Just a few years ago, artificial intelligence stirred our imagination via the voice of Arnold Schwarzenegger from "Terminator" or agent Smith from "The Matrix". It wasn't long before the rebellious robots' film dialogue replaced the actual chats we have with Siri or Alexa over our morning cup of coffee. Nowadays, artificial intelligence is more and more boldly entering new areas of our lives. The automotive industry is one of those that are predicted to speed up in the coming years. By 2030, 95-98% of new vehicles are likely to use this technology.

    What will you learn from this article?  

•     How to use AI in the production process  
•     How AI helps drivers to drive safely and comfortably  
•     How to use AI in vehicle servicing  
•     What companies from the AI ​​industry should pay attention to if they want to introduce such innovations  
•     You will learn about interesting use cases of the major brands  

Looking at the application of AI in various industries, we can name five stages of implementation of such solutions. Today, companies from the Communication Technology (ICT) and Financial Services ("Matured Industries") sectors are taking the lead. Healthcare, Retail, Life Science ("Aspirational Industries") are following closely behind. Food & Beverages and Agriculture ("Strugglers") and companies from the Chemicals and Oil and Gas sectors ("Beginners") are bringing up the rear. The middle of the bunch is the domain of  Automotive and, partly related to it, Industrial Machinery.

Although these days we choose a car mainly for its engine or design, it is estimated that over the next ten years, its software will be an equally significant factor that will impact our purchasing decision.

AI will not only change the way we use our vehicles, but also how we select, design, and manufacture them. Even now, leading brands avail of this type of technology at every stage of the product life cycle - from production through use, to maintenance and aftermarket.

Let's have a closer look at  the benefits a vehicle manufacturing company can get when implementing AI in its operations.

Manufacturing - how AI improves production

1. You will be able to work out complex operations and streamline supply chains

An average passenger car consists of around 30,000 separate parts, which interestingly enough, are usually ordered from various manufacturers in different regions of the world. If, on top of that,  we add a complicated manufacturing process, increasingly difficult access to skilled workers and market dependencies, it becomes clear that potential delays or problems in the supply chain result in companies losing millions. Artificial intelligence can predict these complex interactions, automate processes, and prevent possible failures and mishaps

•  Artificial intelligence complements     Audi's    supply chain monitoring. When awarding contracts, it is verified that the partners meet the requirements set out in the company's internal quality code. In 2020, over 13,000 suppliers provided the Volkswagen Group with a self-assessment of their own sustainability performance. Audi only works with companies that successfully pass this audit.

2. More efficient production due to intelligent co-robots working with people

For years, companies from the automotive industry have been trying to find ways to enhance work on the production line and increase efficiency in areas where people would get tired easily or be exposed to danger. Industrial robots have been present in car factories for a long time, but only artificial intelligence has allowed us to introduce a new generation of devices and their work in direct contact with people. AI-controlled co-bots move materials, perform tests, and package products making production much more effective.

•     Hyundai Vest Exoskeleton (H-VEX)    became a part of Kia Motors’ manufacturing process in 2018. It provides wearable robots for assembly lines. AI in this example helps in the overall production while sensing the work of human employees and adjusting their motions to help them avoid injuries.

•     AVGs (Automated Guided Vehicles)    can move materials around plants by themselves. They can identify objects in their path and adjust their route. In 2018, an OTTO Motors device carried a load of 750 kilograms in this way!

3. Quality control acquires a completely new quality

The power of artificial intelligence lies not only in analyzing huge amounts of data but also in the ability to learn and draw conclusions. This fact can be used by finding weak points in production, controlling the quality of car bodies, metal or painted surfaces, and also by monitoring machine overload and predicting possible failures. In this way, companies can prevent defective products from leaving the factories and avoid possible production downtime.

•     Audi    uses computer vision to find small cracks in the sheet metal in the vehicles. Thus, even at the production stage, it reduces the risk of damaged parts leaving the factory.
•     Porsche    has developed "Sounce", a digital assistant,  using deep learning methods. AI is capable of reliably and accurately detecting noise, for example during endurance tests. This solution, in particular, takes the burden off development engineers who so far had to be present during such tests.  Acoustic testing based on Artificial Intelligence (AI) increases quality and reduces production costs.

