Continental joins UC Berkeley's DeepDrive program

DeepDrive aims to develop AI for use in connected vehicles and transport

Tags: Artifical intelligenceConnected vehiclesContinental Automotive Group (https://www.continental-automotive.com/)USAUniversity of California (universityofcalifornia.edu/)
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Continental joins UC Berkeley's DeepDrive program Continental has joined UC Berkeley's DeepDrive program.
By  Mark Sutton Published  July 31, 2018

Continental has joined the University of California, Berkeley's DeepDrive program, to develop AI for transport solutions.

The automotive manufacturing company will join other companies in the sector in working with Berkeley to develop state-of-the-art technologies for machine seeing and learning in automotive applications.

The research partnership with Continental focuses on optimizing the speed of neural networks, as well as protecting AI systems in safety-critical applications, with the aim of bringing the technology into production as soon as possible.

The collaboration will initially focus on developing reliable testing for AI, to ensure that smart driving solutions are actually functioning as they should. The program will also look at efficiency and optimisation of AI to make it easier to deploy.

"We are joining forces with the world's leading AI researchers," said Demetrio Aiello, Head of Continental's Corporate Artificial Intelligence and Robotics Lab. "Building on the momentum of our strategic partnerships with the University of Oxford, DFKI (German Research Center for Artificial Intelligence) and other AI thought leaders, we have signed a five-year agreement to be members of the UC Berkeley DeepDrive (BDD) center."

Berkeley DeepDrive is a multidisciplinary centre, managed by the Institute of Transportation Studies at the University of California, Berkeley. Industry sponsors support the program to help bring new technologies to automotive applications. Professor Trevor Darrell, also Director of the "Partners for Advanced Transportation Technology" (PATH) program, leads the group.

"Having Continental as a BDD member is something we are very proud of. We are excited to be working closely together to develop innovative solutions," said Darrell. "Continental is a leader in the automotive industry and BDD is opening up opportunities for artificial intelligence and autonomous driving in automotive applications, which makes for a great team."

In the first year of the program membership, Continental and BDD are focused on two fields of research. First is the testability of AI algorithms in safety-relevant systems. Drivers need to be sure that the complex technology in their vehicles will work properly, so BDD is developing methods that will allow the reliability of AI systems to be tested more efficiently. The researchers at the centre are also looking at how to operate AI applications in a memory-efficient way to accelerate and optimize neural networks. This will allow easier implementation of AI methods in vehicles.

"As is the case for BDD, Continental is also at the international peak of the AI revolution in the industry," explained Dr Stefan Voget, who heads up the relationship with UC Berkeley on behalf of the technology company. "Together, we can drive mobility forward faster than doing it alone."

"What inspired us most to team up with the experts in Silicon Valley and UC Berkeley was the highly interesting research in the field of Explainable AI as well as the optimization of deep neural networks that were taking place there," Aiello said. Explainable AI focuses on understanding precisely how an AI system makes decisions. To test artificial intelligence in detail, experts must know exactly how it works. In addition to the benefits of the research itself, Aiello shared another important advantage of the membership. "The opportunity to have colleagues from Silicon Valley and other Continental locations working as part of BDD research teams enables more efficient collaboration and transfer of expertise. It also allows us to identify the talent we need for our AI strategy at an early stage."

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