that due to limited data, it is difficult to accurately identify the culprit of the incident. It is also often the case that compensation is based on rates lower than the actual value of the damage.

 Insurers today need to be aware of the ecosystem in which they operate . Clients are becoming more demanding and, according to an IBM Institute for Business Value (IBV) study, 50 percent of them prefer tailor-made products based on individual quotes. The very model of cooperation between businesses is also changing, as relations between insurance providers and car manufacturers are growing tighter. All of this is linked to the fact that  cars are becoming increasingly autonomous, allowing them to more closely monitor traffic incidents and driver behavior as well as manage risk. Estimates suggest there will be as many as one trillion connected devices by 2025, and by 2030 there will be an increasing percentage of vehicles with automated features (ADAS).

No wonder there's an increasing buzz about changes in the car insurance industry. And these are changes based on technology. The use of  artificial intelligence , machine learning, and  advanced data analytics in the cloud will allow for seamless adaptation to market expectations.

 CASE STUDY

 SARA Assicurazioni and Automobile Club Italia are already encouraging drivers to install ADAS systems in exchange for a 20% discount on their insurance premiums. Indeed, it has been demonstrated that such systems can slash the rate of liability claims for personal injury by 4-25% and by 7-22% for property damage.

Why is this so important for insurers who want to face the reality?

Artificial intelligence-based pricing models provide a significant reduction in the time needed to introduce new offerings and to make optimal decisions. The risk of being mispriced is also lowered, as is the time it takes to launch insurance products.

The new  AI-based insurance reality is happening as we speak. The digital-first companies like Lemonade, with their high flexibility in responding to market changes, are showing customers what solutions are feasible.  In doing so, they put pressure on those companies that still hesitate to test new models.

Areas of change in car insurance due to AI

Artificial intelligence and related technologies are having a huge impact on many aspects of  the insurance industry : quoting, underwriting, distribution, risk and claims management, and more.

Changes in insurance distribution

Artificial intelligence algorithms smoothly create risk profiles so that the time required to purchase a policy is reduced to minutes. Smart contracts based on blockchain instantly authenticate payments from an online account. At the same time, contract processing and payment verification is also vastly streamlined, reducing insurers' client acquisition cost.

Advanced risk assessment and reliable pricing

Traditionally, insurance premiums are determined using the "cost-plus" method. This includes an actuarial assessment of the risk premium, a component for direct and indirect costs, and a margin. Yet it has quite a few drawbacks.

One of them is the inability to easily account for non-technical price determinants, as well as the inability to react quickly to shifting market conditions.

How is risk calculated? For car insurance companies, the assessment refers to accidents, road crashes, breakdowns, theft, and fatalities.

These days, all these aspects can be controlled by leveraging AI, coupled with IoT data that provides real-time insights. Customized pricing of policies, for instance,  can take into account GPS device dataon a vehicle’s location, speed, and distance traveled. This way, you can see whether the vehicle spends most of its time in the driveway or if, conversely, it frequently travels on highways, particularly at excessive speeds.

In addition, insurance companies can use a host of other  sensor and camera data, as well as reports and documents from previous claims. Having all this information gathered, algorithms are able to reliably determine risk profiles.

 CASE STUDY

 Ant Financial, a Chinese company that offers an ecosystem of merged digital products and services, specializes in creating highly detailed customer profiles. Their technology is based on artificial intelligence algorithms that assign car insurance points to each customer, similarly to credit scoring. They take into account such detailed factors as lifestyle and habits. Based on this, the app shows an individual score, assigning a product that matches the specific policyholder.

An in-depth analysis of claims

The cooperation between an insurance company and its client is based on the premise that both parties are pursuing to avoid potential losses. Unfortunately, sometimes accidents, breakdowns or thefts occur and a claims process must be implemented. Artificial intelligence, integrated IoT data, and  telematics come in handy irrespective of the type of claims we are handling.

•  These technologies are suitable for, among other things, automatically generating not only damage information but also repair cost estimates.
•  Machine learning techniques can estimate the average cost of claims for various client segments.
•  Sending real-time alerts, in turn, enables the implementation of predictive maintenance.
•  Once an image has been uploaded, an extensive database of parts and prices can be created.

The drivers themselves gain control as they can carry out the process of registering the damage from A to Z:  take a photo, upload it to the insurer's platform and get an instant quote for the repair costs. From now on, they are no longer reliant on workshop quotes, which were often highly overestimated in line with the principle: "the insurer will pay anyway".

Fraud prevention

29 billion dollars in annual losses These are losses to auto insurers that occur due to fraud. Fraudsters want to scam a company out of insurance money based on illegally orchestrated events. How to prevent this? The answer is AI.

 Analyzed data retrieved from cameras and sensors can reconstruct the details of a car accident with high precision. So, having an accident timeline generated by artificial intelligence facilitates accident investigation and claims management.

 CASE STUDY

 An advanced AI-based incident reconstruction has been tested lately on 200,000 vehicles as part of a collaboration between Israel's Project Nexar and a Japanese insurance company.

Assistance in the event of accidents

According to data from the OECD, car accident fatalities could be reduced by 44 percent if emergency medical services had access to real-time information about the injuries of involved parties.
Still, real-time assistance has great potential not only for public services but also in the context of auto insurance.

By leveraging AI to perform this,     insurers can provide drivers with quick and semi-automated responses during collisions and accidents    . For example, a chatbot can instruct the driver on how to behave, how to call for help, or how to help fellow passengers. All this is essential in the context of saving lives. At the same time, it is a way of reducing the consequences of an accident.

Transparent decision making (client perspective)

New technologies offer solutions to many problems not only for insurers but also for clients. The latter often complain about discrimination and unfair, from their point of view, calculations of policies and compensation.

"Smart automated gatekeepers" are superior in multiple ways to the imperfect solutions of traditional models. This is because, based on a number of reliable parameters, they facilitate the creation of more authoritative and personalized pricing policies. Data-rich and automated risk and damage assessments pay off for consumers because they have decision-making power based on how their actions affect insurance coverage.

The opportunities and future of AI in car insurance

McKinsey's analysis says that across functions and use cases AI investments are worth $1.1 trillion in potential annual value for the insurance industry.

The direction of changes is outlined in two ways: first by increasingly connected and  software-equipped vehicles with more sensors. Second, by the changing analytical skills of insurers. Data-driven vehicles will certainly affect more reliable and real-time consistent repair costs and, consequently, claims payments. And when it comes to planning offers and understanding the client, AI is an enabler of change for personalized, real-time service (24/7 virtual assistance) and for creating flexible policies. All signs indicate that such "abstract" parameters as education or earnings will cease to play a major role in this regard.

As can be inferred from the diagram above, the greater the  impact of a given technology on an insurance company's business , the longer the time required for its implementation. Therefore, it is vital to consider the future on a macro scale, by planning the strategy not for 2 years, but for 10.

 The decisions you make today have a bearing on improving operational efficiency, minimizing costs, and opening up to individual client needs, which are becoming more and more coupled with digital technologies.

 Tesla has Autopilot, Cadillac has Super Cruise, and Audi uses Travel Assist. While there are many names, their functionality is essentially similar. ADAS(advanced driver-assistance systems) assists the driver while on the road and sets the path we need to take toward autonomous driving. And where does your brand rank in terms of vehicle automation?

Consumers’ Reports data shows that 92 percent of new cars have the ability to automate speed with adaptive cruise control, and 50 percent can control both steering and speed. Although we are still two levels away from a vehicle that will be fully controlled by algorithms (  see the infographic below ), which, according to independent experts, is unlikely to happen within the next 10 years (at least when it comes to traditional car traffic), ADAS systems are finding their way into new vehicles year after year, and drivers are slowly learning to use them wisely.

On the six-step scale of vehicle automation - starting at level 0, where the vehicle is not equipped with any driving technology, and ending at level 5 (fully self-driving vehicle) - we are now at level 3. ADAS systems, which are in a way the foundation for a fully automated vehicle, combine automatic driving, acceleration, and braking solutions under one roof.

However, in order for this trend to be adopted by the market and grow dynamically year by year, we need to focus on  functional software and the challenges facing the automotive industry .

The main threats facing automated driving support systems

1. The absence of a driver monitoring system

Well-designed for functionality and UX, ADAS can effectively reduce driver fatigue and stress during extended journeys. However, for this to happen it needs to be equipped with an effective driver monitoring system.

Why is this significant? With the transfer of some driving responsibility into the hands of advanced technology, the temptation to "mind their own business" can arise in the driver. And this often results in drivers scrolling through their social media feeds on their smartphones. When automating driving, it is important to involve the driver, who must be constantly aware that their presence is essential to driving.
Meanwhile, Consumer Reports, which surveyed dozens of such systems in vehicles from leading manufacturers, reports that just five of them: BMW, Ford, Tesla, GM and Subaru - have fitted ADAS with such technology.

