Negotiation-Aware Reachability-Based Safety Verification for AutonomousDriving in Interactive Scenarios

TL;DR

This paper introduces a negotiation-aware, reachability-based safety verification method for autonomous driving that adaptively reduces conservativeness by integrating learning-based predictions and game-theoretic human behavior models, validated on real data.

Contribution

It presents a novel approach combining learning and game theory to improve safety verification in autonomous driving by reducing conservativeness while maintaining safety guarantees.

Findings

Effectively reduces conservativeness of safety verification

Maintains safety guarantees with reasonable human behavior assumptions

Validated on real driving data

Abstract

Safety assurance is a critical yet challenging aspect when developing self-driving technologies. Hamilton-Jacobi backward-reachability analysis is a formal verification tool for verifying the safety of dynamic systems in the presence of disturbances. However, the standard approach is too conservative to be applied to self-driving applications due to its worst-case assumption on humans' behaviors (i.e., guard against worst-case outcomes). In this work, we integrate a learning-based prediction algorithm and a game-theoretic human behavioral model to online update the conservativeness of backward-reachability analysis. We evaluate our approach using real driving data. The results show that, with reasonable assumptions on human behaviors, our approach can effectively reduce the conservativeness of the standard approach without sacrificing its safety verification ability.