ZF and University of California, Berkeley partner
ZF has entered into a strategic research partnership to coincide with the opening of its Innovation Hub in Silicon Valley. Together with researchers from the University of California, Berkeley, the company plans to harness the self-learning machines that are crucial to fully autonomous driving in the automotive field.
“Our latest research collaboration will significantly boost our Vision Zero Ecosystem in two areas that are key to fully autonomous driving—computer vision and deep learning,” said Dr. Stefan Sommer, CEO of ZF Friedrichshafen AG.
With the newly founded Berkeley Deep Drive (BDD) Center, the university has teamed up with partners such as ZF. The BDD consortium brings together faculties and researchers from several departments and centers to combine the latest technology with real applications in the automotive industry. “Even though dramatic progress has been achieved in fields such as computer vision in many areas of industry in recent years, the applications are yet to reach the automotive industry. We now wish to change this,” said Professor Trevor Darrell, head of the multidisciplinary center.
According to ZF, the highly complex environment in which automobiles move around together with numerous other road users places the highest possible demands on the system algorithms. To make matters more difficult, even though the normal test runs used until now have covered millions of kilometers, they still do not come anywhere close to covering all the conceivable traffic events or risk situations. Different ways must therefore be found to ensure the error-free functionality of the system. Part one of the strategy involves algorithms being capable of optimizing themselves in future via machine learning. To this end, machine learning uses so-called neural networks modeled on the way in which the human brain works. If this involves particularly complex neural networks with many hidden layers, it is known as deep learning. Part two of the strategy involves vehicles equipped with the necessary sensors learning how to achieve ever better outcomes using the incoming data. Individual system adjustments are aggregated in the cloud, optimized once again, and re-sent to the entire vehicle fleet. In this way, both the development pace and quality can be increased on a lasting basis.