Today’s consumers expect their cars to become as smart and personalized as their mobile devices and personal voice assistants. In combination with AI, many emerging technologies and concepts, including IoT data analytics, cloud computing, 5G, and big data open up new horizons for interconnectivity between vehicles and the surrounding environment.
Notably, the automotive industry has always been proactive in adopting emerging technologies. Now, companies in the automotive sector are increasingly integrating AI to tap into connected vehicle technologies, autonomous driving, and predictive maintenance.
What is AI in automotive?
AI in the automotive sector covers many areas, from advanced driver assistance systems that detect and respond to potential hazards on the road to voice recognition technology that allows drivers to stay connected while keeping their hands on the wheel.
Exciting aspects of artificial intelligence services in the automotive sector include their potential to increase efficiency and safety and decrease the industry’s environmental impact by optimizing fuel usage and enabling autonomous driving. As artificial intelligence continues to evolve and make a mark on the automotive industry, more AI implementations in the automotive sector are coming shortly.
AI in automotive market statistics
AI in the automotive value chain
With AI applied across all stages of the value chain, the automotive industry stands out from other sectors using the technology:
Modern vehicle manufacturers extensively rely on AI to design smarter cars, make car assembly more efficient using robots and exoskeletons, and streamline supply chain management.
AI is the backbone of driver assistance technology. From helping drivers to navigate highways to recognizing their tiredness to detecting crucial defects in a vehicle, AI makes driving less stressful and more secure.
By embedding IoT sensors into vehicles, sophisticated AI systems can predict engine failure and optimize battery performance based on how drivers use the car. On top of that, there is a growing demand for AI-enabled insurance, which can streamline claims settlements.
20 use cases of AI in the automotive industry
- Vehicle design
- Predictive maintenance
- Vehicle diagnostics
- Automated vehicles
- Marketing automation
- Fleet management
- Generative design
- Voice assistants
- Connected vehicles
- Quality control
- Demand forecasting
- Behavior analytics
- Driver care
- Battery engineering
- Emission monitoring
- Equipment maintenance
- Combating fraud
AI can help drivers make better navigation decisions by analyzing various road situations. Modern AI-powered navigation systems can suggest better routes based on information about nearby road closures, accidents, traffic jams, construction work, and road conditions.
2. Vehicle design with digital twins and AI
Modern AI carmakers extensively use digital twins in their manufacturing facilities to streamline car design by simulating how certain design decisions will impact car performance. By feeding machine learning systems historical and sensor data, engineers and designers can get an insight into how their ideas translate into vehicle performance. This technology can also help eliminate costs associated with testing physical prototypes.
3. Predictive maintenance for customers
With the myriad of connected sensors embedded in various vehicle parts, carmakers can make informed maintenance decisions and address emerging problems before they become critical. For instance, smart sensors can alert drivers about low tire pressure or the need to change oil.
4. Remote vehicle diagnostics
Predictive analytics-powered maintenance benefits car manufacturers as well as car owners. Customers get timely alerts about potential technical issues, and turn to manufacturers for maintenance rather than independent car repair shops.
5. Fully automated vehicles
There are still considerable regulatory challenges around completely autonomous vehicles. However, a future where driverless cars take over delivery and public transportation is closer than one may think. Deep learning and advanced computer vision help vehicles follow traffic rules and safely drive with little to no human intervention.
6. Streamlined automotive insurance
With the help of AI and computer vision, drivers can use their mobile phone cameras to take pictures of damaged cars after accidents for AI- and computer vision-based systems to analyze car damage. This way, the assessment process becomes much faster and more objective.
7. Marketing automation
AI is now a must-have tool for car brands’ marketers. By assessing customers’ purchase cycles and social media data, marketing specialists can discover new opportunities for cross-selling and upselling, automate lead management, personalize advertisements, forecast demand and sales, and improve marketing strategies.
8. Fleet management
Based on data about road conditions, traffic in a specific area, weather, and other environmental information, AI systems can help fleet managers identify the most efficient routes, predict potential delays, and alert appropriate personnel about them.
9. Generative design
Similar to how forward-looking firms use Artificial intelligence in architecture to come up with new shapes and forms, automotive manufacturing companies are also using generative design to create more robust, durable, and sustainable automotive parts. AI-based design systems can generate hundreds of design variations for a specific component based on the set of parameters defined by designers and engineers.
