3 ways AI is disrupting the EV industry

Little has changed in the mechanics or the manufacturing of vehicles since the car was invented over 120 years ago—until now.

We’re in the golden age of personal transportation: the digital age. Just as digitization revolutionized television with the creation of HDTV and digital broadcasting, so too is transportation being transformed by the harnessing of semiconductors and computers to redefine how we interact with and manufacture vehicles.

The newest developments incorporate the advanced technologies of artificial intelligence. AI and autonomous factory control is disrupting the EV industry in three distinct ways:

Increased safety
As AI becomes increasingly sophisticated, OEMs are using it to bring advanced safety systems to vehicles, giving drivers more knowledge and controls to avoid accidents than ever before.

AI helps OEMs skirt recalls and increases driver safety through its predictive data analysis capabilities. Advanced machine learning algorithms can extract input from sensors placed throughout contemporary EVs; this data includes information about many factors that impact operators on the road, including weather conditions, road topography, traffic patterns, and common performance issues. AI acts on this ever-growing dataset to optimize vehicle structure and automatically adjust for anticipated safety concerns.

Since the early 2000s Tesla has pioneered AI-driven safety innovations, including an AI-powered interior camera above the rear-view mirror to improve cabin safety. Leveraging AI innovation, the camera detects and monitors drivers' eyes to perceive their drowsiness and avoid on-road accidents. The technology builds upon the company's neural network technology, which analyzes road images to perform object detection and depth estimation. Harnessing high-quality training data constructed from its fleet of nearly 1 million vehicles in real time, the company's AI effectively warns drivers of nearby risks to avoid collisions.

Audi and other OEMs are developing V2X (vehicle to everything) systems that use AI to identify obstacles, school buses and cyclists in the roadway and eventually, to take automatic evasive action to avoid an accident.

AI is a key component in the development of safe and reliable autonomous vehicles that can anticipate complex road conditions and respond appropriately in a way that keeps a vehicle’s occupants safe while maintaining efficient roadway conditions through advanced, anticipatory navigation.

Personalization
AI is creating the personal vehicle, changing its features as it gets to know the preferences of its various drivers. For example, by combining calendar data, navigation information and historical data, a vehicle can anticipate that a driver is ready to leave the gym and prepare the cockpit for the desired temperature and in-cabin entertainment, while automatically ordering a specific drink from the local coffee bar down the road at the ideal time.

A cleaner climate
Industrial manufacturing is responsible for nearly 24% of global carbon emissions according to the EPA and the manufacturing sector continues to face challenges related to operational inefficiencies.

New legislation is urging automotive manufacturers to pivot towards manufacturing exclusively EVs.  Implementing AI during the production process results in a self-regulating factory correcting errors before they arise, reducing waste and mitigating needless energy and nonrenewable resource expenditures.

NVH (Noise, Vibration, Harshness) Analysis
Reducing NVH in new vehicles is an essential part of the design process, to ensure that a vehicle is created that not only looks good but is enjoyable to drive. Engineers can deploy automated processes by leveraging historical NVH data using artificial intelligence (AI) and machine learning (ML) techniques. This solution enables automotive manufacturers to shorten development cycles and increase overall vehicle and process efficiency.

Speed to market
The automotive industry has a historically slow design cycle, but deploying AI for EV manufacturing is changing that. By expediting design iteration, AI enables greater flexibility and provides manufacturers with more options without the need for tedious back-and-forth adjustments. AI condenses production into a seamless process that self-regulates to meet demand as needed.

Toyota Research Institute (TRI) recently announced a new algorithm to accelerate the design process of new vehicles. This milestone technology will use AI to cut down the iterations needed to reconcile design and engineering considerations, helping Toyota make EVs faster with increased efficiency.

Conclusion
Deploying AI within the automotive industry improves not only mobility infrastructure, but the process of manufacturing itself. Machine learning expedites innovation and accelerates productivity while also enabling ease of operation for users through adaptive control mechanisms, connecting vehicles to the continually expanding realm of autonomous self-improvement.  These advancements augur an exciting future for EVs that will allow manufacturers to expand the possibilities of functionality and ultimately redefine vehicle ownership.