(video) Safe and Efficient Reinforcement Learning for Behavioral Planning in Autonomous Driving, E. Leurent

Workshop: Collaborative Perception & Federated ML for Autonomous Driving
Link : https://sites.google.com/view/cofed-dlad-2020/home
Speaker : Edouard Leurent, PhD Student in Reinforcement Learning, Inria SequeL Team, Inria Valse Team, Renault Group
Title : Safe and Efficient Reinforcement Learning for Behavioral Planning in Autonomous Driving
Abstract : The task of behavioural planning in a dense traffic setting consists in an autonomous agent driving among many other vehicles whose behaviors are uncertain, and whose interactions induce complex couplings in the dynamics of the nearby trafic. Ensuring safety thus requires an accurate understanding of these interactions to produce tight and exhaustive probabilistic trajectory forecasts. On the other hand, the safety requirement can often lead to over-conservative behaviors, which motivates the design of learning algorithms that can adapt to any desired level of risk.