10. Personal voice assistants
While a share of industry players implement third-party personal assistants like Alexa and Siri, some automotive companies have chosen to create their own state-of-the-art voice-recognition software. Such assistants can adjust the temperature, provide information about the amount of gas in the tank, make calls, and change radio stations. Importantly, these tools have high levels of personalization, meaning they can remember drivers' preferences and suggest adjustments based on the context and user history.
11. Connected vehicles
The rapid growth of IoT use cases and advancements in AI, 5G, and cloud computing enable vehicles to connect to each other, mobile devices, and infrastructures, making autonomous driving safer and more efficient. For example, cars can communicate with each other to ensure they keep a safe distance.
Another example is connected delivery trucks that can formulate tight convoys at the highest speeds for minimal wind resistance and fuel consumption decrease. Furthermore, connected vehicles can reduce traffic congestion and travel times. Traffic managers would be able to get a bigger picture of the road situation and more efficiently control traffic flow.
12. Quality control
While many car manufacturers already use machine vision for quality control, they aren’t adaptive to product changes and can detect only a fraction of possible defects. Quality control solutions with deep learning and computer vision can go beyond simple anomaly detection and intelligently detect and classify multiple defects simultaneously. It can almost eliminate human intervention and dramatically increase quality control system efficiency.v
13. Demand forecasting
The automotive industry has the most complex supply chain ecosystem, and AI has proved effective for demand forecasting. Intelligent solutions can predict demand based on economic conditions and change in the industry environment. It allows manufacturers to adjust output in line with the demand and lower excess inventory costs.
Combined with other technologies of Industry 4.0 like blockchain and IoT, AI systems also take into account shipment and equipment conditions information. It can improve supply chain transparency and traceability, ensure visibility across the supply chain, and, ultimately, transform the supply chain into a smart one.
14. Driver behavior analytics
IoT sensors installed across a car can send data to a deep learning-powered system to analyze driver behavior. These insights, which include oil change intervals, brake usage, vehicle downtime after an accident, and fuel consumption, can be used when upgrading existing vehicles or creating new ones.
15. Intelligent driver care
With the help of emotion recognition, computer vision, smart IoT sensors, and AI, modern vehicles can recognize driving behavior that can cause traffic hazards. By assessing a driver’s body temperature, eye movement, head position, driving behavior and time, embedded AI systems can detect if a driver’s state is potentially dangerous and stop the car or shift to self-driving mode.
16. Enhanced battery engineering for electric vehicles
Using modern machine learning applications, engineers can streamline battery development for electric vehicles by accurately predicting how various conditions will impact battery performance. AI can also help determine optimal battery shape, size, and chemical composition much faster, reducing product lifecycles.
17. Emission monitoring
Rapid carbon emissions rise is on the agenda worldwide. Still, many automotive companies have no means to accurately measure their carbon footprint. Fortunately, environmentally-aware organizations like BCG are now working on AI-based systems that help measure greenhouse gas emissions and reduce their carbon footprint by more than 30%.
18. Equipment predictive maintenance
Traditionally, technicians perform equipment maintenance regularly to ensure that the machinery won't fail unexpectedly. Instead, IoT sensors can gather data from machinery parts and send this data to an AI-based system that detects performance deviations and alerts employees about potential failures. This way, automotive manufacturers can cut maintenance costs and save employees' time.
19. Customer service chatbots
Conversational AI is an important tool for enhancing relationships between customers and brands and increasing brand loyalty. AI chatbots can take over employees’ mundane tasks like scheduling test drives, helping customers with car model selection, answering customers’ questions about car features, and gathering customer feedback.
20. Combat insurance fraud
Fraud is a serious problem in the automotive insurance industry. Every year, insurers pay out billions of dollars in fraudulent claims, and the cost of fraud is reflected in higher premiums for policyholders. Combined with natural language processing, AI can spot suspicious patterns in claims and flag them for human employees to review. With the help of predictive data analytics tools, AI can estimate the fraud risk of policyholders based on their behavior patterns and customer profiles.
10 benefits of AI in the automotive industry
AI can help reduce the number of road accidents by providing drivers with real-time information about potential hazards.
Faster emergency response
AI can help improve the speed of emergency response by automatically sending alerts to emergency services in the event of an accident.
Smarter traffic management
AI can manage traffic flows more efficiently, reducing congestion and making journeys more enjoyable for drivers.
Improved fuel efficiency
AI can help drivers make better decisions about when and how to accelerate and brake, improving fuel efficiency.
