When thinking about artificial intelligence in the automotive industry, the first thing that comes to our minds is self-driving cars. But AI can do much more than just drive vehicles. Thanks to AI and machine learning algorithms, drivers remain connected to many different services and get better driving experience, while manufacturers process plenty of valuable data and build better products.

AI in the automotive industry is a large business. Its value is expected to grow at a CAGR of 39.8% from 2019 and reach $15.9 billion by 2027.

In this article, we zoom in on artificial intelligence and its subset machine learning to see how applications of AI are impacting automotive manufacturers, vehicle owners, and service providers.

AI and machine learning in the automotive industry — applications

AI and Machine Learning applications in the automotive industry
There are several applications of AI and machine learning in the automotive industry.

Self-driving and autonomous vehicles

When considering the most popular application of AI, we should know that cars equipped with this technology offer two levels of autonomy: a self-driving system or a fully autonomous mode.

Self-driving systems and driver assistance

These solutions allow the AI to take the co-pilot’s seat in the vehicle. Such applications help everyone from customers and manufacturers to regulators in becoming comfortable with AI as a driver before turning to fully autonomous vehicles.

AI can identify dangerous situations by monitoring data coming from many different sensors and take emergency control of the vehicle to avoid an accident. Blind-spot monitoring, emergency braking, or cross-traffic alert monitors are just a few examples of how AI improves driving.

AI powers autonomous vehicles
The combination of AI, machine learning algorithms, and cloud technologies is the key to fully autonomous vehicles.

Fully autonomous vehicles

We’ve already had the mechanical systems required to control the vehicle braking, steering, and acceleration for many years. What was lacking was the brain to control all of it.

AI promises to fulfill this goal. First of all, the amount of processing power required to drive the vehicle is gigantic and conventional computers aren’t up to the task. This is where cloud computing comes in. Together with sophisticated machine learning algorithms, cloud technologies allow machines not only to perform tasks but also to learn from them.

Read more about autonomous vehicles >

Industry examples: Waymo and Tesla

Waymo is a company that belongs to Alphabet (mother company of Google). Making strides in the autonomous vehicle market, Waymo plans to expand the technologies behind autonomous vehicles and has already been carrying out test drives in Phoenix. The AI software of Waymo brings together data from lidar, radar, high-resolution cameras, GPS, and cloud services to create control signals that operate the vehicle. AI doesn’t only respond to what’s happening outside of the vehicle but also predicts what objects the vehicles might travel past. For example, if the vehicle is located next to a pedestrian sidewalk, the AI system will know that a pedestrian might step into the street at any moment.

Tesla, on the other hand, has succeeded in becoming a mainstream product in the electric car market and wants to popularize autonomous vehicles as well. Tesla’s vehicles are equipped with eight cameras, sensors, forward-facing radar, GPS, and more. To understand its environment, the vehicle’s computer sends all the data into an AI program that transforms sensory data into vehicle control data. That’s what autopilot software does – the autopilot doesn’t only drive the car, but it can also check the driver’s calendar and drive them to their scheduled appointment. Each new model of Tesla comes equipped with features enabling autonomous driving. All we’re waiting for is the regulatory approvals so that the company can enable the software and put AI in the driver’s seat.

Smart manufacturing

AI impacts the end product that actually interacts with the consumer, but it also plays a critical role in revamping the entire manufacturing process of automotive companies.

For example, assembly-line robots that have been part of vehicle production for more than half a century now are now transformed into smart robots that work together with humans. Kia Motors is already using robotics technology via the development of the Hyundai Vest Exoskeleton (H-VEX) wearable industrial robots. These robots enhance the manufacturing process and help the overall production.

Another example is automated guided vehicles able to move materials around factories on their own. They can identify objects on their path and then adjust the route easily. You can also find painting robots on manufacturing floors that follow the preprogrammed standards and instantly alert quality control personnel of any identified defects. All of these features are powered by AI to shorten production time without affecting its quality.

Artificial Intelligence and Machine Learning can help in car maintenance
AI monitors thousands of data points per second that’s why it not only surpasses traditional alerts, but in many cases, it can also notice failures faster than the car’s driver.

