Risk-based Socially-Compliant Behavior Planning for Autonomous Driving

In this study, we introduce an innovative risk-aware behavior planning framework designed for autonomous driving, with the aim of fostering socially compliant vehicle behavior in diverse mixed-traffic highway scenarios. Our objective is to empower autonomous vehicles to exhibit behavior that aligns with societal norms, thus enhancing their acceptability among human drivers. We expand the scope of Control Barrier Function-inspired risk assessment to encompass a heteroge-neous spectrum of road participants, allowing us to explicitly model varying degrees of social influences between different classes of vehicles. We also present a mathematical condition for accountability tracing, enabling the identification of responsible entities in situations where risks surge. Drawing inspiration from Isaac Asimov's “Three Laws of Robotics,” we establish social compliance conditions grounded in our unique risk concept, which seamlessly integrates with a wide range of existing safety-critical controllers, regardless of their type or design. By incorporating these conditions, which encode societal expectations, into existing safe controllers, we demonstrate that autonomous vehicles can exhibit context-aware behavior with-out compromising the safety guarantees provided by existing controllers. This approach effectively excludes behaviors that may be safe but do not align with human intuition while guaranteeing the least interference with the existing controller.