Drivers have a responsibility to exercise reasonable care to avoid collision with other road users. This assumed responsibility allows interacting agents to maintain safety without explicit coordination. Thus to enable safe autonomous vehicle (AV) interactions, AVs must understand what their responsibilities are to maintain safety and how they affect the safety of nearby agents. In this work we seek to understand how responsibility is shared in multi-agent settings where an autonomous agent is interacting with human counterparts. We introduce Responsibility-Aware Control Barrier Functions (RA-CBFs) and present a method to learn responsibility allocations from data. By combining safety-critical control and learning-based techniques, RA-CBFs allow us to account for scene-dependent responsibility allocations and synthesize safe and efficient driving behaviors without making worst-case assumptions that typically result in overly-conservative behaviors. We test our framework using real-world driving data and demonstrate its efficacy as a tool for both safe control and forensic analysis of unsafe driving.

翻译：驾驶员有责任采取合理注意以避免与其他道路使用者发生碰撞。这种假设的责任使交互主体无需明确协调即可维持安全。因此，为了实现安全的自动驾驶车辆交互，自动驾驶车辆必须理解其在维持安全方面的责任，以及这些责任如何影响附近主体的安全。在本研究中，我们试图探究在自主代理与人类对手交互的多主体场景中，责任如何被共享。我们引入了责任感知控制障碍函数，并提出了一种从数据中学习责任分配的方法。通过结合安全关键控制与基于学习的技术，责任感知控制障碍函数使我们能够考虑场景依赖的责任分配，并综合安全且高效的驾驶行为，而无需做出通常导致过度保守行为的极端假设。我们使用真实驾驶数据测试了该框架，并证明了其作为安全控制和不安全驾驶取证分析工具的有效性。