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AI-INFUSED INNOVATION FOR
AUTOMOTIVE DATA

Jean-Marie Lapeyre
14 Jul 2022

AI is integral to many current production-line automation initiatives designed to increase efficiency and quality.

The automotive industry is innovating rapidly in response to multiple disruptions. To focus first on the customer perspective, vehicle buyers increasingly want the whole purchasing experience to happen online, from initial research right through to buying. The ownership model, too, looks set to change, with the increasing use of carpooling, ride-hailing services, short-term rentals, and community fleets. And relationships between manufacturers and customers are being extended: drivers will continue to receive services and over-the-air updates throughout their vehicle’s life.

Frugal innovation through applying AI to existing data

Innovation is critical to dealing with industry changes, but innovation does not necessarily mean invention; often, it’s about reusing an idea or a resource in a different context. This frugal approach to innovation, called ”Jugaad” in India, is a theme of Capgemini’s TechnoVision for Automotive 2022 playbook. The application of AI is a prime example of how the automotive industry is innovating by making better use of what it has. Of course, AI needs data, and this is where the frugality comes in, because much of the necessary data often exists already. For example, data generated by designing and building a vehicle was often discarded after the product’s completion, but with software increasingly determining which options an individual vehicle offers, its value is clear. AI can help companies make the most of data in a range of contexts.

Inside the vehicle: Perhaps the best-known use of AI in automotive is to automate the task of driving. Even if fully autonomous vehicles are still a few years away, Advanced Driver-Assistance Systems (ADAS) features are already appearing. AI can open up a whole world of seamless driver interactions and can support safety, reliability, and robustness.

On the factory floor: AI is integral to many current production-line automation initiatives designed to increase efficiency and quality. AI-based systems can help to analyze camera outputs, carry out shop-floor quality checks on the assembly line, optimize truck loading to improve space utilization, or power augmented-reality goggles to minimize operator errors. In the design workshop: With AI, revolutionary propositions can emerge from data, including new elements for use by human designers. AI can also evaluate solutions generated by humans or machines and recommend the most promising.

In the back office: For strategic planning purposes, AI-enabled processes can assist with rationalizing the choice of vehicle configurations. AI could even help the human resources function because, when talent is scarce, AI can make the most of the people you have.

AI helps optimize ADAS General Motors is assessing the potential of an AI-enabled pattern recognition technology to accelerate the design of ADAS. The Multi-node Evolutionary Neural Networks for Deep Learning rapidly evaluates convolutional neural networks for use in pattern recognition. This approach could, for instance, reveal ways for cars to quickly and accurately assess their surroundings in order to navigate safely through them.

Sharing safety data Capgemini has been working with Volkswagen and Audi to demonstrate the value of the German Federal Government’s Mobility Data Space, of which Volkswagen Group is a founding member. An early use case is Local Hazard Information, which provides aggregated event data on traffic hazards collected from vehicle sensors in the Audi fleet. This data could be used by a navigation service to warn road users of upcoming danger spots in near real-time.

Driving into the future with data and AI

The industry needs to make sure that the data required to power AI is available in the right form, at the right place, and at the right time. Various data-sharing initiatives are underway to help this happen. Once we organize the data correctly and make it available for the right AI applications, the sky’s the limit – perhaps literally. AI-enabled progress in drones could lead to the development of cars that are not only autonomous but also hover in the air. Watch this space!

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 4 features 18 such articles crafted by leading Capgemini and partner experts sharing inspiring examples of it – ranging from digital twins in the industrial metaverse, “humble” AI, serendipity in user experiences, all the way up to permacomputing and the battle against data waste.. In addition, several articles are in collaboration with key technology partners such as  AlationCogniteToucan TocoDataRobot, and The Open Group to reimagine what’s possible. 

Author

Jean-Marie Lapeyre

VP and Chief Technology & Innovation Officer, Global Automotive Industry
Jean-Marie Lapeyre works with automotive clients to develop and launch actionable technology strategies to help them succeed in a data- and software-driven world.