According to William Wallace - safety policy manager at Consumer Reports, "The evidence is clear: if a car facilitates people’s distraction from the road, they will do it - with potentially fatal consequences. It's critical that active driving assistance systems have safety features that actually verify that drivers are paying attention and are ready to take action at all times. Otherwise, the safety risks of these systems may ultimately outweigh their benefits."

2. Lack of response to unexpected situations

According to the same institution, none of the systems tested reacted well to unforeseen situations on the road, such as construction, potholes, or dangerous objects on the roadway. Such deficiencies in functionality in current systems, therefore, create a potential risk of accidents, because even if the system guides the vehicle flawlessly along designated lanes (intermittent lane-keeping or sustained lane-keeping system) the vehicle will not warn the driver in time to take control of the car when it becomes necessary to readjust the route.

There are already existing solutions on the market that can effectively warn the driver of such occurrences, significantly increase driving comfort and "delegate" some tasks to intelligent software. These are definitely further elements on the list of things worth upgrading driving automation systems within the coming years.

3. Inadequate UX and non-intuitive user experience

All technological innovations at the beginning of their development breed resistance and misunderstanding. It's up to the manufacturer and the  companies developing software to support vehicle automation to create systems that are straightforward and user-friendly. Having simple controls, clear displays and transparent feedback on what the system does with the vehicle is an absolute "must-have" for any system. The driver needs to understand right from the outset in which situations the system should be used when to take control of the vehicle and what the automation has to offer.

4. Lack of consistency in symbols and terminology

Understanding the benefits and functionality of ADAS systems is certainly not made easier by the lack of market consistency. Each of the leading vehicle manufacturers uses different terminology and symbols for displaying warnings in vehicles. The buyer of a new vehicle does not know if a system named by Toyota offers the same benefits as a completely different named system available from Ford or BMW and how far the automation goes.

Sensory overload affects driver frustration, misunderstanding of automation, or outright resentment, and this is reflected in consumer purchasing decisions and, thus, in the development of systems themselves. It is challenging to track their impact on safety and driving convenience when the industry has not developed uniform naming and consistent labeling to help enforce the necessary safety features and components of such systems.

5. System errors

Automation systems in passenger cars are fairly new and still in development. It's natural that in the early stages they can make mistakes and sometimes draw the wrong conclusions from the behavior of drivers or neighboring vehicles. Unfortunately, mistakes - like the ones listed below - cause drivers to disable parts of the system or even all of it because they simply don't know how to deal with it.

•  Lane-keeping assists freaking out in poorer weather;
•  Steering stiffening and automatically slowing down when trying to cross the line;
•  Sudden acceleration or braking of a vehicle with active cruise control - such as during overtaking maneuvers or entering a curve on a highway exit or misreading signs on truck trailers.

 How to avoid such errors? The solution is to develop more accurate models that detect which lanes are affected by signs or traffic lights.

Vehicle automation cannot happen automatically

Considering the number of potential challenges and risks that automakers face when automating vehicles, it's clear that we're only at the beginning of the road to the widespread adoption of these technologies. This is a defining moment for their further development, which lays the foundation for further action.

On the one hand, drivers are already beginning to trust them, use them with greater frequency, and expect them in new car models. On the other hand, many of these systems still have the typical flaws and shortcomings of "infancy," which means that with their misunderstanding or overconfidence in their capabilities, driver frustration can result, or in extreme cases, accidents. The role of  automotive OEMs and software developers is to create solutions that are simple and intuitive and to listen to market feedback even more attentively than before. A gradual introduction of such solutions to the market, so that consumers have time to learn and grasp them, will certainly facilitate automation to a greater extent and ultimately the creation of fully automated vehicles. For now, the path leading to them is still long and bumpy.

 As technology is evolving in the car insurance and data-defined vehicles markets, the generational cross-section of people who use them is also undergoing transformation. Currently, two generations, in particular, are coming to the fore: Millennials and Gen Z. They are interested in service and product offerings that are as personalized and tailor-made as possible, rather than generic.  Adding to that the rapid development of vehicle connectivity, now is the perfect time for insurers to roll out and scale up their telematics products offer.

Based on research by Allison+Partners, consumers within the youngest generation view the car simply as yet another life-enhancing device. What's more, about 70% of Generation Z consumers don't have a driver's license, and 30% of this group have no intention or desire to get one. This makes them more interested in carpooling using an autonomous vehicle. More than 45 percent of respondents feel comfortable with this. Thus, "urban mobility" players such as Uber and Lyft, as also e-scooter providers and  on-demand car rental services are in demand . All of these are capable of filling the empty gap in the public transport infrastructure.

There is another interesting point.  Those who do decide to get a driver's license and buy a car on their own, however, face the hassle of costly insurance . The amounts are especially exorbitant for the least experienced drivers, who in fact pay more for their year of birth stamped on their ID cards. There is even talk of the "age tax" phenomenon. Interestingly, young age does not always go hand in hand with traffic violations or dangerous driving behavior.  Generations Y and Z are therefore advocating that     car insurance should be reassessed, with more emphasis on personalization    .

Telematics - technology that supports contemporary society's needs

Consumers today, particularly younger ones, expect cars to be as innovative as possible, as this directly translates into comfort and safety. Above all, personalized experiences, reliable connection, and comfort count. All this is a recipe for success in the  future of mobility as a service .

Individual online services should be consolidated into comprehensive mobility platforms. This will ensure that the user no longer has to switch between applications, and autonomous driving will generate new opportunities for innovative business models.

 Utilizing telematics data opens the door to improving customer experience, unlocking new revenue streams, and increasing market competitiveness.

 A modular and extensible telematics architecture provides tremendous opportunities for all pro-change agents.

This allows you to organize data handling, filter it and add missing information at various stages of processing. And it will not be an overstatement to say that in a technology-oriented information society, it is data that is the most valuable resource these days.

They can be used in all business processes, interacting with the customer experience and responding to their needs. Especially those who represent the younger generations of the future, namely Millennials and Gen Z.

Telematics platforms and the future of the automotive industry

The processed information is also used to train AI/ML models and to monitor system behavior. When you add to this the fact that they are extracted and included in real-time, you gain the added value of the rapid response. This then results in service satisfaction and sustained business performance.

On the other hand, driving data collected from various sources offer a full insight into what is actually happening on the road, how drivers behave, and what decisions they make while driving. Insurers benefit from this, but so do car manufacturers and companies that deal with  shared mobility in its broader sense.
All these advantages are seemingly speaking for themselves. And this is just the beginning because the future of telematics is looking very bright.  The results of IoT Analytics research indicate that by 2025 the total number of IoT devices will have exceeded 27 billion globally. For comparison, it is important to add that currently there are 1.06 billion passenger cars on the roads around the world. Specialists predict that in just these few years this value will increase by more than  400 million     connected vehicles    .

What will the end customer and the insurers themselves gain from applying telematics technology in automotive insurance?

Crash detection

The real-time feature of a telematics platform allows to detect accidents instantly and take proactive actions to mitigate the damages. Car location and sensor data can be used to trigger the crash alerts, coordinate emergency services dispatch, and reconstruct the crash timeline.

Such solutions are already being implemented, for example at IBM. Their Telematics Hub  enables the management of crash and accident data in real-time and with a low probability of error. The tool can distinguish a false event from a real one, generate incident reports, and evaluate driver behavior.

Roadside assistance

According to Highway England, there are over 224,000 car breakdowns a year on England's busiest roads. That's an average of 25 cars per hour. In contrast, in another Anglo-Saxon country, the U.S., there are 1.76 million calls for roadside assistance per year.

Roadside assistance is an optional add-on to drivers' personal car insurance. It’s a popular service among the drivers but it needs to be further developed which requires leveraging real-time data processing. Those insurance providers that offer remote service or that minimize time spent on the side of the road are gaining a competitive advantage.

Monitoring vehicle activity allows pinpointing its location in case of an emergency. Once notified of the breakdown, assistance can be dispatched to the customer position, and the nearest available replacement vehicle can be booked.

UBI & BBI

Behavior-based (pay-how-you-drive) and usage-based insurance - UBI - (pay-as-you-drive) are  the future of car insurance programs . Together with value-added services like automated crash detection or roadside assistance, they will determine the competitiveness and market share of insurance companies.
Handling large volumes of real-time data from every telematics device, like connected cars, mobile apps, and black boxes to extract crucial information and offer insights to customers, requires a robust and scalable telematics platform.

Experts point out many advantages of UBI schemes over the conventional solutions offered so far. The most important of these are:

•  Potential discounts.
•  The authorities and insurance claims adjusters have facilitated accident investigations.
•  Drivers become motivated to improve their performance and eliminate risky driving behaviors and unsafe habits.
•  Enhancing customer loyalty.
•  Providing personalized, value-added services to insurance plans to serve customer interests more effectively.