Reduced carbon emissions
Smart driving can reduce vehicle emissions, helping improve air quality and combat climate change.
AI can personalize in-vehicle infotainment systems based on a driver’s preferences and needs.
AI can help make driving safer by providing warnings about potential hazards and assisting with tasks such as parking and reversing.
AI can help improve connectivity between vehicles and other road users, making journeys more efficient.
Greater convenience for drivers
AI can make driving more convenient by automating route planning and navigation, allowing drivers to focus on other things.
|AI can help automotive insurance companies identify risks, calculate premiums more accurately, and detect fraud.|
How AI in automotive works
5 levels of vehicle automation
One of the most widespread AI applications in the automotive industry is advanced driver-assistance systems (ADAS). According to the World Health Organization, 1.35 million people lose their lives yearly because of traffic accidents. While inadequate road infrastructures and traffic laws are partly to blame, human error is still among the most common reasons for traffic crashes. At this point, machine learning development is the most viable solution to reduce human error in road accidents.
The National Highway Traffic Safety Administration (NHTSA) defines six levels of driving autonomy, as shown in the image below. Most AI-powered vehicles today range from levels 1 to 3 on the NHTSA scale. An immense amount of ML model training is required to make a car fully autonomous, meaning that a driver can essentially become a passenger and entrust all the driving to the machine.
Experts claim that a vehicle needs to process more than 1 TB of data per second on average to become fully autonomous. Companies are turning to various cloud solutions, where the data is labeled, processed, and used to optimize these algorithms. However, the mass deployment of autonomous vehicles doesn't align with the current trend of low energy consumption and poses certain economic hurdles.
Although there are already somewhat successful examples of driverless vehicles, most of them were extensively trained on the same route only. While this way an AI system can develop a highly detailed map that accounts for every nuance of the road, this is not a practical solution when deploying autonomous vehicles on a larger scale, because mapping every possible route even in one region is not feasible.
At the end of 2020, Tesla announced that the beta version of its full self-driving (FSD) software will be available to a very narrow range of selected drivers. At the end of January 2021, the FSD beta managed to impress the public by handling some of the most complex routes in Berkeley, CA, hinting that Level 5 autonomy is not as far away as it may seem.
Here are the six main types of sensors used to gather data in autonomous vehicles:
- Computer vision cameras
Powered with automated visual inspection technology, cameras can detect traffic signals and street signs and recognize vehicles and pedestrians. Data from computer vision cameras are also essential for driver assistance features such as automatic braking, lane departure warnings, and adaptive cruise control.
- Lidar sensors
Lidar sensors gauge the distance between vehicles and other surrounding objects to command the car to slow down or speed up. Interestingly, Tesla, an obvious leader in the autonomous car manufacturing industry, is against the use of Lidar because it can't provide AI systems with sufficient information about an object. Whether it's a plastic bag or a dog, Lidar will define them both as just moving objects.
- Radar sensors
By sending out radio waves and interpreting the reflected signals, radar sensors can accurately measure the distance and speed of nearby objects in real time. This allows the vehicle to detect and respond to obstacles on the road, navigating safely without human input. In addition, radar sensors have a longer range and can operate in various weather conditions, which makes them a more reliable option than other sensors.
- Ultrasonic sensors
Ultrasonic sensors use high-frequency sound waves to detect objects in the car's path, assisting with parallel parking and alerting the driver of objects in their blind spot. In addition, these sensors can also ensure collision avoidance, automatically applying the brakes if an object is detected in front of the vehicle.
- Internal navigation systems
Internal navigation systems typically include accelerometers and gyroscopes that estimate vehicle position in space and its speed. Such systems are often used in conjunction with GPS to accurately locate a vehicle on the map.
- Infrared sensors
Infrared sensors help AI systems to recognize objects in low-lightning conditions with the power of infrared radiation.
There is a combination of three types of AI algorithms that are most commonly used in autonomous vehicles: regression, cluster, and decision matrix:
- Regression algorithms
In autonomous driving, regression algorithms predict the outcome of various situations based on historical data. For example, regression algorithms can forecast the likelihood of a pedestrian stepping into the vehicle's path or encountering an obstacle on a particular road. Moreover, these algorithms constantly gather and analyze data during each journey, allowing for continuous improvement in their decision-making abilities.