4. AI will configure your dream vehicle

In a competitive and excessively abundant market, selling vehicles is very difficult. Brands are constantly competing in services and technologies that are to provide buyers with new experiences and facilitate the purchasing process. Manufacturers use artificial intelligence services not only at the stage of prototyping and modeling vehicles, but also at the end of the manufacturing process, when the vehicle is eventually sold. A well-designed configurator based on AI algorithms is often the final argument, by which the customer is convinced to buy their dream vehicle. Especially when we are talking about luxury cars.

•     The Porsche Car Configurator    is nothing more than a recommendation engine powered by artificial intelligence. The luxury car manufacturer created it to allow customers to choose a vehicle from billions of possible options. The configurator works using several million data and over 270 machine learning modules. Effect? The customer chooses the vehicle of their dreams based on customised recommendations.

Transportation - how AI facilitates driving vehicles

5. Artificial intelligence will provide assistance in an emergency

A dangerous situation on the road, vehicle in the blind spot, power steering on a slippery surface. All those situations can be supported by artificial intelligence, which will calculate the appropriate driving parameters or correct the way the driver behaves on the road. Instead of making automatic decisions - which are often emotion-imbued or lack experience - brands increasingly hand them over to machines, thus reducing the number of accidents and protecting people's lives.

•     Verizon Connect    solutions for fleet management allow you to send speed prompts to your drivers as soon as your vehicle's wipers are turned on. This lets the driver know that they have to slow down due to adverse road conditions such as rain or snow. And the intelligent video recorder will help you understand the context of the accident - for instance, by informing you that the driver accelerated rapidly before the collision.

6. Driver monitoring and risk assessment increase driving safety and comfort

Car journeys may be exhausting. But not for artificial intelligence. The biggest brands are increasingly equipping vehicles with solutions aimed at monitoring fatigue and driver reaction time. By combining intelligent software with appropriate sensors, the manufacturer can fit the car with features that will significantly reduce the number of accidents on the road and discomfort from driving in difficult conditions.

•     Tesla    monitors the driver's eyes, thus checking the driver's level of fatigue and preventing them from falling asleep behind the wheel. It’s mainly used for the Autopilot system to prevent driver from taking short nap during travel.
•     The BMW 3 Series    is equipped with a personal assistant, the purpose of which is to improve driving safety and comfort. Are you tired of the journey? Ask for the "the vitalization program" that will brighten the interior, lower the temperature or select the right music. Are you cold? All you have to do is say the phrase "I'm cold" and the seats will be heated to the optimal temperature.

Maintenance - how AI helps you take care of your car

7. Predictive Maintenance prevents malfunctions before they even appear

Cars that we are driving today are already pretty smart. They can alert you whenever something needs your attention and they can pretty precisely say what they actually need – oil, checking the engine, lights etc. The Connected Car era however equipped with the possibilities given by AI brings a whole lot more – predictive maintenance. In this case AI monitors all the sensors within the car and is set to detect any potential problems even before they occur.

AI can easily spot any changes, which may indicate failure, long before it could affect the vehicle’s performance. To go even further with this idea, thanks to the Over-The-Air Update feature, after finding a bug that can be easily fixed by a system patch, such solution can be sent to the car Over-The-Air directly by the manufacturer without the need for the customer to visit the dealership.

•     Predi    (an AI software company from California) has created an intelligent platform that uses the service order history and data from the Internet of Things to prevent breakdowns and deal with new possible ones faster.

8. Insure your car directly from the cockpit

Driving a car is not only about operating costs and repairs, but also insurance that each of us is required to purchase. In this respect, AI can be useful not only for insurance companies (  see how AI can improve the claims handling process ), but also for drivers themselves. Thanks to the appropriate software, we will remember about expiring insurance or even buy it directly from the comfort of our car, without having to visit the insurer's website or a stationary point.