Predictive maintenance

The application of artificial intelligence and cloud platforms ensures that relevant data is available whenever needed. This powers systems like predictive maintenance, which relies on connected devices sending alerts via sensors.

Conventional vehicles can alert us about maintenance requirements by low battery indicators, check engine light, or oil light. This differs entirely from the possibilities offered by innovative connected vehicles equipped with AI software that monitors hundreds of sensors located all around the vehicle, capable of detecting problems before they affect the vehicle’s operation.

AI monitors thousands of data points per second and can indicate a pending component failure long before that failure actually affects the experience of drivers.

Manufacturers can offer predictive maintenance and over the air software updates for the entire brand of vehicles to help to enhance the customer experience and lower the cost of maintaining their products.

Personalization in marketing

Many industries are experiencing increased competition and struggle to keep customers engaged with their offers. This opens the door to personalized marketing delivered via intelligent vehicles.

Companies can use AI to target an audience of qualified prospects with the most relevant messages at the right time.

AI connected with Big Data and vehicle infotainment systems can suggest products and services to drivers on the basis of their personalization profiles.

For example, a driver who announced a wedding on social media can be alerted for sale at the bridal store just around the corner when driving. If the vehicle experiences low fuel, the system can automatically suggest the nearest gas station that is included in the system. AI will learn its drivers’ needs and notify them when they’re close to a business that might serve them.

AI and ML in automotive
AI can accelerate the process of filing claims when accidents occur, recreate risk profiles based on drivers’ individual data-based risk factors, or even support the driver in DIY auto damage assessment.

AI-powered automotive insurance

The insurance industry and artificial intelligence are both about predicting the future. No wonder that insurance has embraced the use of AI automotive insurance solutions to help make more accurate risk assessments in real time.

For starters, AI accelerates the process of filing claims when accidents occur. But it can do many more things. AI can recreate risk profiles based on drivers’ individual risk factors found in the data and look for many less obvious factors that predict how safe the driver is likely (considering anything from their health issues to personal matters and diet).

Another interesting use of AI is for Do-it-Yourself auto damage assessment. Art Financial published an application to the Chinese auto market powered by AI that enables drivers to carry out their own auto damage assessment for insurance companies. The on-screen instructions show users how to video their vehicle damage for insurance claims and suggest what will be covered by insurance.

Driver monitoring

AI doesn’t only drive but also helps to keep an eye on the driver. For example, the automotive computer vision startup eyeSight uses artificial intelligence and deep learning to offer a broad range of automotive solutions:

  • It uses advanced Time-of-Flight (TOF) cameras and IR sensors to detect driver behavior in four key areas of driver identification, checking whether or not the driver is in the vehicle.
  • It can even implement driver recognition using advanced AI algorithms that detect when the driver is operating the vehicle. For example, every member of a family might have their own preferences and the system can automatically adjust the seats, temperature, and other factors to match the individual.
  • Another solution is driver monitoring. For example, by observing the driver’s gaze, head position, and eye openness, the software can detect distracted driving and alert the driver to keep their eyes open on the road. Drowsiness can be detected by eye openness and head position as well.
  • Moreover, contextual controls allow AI to tailor the content of the heads-up display according to where the driver’s eyes are focused. In case of a crash, the system will release airbags in a way based on how the driver was sitting, thanks to body detection features.

Conclusion

We hope that this article shows you why artificial intelligence and machine learning algorithms play such a critical role in the technological advancements of the automotive industry today.

If you’re considering a project that uses AI and machine learning in the automotive sector, get in touch with us. Our teams are experienced in delivering such projects and know how to leverage the most innovative approaches for the benefit of automotive manufactures and service providers — as you can observe in our case studies: Porsche, BMW/Deloitte, KIA Motors.

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karol.przystalski

Karol Przystalski is CTO and founder of Codete. He obtained a Ph.D in Computer Science from the Institute of Fundamental Technological Research, Polish Academy of Sciences, and was a research assistant at Jagiellonian University in Cracow. His role at Codete is focused on leading and mentoring teams.