Stolen vehicle recovery

The demand for targeted technologies for vehicle tracking and recovery comes in handy for insurance companies, which face the problem of issuing sizable amounts of compensation for stolen vehicles on a daily basis.

It's not true that younger generations are fickle and unwilling to take out insurance. Generation Z consumers and Millenials accounted for 39% of consumers buying auto insurance in 2018. This figure is increasing year on year and applies not only to compulsory insurance but also to additional plans. The problem is that in many cases theft insurance offers, if there are any, are based on statistical indicators rather than actual data, for instance, the high crime rate of this type in a given area. Besides, customers are often dissatisfied with amounts based on market values that are lower than expected.

So here, too, data-driven individualization is needed, and that's what telematics provides.
For instance, by gathering data about customer behavior, insurers can build driver profiles that allow them to set up alerts that are triggered by unusual or suspicious behavior. Another thing is real-time vehicle tracking. The alarm service can be activated on-demand or automatically, and the car establishes a connection to the operations center. It is also possible to document theft. Information detected by the vehicle is collected and then exported and made available for viewing by the appropriate people.

Telematics technology is an answer to a need, but also a challenge

The Millennials and Generation Z expect a holistic customer experience. Digital offerings must bring together a variety of products designed to make life easier and accommodate each individual's consumer personality.

This is exactly the task facing telematics today, which is not just an incomprehensible and distant technology. It is essentially something that allows you to adapt to society's changing service and experience-related expectations.

However, the new expectations of shared mobility, autonomous vehicles, and  personalized data insurance offers are linked to new sacrifices that end customers must also be prepared to make. These include, for example, the need to share more and more data. Yet, the younger generations are already declaring their readiness.  According to the Majesco survey, almost half of generation Z are also willing to share data if they see value in doing so. Questions in the survey also referred to the car and driver data-based insurance industry.

There are also massive challenges for insurers themselves, where data processing is still only at an initial stage. The technological capabilities of individual insurance companies need to be continuously developed. Ideally, driving data, and the software used to collect and process it, should not be scattered but planned holistically. This ultimately leads to the conscious use of telematics and to better management of situations requiring insurance payouts.

 Grape Up helps you realize the potential of telematics by applying the automotive and insurance industry expertise to create scalable, cloud-native solutions.

In my life, I had an opportunity to work in a team that maintains the Api Gateway System. The creation of this system began more than 15 years ago, so it is quite a long time ago considering the rate of technology changing. The system was updated to Java 8, developed on a light-way server which is Tomcat Apache, and contained various tests: integration, performance, end-to-end, and unit test. Although the gateway was maintained with diligence, it is obvious that its core contains a lot of requests processing implementation like routing, modifying headers, converting request payload, which nowadays can be delivered by a framework like Spring Cloud Gateway. In this article, I am going to show the benefits of the above-mentioned framework.

The major benefits, which are delivered by Spring Cloud Gateway:

• support to reactive programming model: reactive http endpoint, reactive web socket
• configuring request processing (routes, filters, predicates) by java code or yet another markup language (YAML)
• dynamic reloading of configuration without restarting the server (integration with Spring Cloud Config Server)
• support for SSL
• actuator Api
• integration gateway to Service Discovery mechanism
• load-balancing mechanisms
• rate-limiting (throttling) mechanisms
• circuit breakers mechanism
• integration with Oauth2 due to providing security features

Those above-mentioned features have an enormous impact on the speed and easiness of creating an Api gateway system. In this article, I am going to describe a couple of those features.

Due to the software, the world is the world of practice and systems cannot work only in theory, I decided to create a lab environment to prove the practical value of the Cloud Spring Gateway Ecosystem. Below I put an architecture of the lab environment:

Building elements of Spring Cloud Gateway

The first feature of Spring Cloud Gateway I am going to describe is a configuration of request processing. It can be considered the heart of the gateway. It is one of the major parts and responsibilities. As I mentioned earlier this logic can be created by java code or by YAML files. Below I add an example configuration in YAML and Java code way. Basic building blocks used to create processing logic are:

• Predicates – match requests based on their feature (path, hostname, headers, cookies, query)
• Filters – process and modify requests in a variety of ways. Can be divided depending on their purpose:
• gateway filter - modify the incoming http request or outgoing http response
• global filter - special filters applying to all routes so long as some conditions are fulfilled

Details about different implementations of getaway building components can be found in docs: https://cloud.spring.io/spring-cloud-gateway/reference/html/ .

Example of configuring route in Java DSL:

Configuration same route with YAML:

Spring Cloud Config Server integrated with Gateway

Someone might not be a huge fan of YAML language but using it here may have a big advantage in this case. It is possible to store configuration files in Spring Cloud Config Server and once configuration changes it can be reloaded dynamically. To do this process we need to use the Actuator Api endpoint.
Dynamic reloading of gateway configuration shows the picture below. The first four steps show request processing consistent with the current configuration. The gateway passes requests from the client to the “employees/v1” endpoint of the PeopleOps microservice (step 2). Then gateway passes the response back from the PeopleOps microservice to the client app (step 4). The next step is updating the configuration. Once Config Server uses git repository to store configuration, updating means committing recent changes made in the application.yaml file (step 5 in the picture). After pushing new commits to the repo is necessary to send a GET request on the proper actuator endpoint (step 6). These two steps are enough so that client requests are passed to a new endpoint in PeopleOps Microservice (steps 7,8,9,10).

Reactive web flow in Api Gateway

As the documentation said Spring Cloud Gateway is built on top of Spring Web Flux. Reactive programming gains popularity among Java developers so Spring Gateway offers to create fully reactive applications. In my lab, I created Controller in a Marketing microservice which generates article data repetitively every 4 seconds. The browser observes this stream of requests. The picture below shows that 6 chunks of data were received in 24 seconds.

I do not dive into reactive programming style deeply, there are a lot of articles about the benefits and differences between reactive and other programming styles. I just put the implementation of a simple reactive endpoint in the Marketing microservice below. It is accessible on GitHub too: https://github.com/chrrono/Spring-Cloud-Gateway-lab/blob/master/Marketing/src/main/java/com/grapeup/reactive/marketing/MarketingApplication.java

Rate limiting possibilities of Gateway

The next feature of Spring Cloud Gateway is the implementation of rate-limiting (throttling) mechanisms. This mechanism was designed to protect gateways from harmful traffic. One of the examples might be distributed denial-of-service (DDoS) attack. It consists of creating an enormous number of requests per second which the system cannot handle.
The filtering of requests may be based on the user principles, special fields in headers, or other rules. In production environments, mostly several gateways instance up and running but for Spring Cloud Gateway framework is not an obstacle, because it uses Redis to store information about the number of requests per key. All instances are connected to one Redis instance so throttling can work correctly in a multi-instances environment.
Due to prove the advantages of this functionality I configured rate-limiting in Gateway in the lab environment and created an end-to-end test, which can be described in the picture below.

The parameters configured for throttling are as follows: DefaultReplenishRate = 4, DefaultBurstCapacity = 8. It means getaways allow 4 Transactions (Request) per second (TPS) for the concrete key. The key in my example is the header value of “Host” field, which means that the first and second clients have a limit of 4TPS separately. If the limit is exceeded, the gateway replies by http response with 429 http code. Because of that, all requests from the first client are passed to the production service, but for the second client only half of the requests are passed to the production service by the gateway, and another half are replied to the client immediately with 429 Http Code.

If someone is interested in how I test it using Rest Assured, JUnit, and Executor Service in Java test is accessible here: https://github.com/chrrono/Spring-Cloud-Gateway-lab/blob/master/Gateway/src/test/java/com/grapeup/gateway/demo/GatewayApplicationTests.java

Service Discovery

The next integration subject concerns the service discovery mechanism. Service discovery is a service registry. Microservice starting registers itself to Service Discovery and other applications may use its entry to find and communicate with this microservice. Integration Spring Cloud Gateway with Eureka service discovery is simple. Without the creation of any configuration regarding request processing, requests can be passed from the gateway to a specific microservice and its concrete endpoint.

The below Picture shows all registering applications from my lab architecture created due to a practical test of Spring Cloud Gateway. “Production” microservice has one entry for two instances. It is a special configuration, which enables load balancing by a gateway.

Circuit Breaker mention

The circuit breaker is a pattern that is used in case of failure connected to a specific microservice. All we need is to define Spring Gateway fallback procedures. Once the connection breaks down, the request will be forwarded to a new route. The circuit breaker offers more possibilities, for example, special action in case of network delays and it can be configured in the gateway.