•  The German company     ACTINEO,    specialising in personal injury insurance, processes and digitises 120,000. claims annually. Their ACTINEO Cockpit service is a digital manager that allows for the comprehensive management of this type of cases, control of billing costs, etc.
•  In collaboration with     Ford, Arity    provides insurers - with the driver's consent, of course - data on the driving style of the vehicle owner. In return for sharing this information, the driver is offered personalised insurance that matches his driving style. The platform’s calculations are based on "more than 440 billion miles of historical driving data from more than 23 million active telematics connections and more than eight years of data directly from cars (source: Green Car Congress).

When will AI take over the automotive industry?

In 2015, it is estimated that only 5-10% of cars had some form of AI installed. The last five years have brought the dissemination of solutions such as parking assistance, driver assistance and cruise control. However, the real boom is likely to occur within the next 8-10 years.

From now on, artificial intelligence in the automotive industry will no longer be a novelty or wealthy buyers’ whims. The spread of the Internet of Things, consumer preferences and finding ways of saving money in the manufacturing process will simply force manufacturers to do this - not only in the vehicle cockpits, but also on the production and service lines.

To this end, they will be made to cooperate with manufacturers of sensors and ultrasonic solutions (cooperation between BMW and Mobileye, Daimler from Bosch or VW and Ford with Aurora) and IT companies providing software for AI. A dependable partner who understands the potential of AI and knows how to use its power to create the  car of the future is the key to success for companies in this industry.

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Generative AI is quickly emerging as one of the key drivers of automotive innovation. This shift is not just a future possibility; it's already happening. In 2023, the generative AI market in the automotive sector was valued at approximately  USD 387.54 million . Looking ahead, it's projected to surge to about  USD 2,691.92 million by 2032 , demonstrating a robust Compound Annual Growth Rate (CAGR) of 24.03% from 2023 to 2032. Major Original Equipment Manufacturers (OEMs) are integrating sophisticated AI algorithms into various aspects of the industry, from vehicle design to enhancing customer interactions.

The impact of generative AI in the automotive sector is already evident. For instance, NVIDIA's generative AI models empower designers to swiftly  transform 2D sketches into intricate 3D models, significantly speeding up the design process and opening up new avenues for creativity and efficiency​​. Meanwhile, automotive manufacturing companies are exploring collaborations with tech giants to integrate advanced AI language models into their vehicles, enhancing the driving experience​​.

This article will explore how leading automotive players are leveraging generative AI to not only keep pace with the evolving demands of the market but also redefine mobility and automotive excellence.

Software-defined vehicles and generative AI

The introduction of software-defined vehicles (SDVs) represents a significant shift in the automotive industry, moving beyond traditional performance metrics such as horsepower and chassis design to focus on software and digital capabilities. By 2025, it is estimated that vehicles could require as much as  650 million lines of code each. These vehicles heavily rely on software for critical operations such as driving assistance, navigation, and in-car entertainment systems.

The integration of generative AI in this domain further amplifies these capabilities. Generative AI is known for its ability to create and optimize designs and solutions, which is beneficial in improving both the software and hardware aspects of SDVs. It helps in generating efficient algorithms for vehicle control systems, contributes to the development of more effective and adaptive software solutions, and even assists in designing vehicle components for better performance and efficiency.

However, bringing generative AI into this landscape presents both unique opportunities and significant challenges.

The opportunities and challenges

•     Challenges  

Integrating advanced AI systems into modern vehicles is a complex and multifaceted task that demands  technical expertise and careful attention to data security and privacy , particularly with the increasing reliance on data-driven functionalities in vehicles.

The automotive industry is facing a complex regulatory environment. With the growing importance of AI and data in-vehicle systems, it has become crucial to  comply with various international regulations and standards , covering areas such as data protection, safety, and environmental impact.

One significant challenge for OEMs is the  lack of standardization within the software-defined vehicles industry, which can complicate the development and integration of new technologies as there are no universal norms or protocols to guide these processes.

 Internal transformation is also a critical aspect of this integration. OEMs may need to revamp their internal capabilities, processes, and technological infrastructure to use generative AI effectively.

•     Opportunities  

Integrating generative AI technology allows for more  creative and efficient vehicle design , resulting in  quicker prototypes and more innovative models .

