Invited Talk: Marco Pavone, Stanford

Title: On safe and efficient human-robot vehicle interactions via CVAE-based intent modeling and reachability-based safety assurance

Abstract: In this talk I will present a decision-making and control stack for human-robot vehicle interactions. I will first discuss a data-driven approach for learning interaction dynamics between robot-driven and human-driven vehicles, based on recent advances in the theory of conditional variational autoencoders (CVAEs). I will then discuss how to incorporate such a learned interaction model into a real-time, intent-aware decision-making framework, with an emphasis on minimally-interventional strategies rooted in backward reachability analysis for ensuring safety even when other cars defy the robot's predictions. Experiments on a full-scale steer-by-wire platform entailing traffic weaving maneuvers demonstrate how the proposed autonomy stack enables more efficient and anticipative autonomous driving behaviors, while avoiding collisions even when the other cars defy the robot’s predictions and take dangerous actions.