Three Questions for Gilles Mabire
CTO Automotive
How important are AI technologies for Continental?
Artificial intelligence offers huge potential for the development of innovative applications in autonomous driving, but also for greater efficiency in research and production. Having already acquired years of experience in the use of AI thanks to our high-performance computers, we are able to shorten development times and thus also contribute to our sustainability strategy. Autonomous driving and assistance systems must be trained with the help of countless test drives. By using AI for this purpose, we can prevent test vehicles from racking up unnecessary miles. At the same time, AI can help further improve the quality of our products and generate competitive advantages. Safe, highly automated and autonomous driving is highly dependent on AI. Who better to spearhead its development than Continental, a company that has been improving road safety for more than 150 years?
In which areas and for which products does Continental currently use AI?
AI is used in production processes such as the monitoring and quality control of electronic components, as well as in the form of collaborative robots, which are able to support and relieve the burden on plant employees in increasingly complex manufacturing processes thanks to machine learning. Machines are even able to learn from mistakes and automatically optimize their behavior. When it comes to products, the focus is on the development of intelligent assistance systems and technologies for autonomous driving, such as computer vision.
How does Continental ensure the reliability of AI technology in autonomous driving, for example?
Continental is a key player when it comes to AI safety in Germany, but we can’t tackle the issue alone. That’s why it’s so important we take part in projects such as the public private partnership for AI security. For 36 months, we worked together with more than 20 partners to agree on a general safety strategy for vehicle AI functions. We achieved a great deal, but the project was only a first step. Various follow-up projects are already in the pipeline to develop effective machine learning models for other autonomous driving functions, such as the prediction and planning of corners and sensor fusion.
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