The Role of Machine Learning in the Automotive Industry

Published on 31 Jul 2021

Machine learning (ML) is most commonly connected with product advancements in the automobile industry, such as self-driving cars, parking and lane-change aid, and smart energy systems. Moreover, machine learning also has a huge impact on the marketing function, from how automotive marketers set targets and analyse returns on investments to how they communicate with customers.

Furthermore, AI technologies have huge potential when used in manufacturing and production processes, as well as in vehicles to enable in-car functions. Simply put, data science isn’t just about self-driving cars. Data science and machine learning technology can help carmakers stay competitive by enhancing everything from research to design to manufacturing and marketing.

According to Gartner, the overall number of new vehicles with autonomy-enabling technology will increase from 137,129 in 2018 to 745,705 in 2023. The global market for autonomous vehicles is anticipated to reach $37 billion by 2025.

In this article, we will go over some areas in the automotive industry that have been revolutionized by machine learning, along with some real-life examples of companies that are making commendable strides in this sector.

See also: Post COVID-19: The Rise or Decline of Fintech?

1. Design and Manufacturing

Vehicle manufacturers may use AI-powered solutions and machine learning algorithms to optimize production processes, speed up data classification during risk assessments, and evaluate vehicle damage, among other things. In the automotive industry, AI systems and robotics solutions based on technologies like computer vision, natural language processing, and conversational interfaces are frequently used.

Nvidia's Quadro RTX graphics card, for example, leverages AI to drastically speed up design procedures. Rethink Robotics develops collaborative robots for activities such as handling heavy objects and inspecting manufactured parts.

2. Passenger Experience

Manufacturers equip their vehicles with a variety of AI-powered applications aimed at improving the experience for passengers to ensure that it is safe and they are satisfied. To assess the state of the driver and passengers, several systems employ technologies such as face recognition and emotion recognition. Others use natural language processing and generation technologies to allow passengers to watch movies, listen to music, and even place orders for products and services while driving.

Dentsu and Hyundai, for example, funded $10 million in the Audioburst project, which aims to develop an AI-powered infotainment system. Passengers will be able to search music/audio libraries, listen to custom music playlists, and get news updates using this technology, which will include automatic speech recognition and natural language comprehension.

Another example is Amazon working on integrating their AI-powered Alexa speech assistant into a variety of automobiles. For infotainment systems in BMW, Toyota, Ford, and Audi vehicles, Alexa integration is now available.

3. Supply Chain

It's critical for vehicle manufacturers to be able to track every step of a component's travel and know when it'll arrive at its final destination. As a result, cutting-edge IoT, blockchain, and AI technologies are frequently used in modern supply chains.

Vehicle makers, in particular, can use solutions based on various machine learning algorithms and AI-powered predictive analytics. Manufacturers can evaluate component demand and foresee potential changes in demand with their assistance. Blue Yonder, for example, uses artificial intelligence (AI) to improve inventory visibility and allow producers to anticipate delivery delays.

4. Quality Control

AI can help detect a variety of technological difficulties in real time. An AI system can warn a user that a given component or system requires maintenance or replacement as soon as the need arises, based on data acquired by in-vehicle sensors. AI-powered quality control systems are also used by manufacturers to detect potential defects in parts before they are installed.

In-car quality control systems primarily rely on data processing and analysis methods, whereas manufacturing solutions make use of AI-based image recognition and sound processing.

BMW employs AI-powered solutions for a variety of operations, including welding tongs, predictive maintenance and paintwork quality inspection. Predii's AI-powered platform recommends car repairs based on sensor data analysis.

5. Automotive Insurance

AI-powered solutions have great potential in handling insurance claims. On the driver’s side, in-vehicle AI capabilities can be used for gathering incident data and filling out claims. Such a system would need to combine smart data analytics, speech recognition, natural language processing, and text processing and generation.

On the insurance provider’s side, AI systems leveraging image processing and object detection technologies can be of great help for improving the accuracy of vehicle damage analysis.

The Ping An Auto Owner application uses AI capabilities to assess photos uploaded by users making insurance claims. Nauto’s intelligent fleet management system has an AI-powered collision detection feature that enables quicker and more accurate processing of insurance claims.

6. Driver Assistance

Finally, don't overlook the enhancements to the driving experience provided by AI technologies. There are AI systems that help drivers safeguard their safety by alerting them to traffic and weather changes, recommending the most efficient routes, and allowing them to pay for goods and services while on the road.

CarVi is an Advanced Driving Assistance System (ADAS) that analyses traffic data using artificial intelligence. It also warns drivers about potential dangers such as poor driving conditions, lane deviation, and forward crashes in real-time. Real-time image and video recognition, object detection, and action detection are all used substantially in such solutions, but speech recognition and natural language processing technologies may also be used.

Other systems aim to take over the driver's duty — either temporarily, as with some Tesla cars' autopilot features, or totally, as with Waymo's driverless cars and Zoox's robotic ride-sharing vehicles. Complex computer vision skills are frequently combined with real-time big data analysis and natural language processing in these systems.

Machine learning has a wide range of potential applications in the automotive industry. Manufacturers may use AI technology to develop and create new prototypes, improve supply chain efficiency, and enable predictive maintenance for manufacturing equipment and on-the-road vehicles.

As traditional business models are being disrupted by changes in consumer behavior and technological advancements, there’s no doubt that the industry will keep growing as new and advanced technologies keep providing enhanced user experience.

Hence, Carmakers, dealers, and other organisations in the automotive ecosystem must react fast to the changing environment, embracing challenges and opportunities by leveraging data.

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