- Cluster algorithms
In autonomous vehicles, cluster algorithms have a vital role in organizing and interpreting the massive amounts of data collected from sensors, cameras, and GPS systems. These algorithms help identify and categorize objects on the road so that the vehicle can take appropriate actions such as slowing down or changing lanes. Cluster algorithms also play a key role in route planning and navigation, constantly updating and optimizing the route based on real-time traffic conditions.
- Decision matrix algorithms
In essence, decision matrix algorithms are used for making decisions. They operate on predetermined conditions and responses programmed into the algorithm but can also take into account real-time data from a vehicle's sensors. These algorithms determine the car’s next move, like whether to take a turn and which turn to take. The success of these actions relies on the algorithm's ability to accurately classify, recognize, and predict an object's next movement.
Other technologies enabling autonomous driving
Here are some of the technologies used in combination with AI to enable more convenient autonomous driving:
5G allows all vehicles within a network to swiftly share their state and location data at sufficient speed.
IoT enables sharing of data between the vehicles within a network.
Cloud allows the storage and processing of map and traffic data on remote services instead of on a vehicle’s onboard computer.
Self-driving vehicles rely on the Global Positioning System to locate a car in space with the help of real-time geographical data.
Sensor fusion technology combines data from multiple sensors to create a more accurate picture of the environment.
Top 5 AI in automotive companies
Arguably the most known EV manufacturer in the world, Tesla uses AI to enable self-driving capabilities in its cars. Last year, Tesla unveiled its new D1 chip that runs its supercomputer and can allegedly process camera imaging data four times faster than other computing systems.
BMW uses more than 400 AI applications across various company operations, including the development of new vehicles and buildings’ energy management, as well as to power automated driving and in-vehicle personal assistants.
Nvidia is one of the world's leading manufacturers of AI-powered solutions for autonomous vehicles. Their software can process large amounts of sensor data, help vehicle manufacturers design new cars, and enable driver monitoring.
One of the most well-known US-based vehicle manufacturers, General Motors, uses AI for the development of self-driving cars as part of its Cruise Automation subsidiary and for autonomous vehicle inspection at its manufacturing facilities.
Formerly the Google self-driving car project and now an autonomous driving tech company, Waymo uses AI to power self-driving capabilities in its taxis, delivery vans, and tractor-trailers.
Notable examples of AI used in automotive
Hyundai’s walking vehicle
Hyundai Motor Group applies AI-based generative design to redefine vehicle development and revolutionize how vehicles traverse.
Designing a vehicle that can not only drive in a conventional sense but also walk called for novel development methods and out-of-the-box thinking. Thanks to the extensive expertise of Hyundai engineers, their collaboration with a product innovation studio Sundberg-Ferar, and the power of generative design, the company has managed to build the Ultimate Mobility Vehicle called Elevate that can traverse terrains that no other vehicle can. Elevate can transform from a four-wheeled vehicle into a four-legged walking robot. Elevate’s inventors claim the vehicle will prove most useful for search and rescue.
Generative design really allows us to tackle complex problems that would take somebody a lot more time than they have to go through different analyses. It’s a mind multiplier, I like to call it, where a single designer or engineer can go through perhaps dozens or hundreds of different design iterations. It allows them to see things that they may not have otherwise considered.
BMW’s AI-enabled engineering
Monolith is well-known AI-based software in the engineering world used by many aerospace, automotive, and industrial engineering companies. In 2022, BMW, one of the world's leading manufacturers of automobiles and motorcycles, announced that its engineers are now using Monolith in vehicle development. More precisely, BMW's AI engineers use AI to accurately predict a car's performance in terms of aerodynamics without building physical prototypes. What's more, BMW's crash test engineering team also managed to apply Monolith for predicting the force on a passenger’s tibia during a crash without conducting physical tests and way earlier in the development process.
When the intractable physics of a complex vehicle system means it can’t be truly solved via simulation, AI and self-learning models can fill the gap to instantly understand and predict vehicle performance. This offers engineers a tremendous new tool to do less testing and more learning from their data by reducing the number of required simulations and physical tests while critically making existing data more valuable.
Kia, the oldest vehicle manufacturer in South Korea, embeds computer vision inside a vehicle to make the driving experience more comfortable by personalizing the mirror, seat positioning, and other features to the driver’s needs.
Audi uses computer vision for the visual inspection of sheet metal in vehicles. AI systems can detect even the smallest cracks at the production stage, allowing the company to significantly reduce faulty parts in finished products.