Experiment on your own

I encourage you to conduct your own tests or develop a system that I build, in your own direction. Below, there are two links to GitHub repositories:

1. https://github.com/chrrono/config-for-Config-server (Repo for keep configuration for Spring Cloud Config Server)
2. https://github.com/chrrono/Spring-Cloud-Gateway-lab (All microservices code and docker-compose configuration)

To establish a local environment in a straightforward way, I created a docker-compose configuration. This is a link for the docker-compose.yml file: https://github.com/chrrono/Spring-Cloud-Gateway-lab/blob/master/docker-compose.yml

All you need to do is install a docker on your machine. I used Docker Desktop on my Windows machine. After executing the “docker-compose up” command in the proper location you should see all servers up and running:

To conduct some tests I use the Postman application, Google Chrome, and my end-to-end tests (Rest Assured, JUnit, and Executor Service in Java). Do with this code all you want and allow yourself to have only one limitation: your imagination 😊

Summary

Spring Cloud Gateway is a huge topic, undoubtedly. In this article, I focused on showing some basic building components and the overall intention of gateway and interaction with others spring cloud services. I hope readers appreciate the possibilities and care by describing the framework. If someone has an interest in exploring Spring Cloud Gateway on your own, I added links to a repo, which can be used as a template project for your explorations.

 Insurance companies, especially those focused on life and car insurance, in their offers are placing more and more emphasis on big data analytics and driving behavior-based propositions. We should expect that this trend will only gain ground in the future. And this raises further questions. For instance, what should be taken into account when choosing a technological partner for insurance-technology-vehicle cooperation?

Challenges in selecting a technology partner

The potential of  telematics insurance programs encourages auto insurers to move from traditional car insurance and build a competitive advantage on collected data.

No wonder technology partners are sought to support and develop increasingly innovative projects. Such synergistic collaboration brings tangible benefits to both parties.

As we explained in the article     How to enable data-driven innovation for the mobility insurance   , the right technology partner will ensure:

•  data security;
•  cloud and IoT technology selection;
•  the reliability and scalability of the proposed solutions.

Finding such a partner, on the other hand, is not easy, because it must be a company that efficiently navigates in as many as three areas:  AI/cloud technology, automotive, and insurance . You need a team of specialists who operate naturally in the  software-defined vehicle ecosystem , and who are familiar with the characteristics of the P&C insurance market and the challenges faced by insurance clients.

Aim for the cloud. The relevance of AI and data collection and analytics technologies

Information is the most important asset of the 21st century. The global data collection market in 2021 was valued at $1.66 billion.  No service based on the Internet of Things and AI could operate without a space to collect and analyze data.  Therefore,     the ideal insurance industry partner must deliver proprietary and field-tested cloud solutions   . And preferably those that are dependable. Cloud services offered these days by insurance partners include:

•  cloud R&D,
•  cloud and edge computing,
•  system integration,
•  software engineering,
•  cloud platforms development.

Connectivity between the edge device and the cloud must be stable and fast. Mobility devices often operate in limited connectivity conditions, therefore car insurance businesses should leverage multiple methods to ensure an uninterrupted connection. Dynamic switching of cellular, satellite, and Wi-Fi communications combined with globally distributed cloud infrastructure results in reliable transmission and low latency.

A secure cloud platform is capable of handling an increasing number of connected devices and providing them all with the required APIs while maintaining high observability.

As a result, the data collected is precise, valid,  and reliable . They provide full insight into what is happening on the road, allowing you to better develop insurance quotes. No smart  data-driven automation is possible without it.

Data quality, on the other hand, also depends on the technologies implemented inside the vehicle ( which we will discuss further below) and on all intermediate devices, such as the smartphone.  The capabilities of a potential technology partner must therefore reach far beyond basic IT skills and most common technologies.

Telemetry data collection

Obviously, data acquisition and collection is not enough, because information about what is happening on the road, usage and operation of components in itself is just a "record on paper". But to make such a project a reality, you still need to implement  advanced analytical tools and telematics solutions.
Real-time data streaming from telematics devices, mobile apps, and  connected car systems gives access to driving data, driver behavior analysis, and car status.  It enables companies to     provide insurance policies based on customer driving habits    .

Distributed AI

AI models are an integral part of modern vehicles. They predict front and rear collision, control damping of the suspension based on the road ahead, recognize road signs, or lanes.  Modern infotainment applications suggest routes and settings depending on driver behavior and driving conditions.

Empowering the automotive industry to build software-defined vehicles. Automotive aspect

 Today it is necessary to take into consideration a strategy towards modern, software-defined vehicles. According to Daimler AG, this can be expressed by the letters “CASE”:

•     C    onnected.
•     A    utonomous
•     S    hared.
•     E    lectric.

This idea means the major focus is going to be put on making the cars seamlessly connected to the cloud, support or advancements in autonomous driving based on electric power.
Digitalization and evolution of the computer hardware caused a natural evolution of the vehicle. New SoC’s (System on a Chip, integrated board containing CPU, memory, and peripherals) are multipurpose and powerful enough to handle not just a single task but multiple, simultaneously. It would not be an exaggeration to say that the cars of the future are smart spaces that combine external solutions (e.g. cloud computing, 5G) with components that work internally (IoT sensors).  Technology solution providers must therefore work in two directions, understanding the specifics of both these ecosystems. Today, they cannot be separated.

The partner must be able to operate at the intersection of cloud technologies, AI and telemetry data collection.  Ideally, they should know how these technologies can be practically used in the car. Such a service provider should also be aware of the so-called bottlenecks and potential discrepancies between the actual state and the results of the analysis. This knowledge comes from experience and implementation of complex software-defined vehicle projects.

Enabling data-driven innovation for mobility insurance. Insurance context

There are companies on the market that are banking on the innovative combination of automotive and automation. Although  you have to separate the demand of OEMs and drivers from the demand of the insurance industry.

It's vital that the technology partner chosen by an insurance company is aware of this. This, naturally, involves experience supported by a portfolio for similar clients and specific industry know-how.  The right partner will understand the insurer's expectations and correctly define their needs, combining them with the capabilities of a     software-defined vehicle    .
From an insurer's standpoint, the key solutions will be the following:

•     Roadside assistance    . For accurately determining the location of an emergency (this is important when establishing the details of an incident with conflicting stakeholders’ versions).
•     Crash detection.    To take proactive measures geared toward mitigating the consequences.
•     UBI and BBI.    The data gathered from mobile devices, plug-in dongles, or the vehicle embedded onboard systems can be processed and used to build risk profiles and tailored policies based on customers’ driving styles and patterns.

Technology and safety combined

The future of  technology-based insurance policies is just around the corner. Simplified roadside assistance, drive safety support, stolen vehicle identification, personalized driving feedback, or crash detection- all of these enhance service delivery, benefit customers, and increase profitability in the insurance industry.

Once again, it is worth highlighting that the real challenge, as well as opportunity, is  to choose a partner that can handle different, yet consistent, areas of expertise.

 If you also want to develop data-driven innovation in your insurance company, contact GrapeUp. Browse our portfolio of     automo    tive & insurance projects .

 Driving a car must evoke certain emotions and associations. Without them, a vehicle loses its soul, becomes a machine like any other and it is extremely hard for it to win popularity in a market filled to the brim. For years, brands have been striving to build their individual character and stand out with features such as unique design, performance, safety, or high quality of workmanship. With the proliferation of digital technologies, there is now one more element in the OEMs' toolkit: mood-building. From now on, drivers themselves can decide how they want to feel at a given moment. It's time for mood focused car enhancement .  Digital technology will allow them to attain this state.

Up until now, remarkable driving sensations have typically been achieved by manufacturers through smooth driving, luxurious interior design or high-end sound systems. With modern technology, all of these elements can be combined into one  seamless, sensory experience . In the  vehicles of the future , the installed software will enable the creation of holistic experiences in which different senses are involved, and the driver's experience addresses sensations at both the functional level of the vehicle and the emotional level. Sound, color, scents, temperature, mood lighting, or tactile experiences (such as a massaging seat for the driver) can all create a one-of-a-kind experience that would distinguish the brand and offer the driver something that other manufacturers won't be able to give.

This suggests that sensor technology will become an important distinguishing mark in the user experience and will allow brands to more effectively influence purchase decisions and build consumer loyalty to a particular brand. According to PwC research, 86 percent of buyers are willing to pay more for a better customer experience.

Contextualizing the vehicle according to driving time, who is driving, or what mood they are in is already emerging as a trend set by major car brands.

Just as we approach the personalization of our own cell phones or computer accounts, we are already beginning to approach the personalization and contextualization of our own vehicles. As the implementations outlined below show, you can already see real-life examples of this today.
Manufacturers are using  cloud solutions and  AI not only to create a new vehicle functionality but also to induce us into a specific mood to make driving more enjoyable.

