Learning Responsibility Allocations for Safe Human-Robot Interaction with Applications to Autonomous Driving

TL;DR

This paper introduces Responsibility-Aware Control Barrier Functions (RA-CBFs) to learn responsibility sharing in autonomous driving, enabling safer interactions with humans by adapting to scene-specific responsibilities without overly conservative assumptions.

Contribution

The paper presents a novel method combining safety-critical control and learning to allocate responsibilities dynamically in multi-agent driving scenarios.

Findings

RA-CBFs effectively learn responsibility allocations from real-world data.

The approach improves safety and efficiency in autonomous driving interactions.

Framework aids forensic analysis of unsafe driving incidents.

Abstract

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.