Tesla uses AI to detect the driver's level of tiredness and fatigue, preventing the driver from falling asleep behind the wheel.
Some of BMW's latest models are equipped with AI-powered personal assistants that can enhance drivers' safety and comfort by brightening the interior or changing music depending on one’s mood and the level of fatigue.
Ford partnered with Arity, a mobility data and analytics company, to provide insurance companies with information about vehicle owners' driving styles, allowing insurers to tailor their insurance offerings to each individual case.
Bottlenecks of using AI in the automotive industry
Despite a multitude of benefits that AI can bring to the automotive industry, companies need to take into account certain risks and bottlenecks associated with the technology’s implementation:
Data privacy concerns
Existing regulatory frameworks like GDPR pose considerable limitations on the collection of personal data, which is essential in AI-enabled driving. Most data processed by AI-assisted vehicles is considered personal data. For example, video footage collected from dashcams or geolocation data must be processed in line with GDPR data protection rules.
Fortunately, industry players including legislators and manufacturers are actively adjusting the relevant legal frameworks. In November 2020, the European Automobile Manufacturers Association released a paper that highlights the current legislative challenges and proposes a clear roadmap for overcoming them.
With the increasing volumes of data needed for AI-assisted vehicles’ operation, more security risks arise. Fraudsters’ motives range from vehicle tracking to data theft and vehicle ID reassignment. For example, in March 2019, the hacking duo Fluoroacetate hacked a Tesla Model 3 car via its embedded browser and thus won the Pwn2Own hacking contest.
One of the most effective preventive measures revolves around encryption-key management systems exclusive to each vehicle. However, encryption keys must be regularly updated and when data is transmitted to a third party, users' consent has to be obtained. While such mechanisms are not user-friendly, one may argue that drivers' confidentiality comes first.
Legal and ethical implications
Legislative matters are paramount for widespread AV adoption. For example, who is accountable in case of an accident?
Many fatal accidents can’t be avoided, implying that fully autonomous vehicles would need to make some of the toughest ethical decisions. Should a transportation AI system decide to run over pedestrians to save the passengers or vice versa? For autonomous vehicles to become a reality, such issues need to be definitively resolved.
Manufacturers are working closely with governments and research organizations to create a supportive environment for technical development while ensuring safety.
Implementation costs and development times
In the automotive context, AI is usually applied to highly complex problems. Regardless of significant breakthroughs in autonomous driving technology, AI solutions still require enormous amounts of training data and consequential training to be truly safe and reliable, which is extremely costly and time-consuming. This is why even world-renowned vehicle manufacturers with plenty of resources partner with third-party AI developers to augment their cars with AI.
5 myths about self-driving cars, explained
You will be able to buy a fully autonomous car soon
Currently, fully autonomous driving vehicles can legally operate only in selected regions in the US. This is because currently level 5 autonomy calls for predefined routes and highly detailed maps. And while Elon Musk predicted that over a million driverless taxis will be operating by 2020, it hasn't come true even at the end of 2022.
Public transport will become obsolete
Driverless vehicles won’t replace public transport because it would most likely make traffic flow worse and only increase parking problems. “What you’re likely to end up with is some sort of equilibrium—one that moves the most people in the most efficient manner that reduces congestion and improves safety” - Sam Abuelsamid, an analyst at Guidehouse Insights.
ADAS will evolve into fully autonomous cars
The whole purpose of advanced driver assistance is to enhance drivers’ safety by helping them in complex situations on the road. It's more about braking at the last second to avoid crashing, alerting drivers about lane departure, and helping with parallel parking than about letting drivers sleep behind the wheel during a daily commute.
Self-driving cars won’t need headlights
Although autonomous vehicles can drive in the dark using sensors, headlights are still important for pedestrians, cyclists, and human drivers.
All autonomous vehicles will be electric
While it’s true that the absolute majority of autonomous vehicles are electric, they are not necessarily driverless and vice versa.
Transforming the automotive industry with AI
The automotive industry is on the cusp of a major shift, thanks to advancements in artificial intelligence. From manufacturing and design to sales and marketing, to service and maintenance, AI can play a role in making cars smarter, safer, and more efficient. The inevitable shift from hardware to software in the automotive industry requires vehicle manufacturers to reimagine their workflows and pay close attention to relevant regulatory frameworks. Given the unprecedented potential of AI in this sector, overcoming legal, ethical, and security challenges is just a matter of time.