BMV My Modes use case

A whole new dimension of personalization and driving experience has recently been ventured by the BMW brand. With its  BMW iX model, it is promoting a solution called  "My Modes" . It features different colors and layout of the infotainment system with a curved display and digital cockpit. The user, depending on their mood, can change the color and sound theme (BMW IconicSounds Electric) in their vehicle.

Two popular modes are worth examining, namely Expressive and Relax. The former focuses on an active driving experience. Abstract patterns and vibrant colors stimulate action, inspire, and broaden thought paths. The experience is enhanced by interior audio that reflects the context of where you are at a given moment.

The Relax mode, as the name suggests, is designed to promote tranquility and well-being. The images displayed on the screens are inspired by nature and evoke associations of bliss and harmony. This is accompanied by discrete and serene sounds in the background.

  https://youtu.be/vg6B0FY3mc4?t=266

Ford Mindfulness concept: Attention (to) safety

Mindfulness. A keyword in automotive safety in the broadest sense, but also - increasingly - in vehicle design. Focusing attention on the present and on real needs is becoming the status quo. This approach to on-board technology helps create electrified and autonomous vehicles where the driver and passengers can travel safely and pleasantly, being present in the moment. This is being developed by the Ford brand with the  Mindfulness Concept Car.

According to Mark Higbie, senior advisor, Ford Motor Company, who helped introduce mindfulness into the Ford workplace:  A car by itself is not mindful. But how a car is used and the behaviors that it supports, can be. Ford’s goal with this concept is to create experiences that encourage greater awareness. With unique features and embedded technologies, Ford is providing drivers and passengers with new ways to be mindful while in a Ford vehicle, anywhere along the road of life.

Features perfectly suited to your needs

The Mindfulness Concept Car is a vehicle that helps reduce distractions and stress, enhance travelers' well-being, and increase their level of sanitation. The latter is especially important given the pandemic reality.

 Hygienic = safe
The pilot-activated Unlock Purge air conditioning system is geared to give the cabin a shot of clean, fresh air even before you enter the car. A more hygienic environment inside the car is also guaranteed by UV-C light diodes, which stop viruses and germs from multiplying.

Clean air is facilitated by a premium filter that removes almost all dust, odors, smog, allergens and bacteria-sized particles. It's an option specifically designed with allergy sufferers in mind.

 The car that takes care of your health
Modern Ford cars prioritize individual driver characteristics, including what's going on in the driver's body that could potentially affect travel safety.

The Mindfulness Concept Car uses data from external measuring devices. These take real-time physiological data from the driver. Feedback on selected health parameters is then displayed on an in-car screen.

Additionally, an electrically activated driver seat provides a stimulating impact on breathing and heart rate.

 Relaxing"here and now"
Ford's new addition allows you to fully indulge in an experience of tranquility and harmony. Mood lighting combined with temperature climate control provides specific moods inside the cockpit, such as refreshing dawn, relaxing blue sky and starry night.

Mindful driving guides are also provided in the new car concept. For instance, when the car is parked, the driver is instructed in yoga-based mini exercises that help relax the body and mind. The Powernap function, on the other hand, comes in handy during breaks on long journeys: a reclining seat, neck support and soothing sounds help drivers to fall asleep in a less stressful environment between travel points.

Speaking of relaxation, it's also interesting to note that the adaptive air conditioning provides calming cool air and simulates deep breathing. This happens especially after a dangerous incident, such as emergency braking (which is also supported by smart technology).

 Personalized premium audio
The newly developed Ford's vehicle is a true host of new technologies to improve the existing driving experience. This applies, for instance, to the loudspeakers, including the B&O headrest speakers and the overhead speakers. Together they provide the finest possible listening experience.

The B&O Beosonic™ equalizer enables you to select sound spaces to suit your mood, such as: "Energetic", "Relaxed", "Warm". The other playlists, in turn, are tailored to fit a specific situation and location. A troublesome traffic jam? The car itself will turn on the calming tunes.

Vehicles tailored to the users' context and mood

Improving the driver's mood in the car is slowly becoming an equally important factor as safety, functionality or economy. Vehicle interiors will therefore be increasingly adapted to individual desires and moods (human context), but also to occasions and situations (driving environment context).

OEMs are already aware of this, and major automotive giants today are testing solutions that allude to almost a spa-like salon experience. They are doing so with no coincidence. Predictions from the consulting firm Walker say that customer experience will overtake product superiority and price, which so far have been the key differentiator between companies. Emotions and experience therefore have a direct impact on purchasing decisions and brand loyalty. This can be summed up by the phrase: through the senses to the mind.

 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.

 Introduction

This article covers basic concepts of web applications that are designed to be run in Cloud environment and are intended for software engineers who are not familiar with  Cloud Native development but work with other programming concepts/technologies. The article gives an overview of the basics from the perspective of concepts that are already known to non-cloud developers including mobile and desktop software engineers.

 Basic concepts

Let’s start with something simple. Let’s imagine that we want to write a web application that allows users to create an account, order the products and write reviews on them. The simplest way is to have our backend app as a single app combining UI and code. Alternatively, we may split it frontend and into the backend, which just provides API.

Let’s focus on the backend part. The whole communication between its components happens inside of a single app, on a code level. From the executable file perspective, our app is a monolithic piece of code: it’s a single file or package. Everything looks simple and clean: the code is split into several logical components, each component has its own layers. The possible overall architecture may look as follows:

But as we try to develop our app we'll quickly figure out that the above approach is not enough in the modern world and modern web environment. To understand what's wrong with the app architecture we need to figure out the key specificity of web apps compared to desktop or mobile apps. Let’s describe quite simple yet very important points. While being obvious to some (even non-web) developers the points are crucial for understanding essential flaws of our app while running in the modern server environment.

Desktop or mobile app runs on the user's device. This means that each user has their own app copy running independently. For web apps, we have the opposite situation. In a simplified way, in order to use our app user connects to a server and utilizes an app instance that runs on that server. So, for web apps, all users are using a single instance of the app. Well, in real-world examples it's not strictly a single instance in most cases because of scaling. But the key point here is that the number of users, in a particular moment of time is way greater than the number of app instances. In consequence, app error or crash has incomparably bigger user impact for web apps. I.e., when a desktop app crashes, only a single user is impacted. Moreover, since the app runs on their device they may just restart the app and continue using it. In case of a web app crash, thousands of users may be impacted. This brings us to two important requirements to consider.

1.  Reliability and testability  
    Since all the code is placed in a single (physical) app our changes to one component during development of the new features may impact any other existing app component. Hence, after implementing a single feature we have to retest the whole app. If we have some bug in our new code that leads to a crash, once the app crashes it becomes unavailable to all the users. Before we figure out the crash we have some downtime when users cannot use the app. Moreover to prevent further crashes we have to roll back to a previous app version. And if we delivered some fixes/updates along with the new feature we’ll lose those improvements.
2.  Scalability  
    Consider the number of users is increased during a short period. In case of our example app, this may happen due to, e.g., discounts or new attractive products coming in. It quickly turns out that one app instance running is not enough. We have too many requests and app “times out” requests it cannot handle. We may just increase the number of running instances of the app. Hence, each instance will independently handle user orders. But after a closer look, it turns out that we actually don’t need to scale the whole app. The only part of the app that needs to handle more requests is creating and storing orders for a particular product. The rest of the app doesn’t need to be scaled. Scaling other components will result in unneeded memory growth. But since all the components are contained in a monolith (single binary) we can only scale all of them at once by launching new instances.

The other thing to consider is network latency which adds important limitations compared to mobile or desktop apps. Even though the UI layer itself runs directly in the browser (javascript), any heavy computation or CRUD operation requires http call. Since such network calls are relatively slow (compared to interactions between components in code) we should optimize the way we work with data and some server-side computations.

Let’s try to address the issues we described above.

Microservices

Let’s make a simple step and split our app into a set of smaller apps called microservices. The diagram below illustrates the general architecture of our app rethinks using microservices.

This helps us solve the problems of monolithic apps and has some additional advantages.

• Implementing a new feature (component) results in adding a new service or modifying the existing one. This reduces the complexity of the development and increases testability. If we have a critical bug we will simply disable that service while the other app parts will still work (excluding the parts that require interaction with the disabled service) and contain any other changes/fixes not related to the new feature.

• When we need to scale the app we may do it only for a particular component. E.g., if a number of purchases increase we may increment the number of running instances of Order Service without touching other ones.

• Developers in a team can work fully independently while developing separate microservices. We’re also not limited by a single language. Each microservice may be written in a different language.

• Deployment becomes easier. We may update and deploy each microservice independently. Moreover, we can use different server/cloud environments for different microservices. Each service can use its own third-party dependency services like a database or message broker.

