Every automaker is working on driver assistance systems and self-driving cars. Conventional computer vision used for ADAS is reaching its threshold because it is impossible to write code for every possible scenario as a vehicle navigates. In order to develop a truly autonomous car, deep learning and artificial intelligence are required. With deep learning, the vehicle can be trained to have super human levels of perception, driving safer than anyone on the road. An end-to-end artificial intelligence platform based on supercomputers in the cloud and in the vehicle enables cars to get smarter and smarter. Coupled with an extensive software development kit with vision and AI libraries and software modules, automakers, tier 1s, and startups can build scalable systems from ADAS to full autonomy.

Speaker Bio

Danny Shapiro is Senior Director of NVIDIA’s Automotive Business Unit, focusing on solutions that enable faster and better design of automobiles, as well as in-vehicle solutions for self-driving cars, infotainment systems, and digital instrument clusters. Danny holds a BSE in Electrical Engineering and Computer Science from Princeton University and an MBA from the Hass School of Business at UC Berkeley. Danny serves on the Advisory Boards for the LA Auto Show, the Connected Car Council and the NVIDIA foundation that focuses on computational solutions for cancer research.