It also allows for creating  personalized vehicles that cater to individual user preferences like never before. In manufacturing, generative AI promotes more efficient and  streamlined production processes , which optimizes resources and reduces waste.

Let's explore how automotive manufacturers already use gen AI to boost their operations.

Generative AI applications in the automotive industry

Generative AI's integration into the automotive industry revolutionizes multiple facets of vehicle design, manufacturing, and user experience. Let's explore these areas:

Design and conceptualization

•     Vehicle Design Enhancement    : Artificial Intelligence is revolutionizing the vehicle design process by speeding up the initial phase of the design cycle. Generative design algorithms use parameters such as material properties, cost constraints, and performance requirements to generate optimal design solutions. For example, in vehicle body design, AI can propose multiple design options that optimize for aerodynamics and strength while minimizing weight. This enables quick visualization and modification of ideas.

 Toyota Research Institute has introduced a generative AI technique to optimize the vehicle design process to produce more efficient and innovative vehicles. This approach allows designers to explore a wider range of design possibilities, including aerodynamic shapes and new material compositions.

•     Digital Prototyping    : The use of Generative AI technology makes it possible to create digital prototypes, which can be tested and refined extensively without the need for physical models. This approach is highly beneficial, as it enables designers to detect and correct potential design flaws early in the process.

 BMW's use of NVIDIA Omniverse is a significant step in design improvement. The company uses this platform to create digital twins of their manufacturing facilities, integrating generative AI to enhance production efficiency and design processes.

Manufacturing and production

•     Streamlining Manufacturing Processes    : Generative AI significantly enhances the efficiency of manufacturing processes. Unlike traditional AI or machine learning models, generative AI goes beyond identifying inefficiencies; it actively generates novel manufacturing strategies and solutions. By inputting parameters such as production timelines, material constraints, and cost factors, generative AI algorithms can propose a range of optimized manufacturing workflows and processes.

 BMW has implemented generative AI in a unique way to improve the scheduling of their manufacturing plant. In partnership with Zapata AI, BMW utilized a quantum-inspired generative model to optimize their plant scheduling, resulting in more efficient production. This process, known as Generator-Enhanced Optimization (GEO), has significantly improved BMW's production planning, demonstrating the potential of generative AI in industrial applications.

•     Supply Chain Resilience    : In the context of supply chain management, particularly during challenges like the automotive microchip shortage, generative AI plays a crucial role. Unlike conventional AI, gen AI can do more than just analyze existing supply chain networks; it can creatively generate alternative supply chain models and strategies. The algorithms can propose diverse and robust supplier networks by leveraging data about supplier capabilities, logistics constraints, and market demands.

•     Customized Production    : With generative AI, it is now possible to create personalized vehicles on a large scale, meeting the growing demand for customization in the automotive industry.

Predictive maintenance and modelling

Traditionally, predictive maintenance relies on historical data to forecast equipment failures, but generative AI enhances this process by creating detailed, simulated data environments. This technology generates realistic yet hypothetical scenarios, encompassing a vast array of potential machine failures or system inefficiencies that might not be present in existing data sets.

The generative aspect of this AI technology is particularly valuable in situations where real-world failure data is limited or non-existent. By synthesizing new data points, generative AI models can extrapolate from known conditions to predict how machinery will behave under various untested scenarios.

In modeling, generative AI goes a step further. It not only predicts when and how equipment might fail but also suggests optimal maintenance schedules, anticipates the impact of different environmental conditions, and proposes design improvements.

Customer experience and marketing

One of the challenges in using generative AI, particularly in customer interaction, is the accuracy of AI-generated responses. An example of this was an error by ChatGPT, where it was tricked into suggestion of  buying a Chevy for a dollar . This incident underlines the potential risks of misinformation in AI-driven communication, emphasizing the need for regular updates, accuracy checks, and human oversight in AI systems. Nevertheless, this technology offers many opportunities for improving the customer experience:

•     Personalized User Experiences and Enhanced Interaction    : AI's capability to adapt to individual preferences not only enhances the driving experience but also improves the functionality of vehicle features.