Besides its advantages, microservice architecture brings additional complexity that is driven by the nature of microservice per se: instead of a single big app, we now have multiple small applications that have to communicate with each other through a network environment.

In terms of desktop apps, we may bring up here the example of inter-process communication, or IPC. Imagine that a desktop app is split into several smaller apps, running independently on our machine. Instead of calling methods of different app modules within a single binary we now have multiple binaries. We have to design a protocol of communication between them (e.g., based on OS native IPC API), we have to consider the performance of such communication, and so on. There may be several instances of a single app running at the same time on our machine. So, we should find out a way to determine the location of each app within the host OS.

The described specificity is very similar to what we have with microservices. But instead of running on a single machine microservice apps run in a network which adds even more complexity. On the other hand, we may use already existing solutions, like http for communicating between services (which is how microservices communicate in most cases) and RESTful API on top of it.

The key thing to understand here is that all the basic approaches described below are introduced mainly to solve the complexity resulting from splitting a single app into multiple microservices.

Locating microservices

Each microservice that calls API of another microservice (often called client service) should know its location. In terms of calling REST API using http the location consists of address and port. We can hardcode the location of the callee in the caller configuration files or code. But the problem is that can be instantiated, restarted, or moved independently of each other. So, hardcoding is not a solution as if the callee service location is changed the caller will have to be restarted or even recompiled. Instead, we may use Service Registry pattern.

To put it simply, Service Registry is a separate application that holds a table that maps a service id to its location. Each service is registered in Service Registry on startup and deregistered on shutdown. When client service needs to discover another service it gets the location of that service from the registry. So, in this model, each microservice doesn’t know the concrete location of its callee services but just their ids. Hence, if a certain service changes its location after restart the registry is updated and its client services will be able to get this new location.

Service discovery using a Service registry may be done in two ways.

1. Client-side service discovery. Service gets the location of other services by directly querying the registry. Then calls discovered the service’s API by sending a request to that location. In this case, each service should know the location of the Service Registry. Thus, its address and port should be fixed.

2. Server-side service discovery. Service may send API call requests along with service id to a special service called Router. Router retrieves the actual location of the target service and forwards the request to it. In this case, each service should know the location of the Router.

Communicating with microservices

So, our application consists of microservices that communicate. Each has its own API. The client of our microservices (e.g., frontend or mobile app) should use that API. But such usage becomes complicated even for several microservices. Another example, in terms of desktop interprocess communication, imagines a set of service apps/daemons that manage the file system. Some may run constantly in the background, some may be launched when needed. Instead of knowing details related to each service, e.g., functionality/interface, the purpose of each service, whether or not it runs, we may use a single facade daemon, that will have a consistent interface for file system management and will internally know which service to call.

Referring back to our example with the e-shop app consider a mobile app that wants to use its API. We have 5 microservices, each has its own location. Remember also, that the location can be changed dynamically. So, our app will have to figure out to which services particular

requests should be sent. Moreover, the dynamically changing location makes it almost impossible to have a reliable way for our client mobile app to determine the address and port of each service.

The solution is similar to our previous example with IPC on the desktop. We may deploy one service at a fixed known location, that will accept all the requests from clients and forward each request to the appropriate microservice. Such a pattern is called API Gateway.

Below is the diagram demonstrating how our example microservices may look like using Gateway:

Additionally, this approach allows unifying communication protocol. That is, different services may use different protocols. E.g., some may use REST, some AMQP, and so on. With API Gateway these details are hidden from the client: the client just queries the Gateway using a single protocol (usually, but not necessarily REST) and then the Gateway translates those requests into the appropriate protocol a particular microservice uses.

Configuring microservices

When developing a desktop or mobile app we have several devices the app should run on during its lifecycle. First, it runs on the local device (either computer or mobile device/simulator in case of mobile app) of the developers who work on the app. Then it’s usually run on some dev device to perform unit tests as part of CI/CD. After that, it’s installed on a test device/machine for either manual or automated testing. Finally, after the app is released it is installed on users’ machines/devices. Each type of device

(local, dev, test, user) implies its own environment. For instance, a local app usually uses dev backend API that is connected to dev database. In the case of mobile apps, you may even develop using a simulator, that has its own specifics, like lack or limitation of certain system API. The backend for the app’s test environment has DB with a configuration that is very close to the one used for the release app. So, each environment requires a separate configuration for the app, e.g., server address, simulator specific settings, etc. With a microservices-based web app, we have a similar situation. Our microservices usually run in different environments. Typically they are dev, test, staging, and production. Hardcoding configuration is no option for our microservices, as we typically move the same app package from one environment to another without rebuilding it. So, it’s natural to have the configuration external to the app. At a minimum, we may specify a configuration set per each environment inside the app. While such an approach is good for desktop/mobile apps it has provides a limitation for a web app. We typically move the same app package/file from one environment to another without recompiling it. A better approach is to externalize our configuration. We may store configuration data in database or external files that are available to our microservices. Each microservice reads its configuration on startup. The additional benefit of such an approach is that when the configuration is updated the app may read it on the fly, without the need for rebuilding and/or redeploying it.

Choosing cloud environment

We have our app developed with a microservices approach. The important thing to consider is where would we run our microservices. We should choose the environment that allows us to take advantage of microservice architecture. For cloud solutions, there are two basic types of environment: Infrastructure as a Service, or IaaS, and Platform as a Service, or PaaS. Both have ready-to-use solutions and features that allow scalability, maintainability, reliability which require much effort to achieve on on-premises. and Each of them has advantages compared to traditional on-premises servers.

Summary

In this article, we’ve described key features of microservices architecture for the cloud-native environment. The advantages of microservices are:

- app scalability;

- reliability;

- faster and easier development

- better testability.

To fully take advantage of microservice architecture we should use IaaS or PasS cloud environment type.

 Retail stores and factories are being cloned for the virtual world, for familiarity and efficiency. Now it's time for automotive, which is more and more willing to use digital twin factory. This innovative technology perfectly bridges the real and virtual worlds. It is already happening now, for instance in BMW factories.

Digital twin and Industry 4.0

The fourth industrial revolution necessitates the use of advanced data-driven technologies. This includes digital twins. It's an idea that allows you to simulate products, services, and entire processes for creating more efficient and faster quality solutions.  By using video, images, diagrams or other data for advanced 3D mapping, a new virtual reality is created.

This concept is becoming increasingly common in various market sectors, including  automotive . Not only     individual vehicle parts   , but even entire factories are already being created in the digital space. The latter can be seen, for example, at BMW.

But it is also being used in many other sectors, not just the industry as such. For instance, tests are being carried out to use the technology for surgical treatment of patients with heart conditions - so digital twins would be used for the advanced replication and examination of internal organs. Besides, they would enable faster development of prototypes of even such machines as airplanes. Architects, by contrast, no longer have to rely solely on their imagination in such a scenario, but can use perfectly reproduced models of skyscrapers, accurate down to the nearest centimeter.

Digital twins enter factories now

Just imagine this scenario: the opening gate of a manufacturing plant. Coating a car door with paint. Workers, going from section to section, carrying out their jobs. Except that these are just very realistic simulations. And the workers are, in fact, only avatars. This is how the idea of the digital twin in the automotive industry can be summarized.  It's creating a separate, comprehensively perceived manufacturing process.

 The digital twin in the automotive industry includes a virtual replica of the entire car and its physical behavior, including software, electronics, mechanisms, etc. And it can additionally store all performance and sensor data in real-time, as well as configuration changes, service history, and warranty information.

Making the digital twin a reality at BMW

 This trend is already becoming widespread. For example, at the German BMW factory. The virtual three-dimensional replica of the vehicle factory used by the company is a space reproduced down to the smallest detail, which can be accessed using a screen or VR goggles. Why " dabble" in such technology at all? To save money, at least, among other things. Non-physical, virtual resources  allow you to test or improve assembly line parts without having to move or operate on heavy machinery.

Machine learning algorithms also help in managing robots. These, in a simulated version, can make various complex moves  to make the process as streamlined as possible. And all this without wasting energy on time-consuming tests. Besides, this way robots learn new ways of working.

Advanced software also simulates,e.g., the behavior of workers: their paths of movement and actions. By doing so, an attempt is made  to minimize possible ergonomic problems. Frank Bachmann, BMW's factory manager, says the time needed to plan the factory's operations has been reduced by at least  25 percent . Anyway, the changes are happening  as we speak , because even before the individual parts of the drive systems for electric vehicles leave the BMW plant, the entire production process is already finalized in the virtual version of the Regensburg factory.

The aforementioned benefits are such a boon for BMW that the company intends to develop more of this type of technology. Their soon-to-be-introduced twin factory model is expected to be a replica of the factory in Hungary, and subsequently, this will apply to other factories around the world.

