安全な人とロボットのインタラクションのための責任配分の学習：自動運転への応用
Learning Responsibility Allocations for Safe Human-Robot Interaction with Applications to Autonomous Driving
自動運転車が人間ドライバーと安全に相互作用するため、責任配分をデータから学習するResponsibility-Aware Control Barrier Functions (RA-CBFs)を提案し、実世界の運転データで有効性を示した。
著者: Ryan K. Cosner, Yuxiao Chen, Karen Leung, Marco Pavone
分類: cs.RO
原文アブストラクト
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.