For instance, in collaboration with Microsoft,  General Motors is exploring the use of AI-powered virtual assistants that offer drivers a more interactive and informative experience. These assistants can potentially provide detailed insights into vehicle features and performance metrics and offer personalized recommendations based on driving patterns.

Also,  Mercedes-Benz is exploring the integration of generative AI through voice-activated functionalities in collaboration with Microsoft. This includes leveraging the OpenAI Service plugin ecosystem, which could allow for a range of in-car services like restaurant reservations and movie ticket bookings through natural speech commands.

Example applications

•     Simplified Manuals    : AI technology, enabled by natural language processing, has simplified the interaction between drivers and their vehicles. Beyond just responding to voice commands with pre-existing information, a generative AI system can even create personalized guides or tutorials based on the driver's specific queries and past interactions.

    Grape Up has developed an innovative          voice-driven car manual         that allows drivers to interact with their vehicle manual through voice commands, making it more accessible and user-friendly. With this technology, drivers no longer have to navigate through a traditional manual. Still, they can easily ask questions and receive instant verbal responses, streamlining the process of finding information about their vehicle.  

•     Roadside Assistance:    In this scenario, generative AI can go beyond analyzing situations and suggesting solutions by creating new, context-specific guidance for unique problems. For instance, if a driver is stranded in a rare or complex situation, the AI could generate a step-by-step solution, drawing from a vast database of mechanical knowledge, previous incidents, and environmental factors.

•     Map Generation    : Here, generative AI can be used to not only update maps with real-time data but also to predict and visualize future road conditions or propose optimal routes that don't yet exist. For example, it could generate a route that balances time, fuel efficiency, and scenic value based on the driver's preferences and driving history.

•     Marketing and Sales Innovation    : Generative AI-enabled content engine is transforming the creation of digital advertising for the automotive industry. This content is tailored to meet the unique requirements of automotive brands and their consumers, thereby revolutionizing traditional marketing strategies.

Safety and compliance

•     Enhancing Vehicle Safety    : Generative AI in vehicles goes beyond traditional AI systems by not only assisting drivers but also by creating predictive models that enhance safety features. It processes and interprets data from cameras and sensors to foresee potential road hazards, often employing advanced generative models that simulate and predict various driving scenarios.

•     Regulatory Compliance    : Similarly, gen AI helps automakers comply with safety standards and navigate complex regulation changes by monitoring performance data and comparing it against regulatory benchmarks. This allows automakers to stay ahead of the compliance curve and avoid potential legal and financial repercussions.

Autonomous vehicle development

•     Simulation and Testing    : Generative AI is crucial for developing autonomous vehicle systems. It generates realistic simulations, including edge-case scenarios, to test and improve vehicle safety and performance.

•     Enhancing ADAS Capabilities    : AI technology can improve essential Advanced Driver Assistance Systems (ADAS) features such as adaptive cruise control, lane departure warnings, and automatic emergency braking by analyzing data from various sensors and cameras. Generative AI's strength in this context lies in its ability to not only process existing data but also to generate new data models, which can predict and simulate different driving scenarios. This leads to more advanced, reliable, and safer ADAS functionalities, significantly contributing to the evolution of autonomous and semi-autonomous driving technologies.

Conclusion

As the automotive industry accelerates towards a more AI-integrated future, the role of expert partners like Grape Up becomes increasingly crucial. Our expertise in navigating the intricacies of AI implementation can help automotive companies unlock the full potential of this technology. If you want to stay ahead in this dynamic landscape, now is the time to embrace the power of generative AI. For more information or to collaborate with Grape Up,  contact our experts today.

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

What exactly is Open Source Software in the automotive industry?

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

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

Flexible customization to meet your needs

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

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

OSS is gaining ground

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

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

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

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

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

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

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

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

Examples of open-source solutions in the auto industry

Automotive Grande Linux

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

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

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

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

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

Red Hat In-Vehicle Operating System

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

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

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

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

Android Automotive OS

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

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

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

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

Here are selected capabilities of the above-mentioned software:

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

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

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

COVESA / Genivi

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

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

Among the issues addressed are the following:

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

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

What limitations do you need to be aware of?

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

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

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

OSS opportunities and challenges

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

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

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

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