Innovation driven by synergistic collaboration

BMW is an automotive giant that promotes and uses the virtual technology of tomorrow not alone, but with the right support from technology companies that are responsible for the software implementation. In the case of the German automotive brand, the partner is the chip company, Nvidia. It uses its proprietary Omniverse system, which offers the possibility to simulate the entire production process, taking into account even such physical factors as gravity.
Clearly, everything is to be conducted in the framework of photorealistic detail. This complex virtual environment allows for the creation of diverse 3D models. It's also innovative in the sense that Omniverse's open file standard is compatible with numerous computer-aided design packages. Richard Kerris, general manager of Omniverse at Nvidia, refers to the project as "one of the most complex simulations ever made".

But the solutions do not close at Invidia, and automotive companies can also choose from other offers of technological implementations. And there is every indication that there will be more and more of these offerings. For example, in November 2021, Amazon unveiled the  AWS IoT TwinMaker , a service that generates digital duplicates of real-world systems for business. An immersive 3D view of systems and operations enables optimizing efficiency, increasing production, and improving performance.  So does the  platform-as-a-service (PaaS) offering,     Azure Digital Twins   . It enables the creation of digitally based models of various environments such as buildings, factories, power grids, and even entire cities.

Use cases, in other words: how can the concept of digital twin be used in the automotive industry?

It may seem to some that creating digital twins in the automotive industry is unnecessary "gadgetry" or blind following of trends.

After all, why simulate the creation of a vehicle? Isn't it better to spend time, energy, and resources on improving what is already underway? Isn't it better to invest in the REAL production result? All of this is not so simple. Especially when you realize that this technology is not just about virtualizing the vehicle development stage.  The idea behind digital twin factories focuses not so much on the development of the cars themselves, but on the entire broad ecosystem. It is about creating and sustaining, in a controlled environment, the entire production environment:

•  logistics,
•  employees,
•  deployment of machinery,
•  chain value.

Ding Zhao, a Carnegie Mellon University professor specializing in artificial intelligence and digital simulations, argues that simulations are crucial to the industry. This is the case for two reasons. First, it's about simulating dangerous situations. Under "normal" circumstances, this is often simply impossible. Just as impossible is running machines for millions of cycles each time, only to collect the necessary data for analysis.

The simulation, therefore, takes into account the entire environment of the production process.  It is a comprehensive and all-encompassing view of the problem. A virtual answer to the question of real needs, and of real benefits. And these are numerous.

Prediction first

The digital twin gives people in charge of maintaining productivity in a factory an important "weapon" to fight against financial loss. It's called predictive maintenance. Predicting what's to come saves resources and allows us to better plan future production and sales activities.

This ranges from product testing, determining maintenance needs and line improvements, to turnover planning. For instance, different types of chassis can be tested in diverse weather conditions. In a virtual world, of course. What is more, such solutions can be tested right away by customers, who will thus immediately share their impressions of the product. So you get feedback even before the solution is released on the market.

OEMs can maintain a twin vehicle of each VIN and software number and can do  updates wirelessly (SOTA) or temporarily enable or disable some features.

In the simulation, for example, you can also pay attention to functionalities that drivers rarely use. If something doesn't work, you can back out of the idea, even before it is implemented.

In addition, it is also possible to configure the infrastructure of factories so that employees can be trained remotely without physically installing the equipment. This opens up further possibilities for the internationalization of a brand. In this way, a manufacturing company in the U.S. can train a new team in Japan even before the plant in the Land of Cherry Blossoms is completed.

Improving manufacturing capabilities

The technology described here yields huge savings not only in terms of money but also in terms of time. In the traditional automotive industry, companies have to focus too long on verifying new features or designs. And all because they have to wait for the production process to be completed.

The digital twin clears this hurdle.  You can easily test the impact of a new machine with new features and parameters for your production output. It's a fast, yet reliable way to verify the success and performance of an innovative project.

Effective data management

Virtual simulation technology allows for  reliable data analysis , both present, and past. All data, e.g. regarding stoppages or configuration changes, are collected in real-time. So you can see exactly when machine stoppages are likely to occur. And not only that.

As a result, people in decision-making positions can  plan uninterrupted production with minimal financial loss. And car dealers, having an insight into a vehicle's service history, know exactly what they are marketing.
Based on this, you can also better  anticipate customers' demand and improve customer satisfaction when using the car.

Importantly, the data collected is integrated and unified across several sources simultaneously. It is not a problem to  get insight into     performance data, driver behavior data    , and archived information on previous models.

Perfect finish

As you may be aware, the production of a new model may take even 5-6 years, therefore even a minor oversight may disturb the stability of a company, especially when it concerns the flagship and widely advertised model.  For image and financial reasons, it is particularly significant today that the product is competitive, reliable and perfectly developed.

What is the conclusion? Even a small omission can impair the stability of a company, especially when it involves its flagship and widely advertised model.  For image and financial reasons, what matters today is that the product is competitive, reliable and perfectly developed.

The digital twin, which allows design and simulation in a completely virtual environment, favors the creation of products perfect in every detail. High-performance rendering and visualization tools allow you to select from a wide variety of materials and textures. And nothing stands in the way of optimizing airflow or heat emission. Every detail will be planned.

Why use a digital twin?

There are many benefits when using a digital twin in automotive. A simulation of this type means:

•  an optimal design of the production process already at the digital copy stage, rather than on a "living organism".
•  saving time and money. By "getting ahead" of future production problems.
•  a better estimate of production line extension costs
•  an easier analysis of each stage of the production process for so-called "bottlenecks".
•  faster, more interactive communication between vehicle designers, stakeholders and end customers.
•  improved ergonomics at all workstations in the plant.
•  the determination of product behavior throughout the life cycle. Thus facilitating R&D work.
•  the ability to reuse proven models and quickly evaluate the impact of changes.
•  an option to integrate all data between the previous vehicle generation and the current vehicle design in a digital model.

Clearly, this is one of the most cost-effective data-driven manufacturing concepts today.

Digital Twin factory. No longer science fiction

The concept of digital twins in  the automotive industry is the future, not science fiction. Before long, every factory or building will have a digital counterpart, helping to better manage it.

The digital and real worlds will seamlessly intertwine. The convergence of physical and virtual versions offers the possibility of overcoming various challenges that are now commonplace in the automotive value chain.

The most powerful giants, with BMW at the forefront, know this.  Everything indicates that soon every manufacturer in the industry will have to consider investing in such solutions at some stage and to some extent. Anyway, from the company's point of view, it is not a sacrifice, but a chance to develop against the competition. And an opportunity to achieve numerous measurable benefits.

 Digitalization has changed the way we shop, work, learn and take care of our health or travel. Cars are no longer used just to get from A to B. They are jam-packed with technology that connects us to the world, enhances safety, prevents breakdowns, and even provides entertainment. With the rise of the Internet of Things and artificial intelligence, a vehicle is no longer understood solely in terms of its performance and sleek design. It has become software on wheels, a gateway to new worlds - not just physical, but also virtual. And if the nature of insurance itself is changing, then the company offering insurance must keep up with these changes as well. Insurance needs digital innovation, as much as any other market area.

These days customers are looking for customization, personalization, and understanding their needs on an almost organic level. Data and advanced analytics allow us to effectively satisfy these needs. Thanks to them, it is possible to fine-tune the offer, not so much for a specific group, but for a particular person - their habits, daily schedule, interests, health restrictions, or aesthetic preferences. And in the case described by us - a person's  driving style and commuting patterns .

If you think about it, the insurer has the perfect tool in their hands. If they can tap into the potential of the  software-defined vehicle and equip it with the right applications, there will be nearly zero chance of inaccurate insurance risk estimates. Data doesn't lie and shows a factual, not imaginary picture of a driver's driving style and behavior on the road.

While in the traditional insurance model pricing is static and data is collected offline and not aligned with the driver's actual preferences, new technologies such as the  cloud, the IoT, and AI allow for these limitations to be effectively lifted.

With them, an offering is created that competes in the marketplace, generates new revenue streams within the company, and builds customer loyalty.

Data-driven innovation - easier said than done. Or maybe not?

The transformation of a vehicle from a traditionally understood mechanical device into a "smartphone on four wheels," as Akio Toyoda once said about modern vehicles, takes time and will not happen overnight. But year by year it already happens, and as the new car models distributed by the big corporations show, this process is actually underway.

        Read our article on the latest trends in the automotive industry    

The so-called software-defined vehicle that we are developing with our clients at Grape Up is a vehicle that moves through an ecosystem of numerous variables, accessed by different players and technologies.
Clearly, one such provider can be - and should be - the insurer whose products have been tied to the automotive market invariably since 1897, when a certain Gilbert J. Loomis, a resident of Dayton, Ohio, first purchased an automotive liability insurance policy.

