Responsibility Sensitive Safety of Self-Driving Cars

Abstract: In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of the talk is to add to the equation two additional crucial parameters. The first is standardization of safety assurance --- what are the minimal requirements that every self-driving car must satisfy, and how can we verify these requirements. The second parameter is scalability --- engineering solutions that lead to unleashed costs will not scale to millions of cars, which will push interest in this field into a niche academic corner, and drive the entire field into a "winter of autonomous driving". In the first part of the talk I will show why statistical guarantees give a very weak safety and will propose instead a white-box, interpretable, mathematical model for safety assurance, which we call Responsibility-Sensitive Safety (RSS). Time permitting, the second part of the talk will involve reinforcement learning techniques for scalable driving policy.

Joint work with Shaked Shammah and Amnon Shashua.

Bio:
Shai Shalev-Shwartz is a VP Technology at Mobileye, a Senior Fellow at Intel, and a professor in the Faculty of Computer Science and Engineering at the Hebrew University of Jerusalem. Prof. Shalev-Shwartz is the author of the book “Online Learning and Online Convex Optimization,” and a co-author of the book “Understanding Machine Learning: From Theory to Algorithms”. His research is around the theoretical foundations of machine learning as well as crafting theoretically justified optimization algorithms that make learning more efficient. He received several best paper awards, was listed in the Aminer top 100 most influential scholar list of 2016, and was listed at the 17'th place in The-Marker's top 100 most influential people in Israel, under the title “the brain behind artificial intelligence”, acknowledging his contributions to Mobileye's technology.