However, for insurance companies to play an integral role in the use of vehicle-generated data, the driver must receive a precisely functioning and secure service from which they will derive real benefits. Without building specific technical competencies and  software-defined vehicle knowledge , the insurer cannot achieve these goals.

Only by creating this type of business unit from scratch in-house, or by partnering with software companies, will they be able to compete with insurtech startups like, e.g. Lemonade, which builds their businesses from the ground up  based on AI and data analytics .

The right technology partner will take care of:

•  data security;
•  selection of cloud and IoT technologies;
•  and will ensure the reliability and scalability of the proposed solutions.

During this time, the insurer can focus on what they do best - developing insurance competencies and tweaking their offers.

How to choose the right technology partner?

Just as customers are looking for insurance that accommodates their driving and lifestyle, an insurance company should select a technology partner that has more than just technical skills to offer. After all, changing the model in which a traditional insurance company operates does not boil down to creating a digital sales channel on the Internet and launching a modern website. We are talking about a completely different scale of operations requiring the insurance company to be embedded in a completely new, rapidly developing environment.

Therefore they need a partner who naturally navigates the software-defined vehicle ecosystem, understands its specifics, and has experience in working with the automotive industry. Besides, it should be someone  knowledgeable about the specifics of the P&C insurance market and the challenges faced by the insurance client.

It is only at the intersection of these three areas: technology, automotive, and insurance, that competencies are built to effectively compete against modern insurtechs.

Like in the Japanese philosophy of ikigai, which explains how to find one's sense of purpose and give meaning to one's work, both companies can build valuable, useful solutions for users. They will bring satisfaction not only to customers but also to the insurance company, which will open a new revenue channel and meet the needs of the market.

 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.

July 2021, Porsche recalls 43 000 of its newest EVs: Taycan and Taycan Cross. Why? Due to software issues resulting in power loss. How could this have been prevented while reducing costs and fixing the defects in one go on all cars? The answer is short and comes from the mouths of everyone working in the automotive industry: Over-The-Air Upgrade.

Although hard to implement correctly, the cost of not having the ability to remotely upgrade software and firmware in the vehicle is huge. Today it’s not the question of „IF” and „WHEN”, (since the automotive industry has long known the answers to these questions), today it’s the question of „HOW”.

Upgrading a GPS or infotainment application is one thing, but upgrading the vehicle's firmware is another. And it does not matter whether it's a car, an e-scooter, or a smartphone. The principles are always the same. We will try to outline them in this article.

Let's start from the beginning - what are the core benefits of the over-the-air upgrade.

OTA allows for remote diagnosis. Initial diagnosis done remotely helps with better planning of repairs, as well as with predictive maintenance – both giving a better customer experience and reducing the cost for the OEMs, especially during the warranty period.

The upgrade can also happen on the production line while waiting for shipment. The vehicle always has the newest stable version of the firmware and software, reducing the amount of manual work required for the whole vehicle lifecycle.

The only part of the car life cycle where the Over-The-Air Upgrade is not really useful is aftersales.

Key benefits of implementing an over-the-air upgrade are:

• An ability to remain compliant with evolving industry standards through vehicle lifetime.
• It helps to reduce warranty and recall costs by reducing service center visits or help desk calls for the vehicle (it also works on the production line, while waiting for shipment).
• The vehicle always has the newest stable version of the firmware and software, reducing the amount of manual work required for the whole vehicle lifecycle.
• An ability to resolve issues remotely, so the customer doesn’t have to waste time traveling on-site.
• An ability to update multiple vehicles simultaneously, reducing time required to update the whole fleet.

SOTA - the most common implementation of over-the-air upgrade

SOTA is used widely by almost every OEM to update navigation systems (maps, POIs) and sometimes other infotainment applications, like voice assistance. As opposed to the firmware update, the failure of the software update is rarely critical to vehicle operations. It can result in inconvenience when due to update failure, the navigation system crashes or fails to display a map.

This is also the part that makes the customer experience bad if SOTA is done without due diligence because the software makes the infotainment appealing and responsive . And yet no one likes slow or difficult-to-use applications or services. Especially when they're intended to boost driving satisfaction.

Firmware over-the-air-upgrade is a different beast

With FOTA, we play a much more demanding game. That’s why it’s important to separate software updates from firmware updates.

First, it’s just easier for a developer to focus on his part of the job, the specific application. Secondly, the firmware part is riskier and more complex, and the update might not be required that often.

The complication comes partially from the idea of replacing the Operating System of the ECUSoC and partially from the criticality of the systems. Computers controlling engine operations, ESPTC, gearbox, or electronic chassis controller are required for safe and reliable operations of the vehicle.

Firmware Over-The-Air Update Failure in the update process, resulting in critical fault of this kind of subsystem, in most cases, makes the vehicle inoperable, beyond repair capabilities of regular users. The cost of restoring the vehicle to an operational state is fully on the manufacturer’s side. This is obviously the scenario that should be avoided at all costs.

Key requirements for implementation of (F)OTA successfully

• Automatic recovery corrupted updates

Firmware updates should be atomic. The whole process should be successful, or the system should automatically roll back to the previous/ existing version of the software. The problem does not have to be caused by a bug in the original image – the package can be corrupted in transit, or the transfer might be interrupted and result in a partial package being in the process.

• Internet connectivity consistency

Parts of the firmware being updated, especially ones regarding device to network connectivity, should never break away if the SoC is connected to the internet – otherwise, the next version might be never installed automatically. It’s important especially if the device does not have a way to notify the user about the problem or allow them to reconfigure the network settings.

• Code provenance, code identity, code compatibility and code integrity – security of the executed program

Firmware update in most cases regards critical systems . The wireless update is tempting, but it must be secure, especially regarding verifying the identity of authors of change and source of the update – as well as if the code was not replaced or altered during transit. If the edge device can cryptographically confirm code signs, it can be installed. Additionally, there should be a way for the update system to confirm if the package is built for that specific it’s being installed on.

• Secure communication medium for package transport

All channels used for the update should be secure. Ideally, it should be a mutual TLS, but even a regular secure TLS connection is sufficient as long as the whole path is secure (both local connection and in the cloud).

• [NICE-TO-HAVE] Sending OTA firmware updates in chunks and partial updates support

It’s easier to handle updates that are sent in chunks. When the connection is unstable, the whole download process does not have to be repeated. Additionally, if partial updates are supported, a small update takes less time to install and less bandwidth to transfer.

• [NICE-TO-HAVE] Separate base system layer from the installed software

If the application and data layer is not part of the firmware update, it’s easier to develop the applications, safely update the system without breaking the data, and securely update the system without breaking the applications. Combined with partial updates, it also helps with making updates faster.

Opposite to the chip flashing using a wired connection, the failure is not really an option – if the device cannot boot, even to some basic OS functions, it is bricked – unless you are an expert with specialistic hardware, it may be really hard to directly write new firmware to the chip to overwrite the faulty or broken version.

And what if a broken package is written to the device?

Does not matter if it was a human error, device issue, or just really bad luck – in the end, the important part is to make sure the user does not end up with a broken vehicle. The battle-tested solution for this problem is AB filesystems – or AB slots.

The idea is rather simple – system areas in storage are duplicated. Graphically speaking, there are two fully operational versions of the system being installed simultaneously on the single device, and there is a programmatical switch in the bootloader which selects the OS to start.

In regular operation, a single system, let’s call it “A”, is being continuously used while the other one, “B”, is the exact copy of the “A”, but works as a backup. If the “A” fails to start, the bootloader switches to the other version. During the update, the inactive partition is overwritten with the update packages – either whole partition or subset of files, depending on the type of update. If the update finishes and the checksum of the result is correct, as the last step, the bootloader configuration is changed to run from the “B” slot, and the device restarts.

As previously stated – if something fails, the bootloader, after a failed attempt, will switch back to the previous, working version. This makes this approach safe, allowing us to retry the upgrade process. Otherwise, the update is successful and there are two approaches:

• Leave the old version on the other partition and remain to boot from the slot selected after the update process.
• Copy the contents of the upgraded partition to the other slot t o have two copies of the same version .

The same approach is used in modern smartphones, and as a direct continuation, the same approach was selected for Android Automotive OS – which is a Google Android Open-Source Project (AOSP) implementation-specific for the automotive industry.

Currently, both Volvo (including, of course, Polestar) and General Motors use AAOS for their newest vehicles as an infotainment system. Being an open system, a lot of applications can be developed for cars from different OEMs and leverage the bigger, open market – plus of course, the code is open source, and a lot of work on things like upgrade system (OTA), application delivery, connection to subsystems (air conditioning, navigation, interior buttons) is already finished and can be reused.

Building using open and tested frameworks and code is just easier – and a proven way to update both application and system is an asset when starting from scratch with new infotainment firmware and software.