Parameter-Conditioned Reachable Sets for Updating Safety Assurances Online

TL;DR

This paper introduces a method for real-time safety assurance updates in autonomous systems by precomputing parameter-conditioned reachable sets, allowing quick adaptation to system and environment changes without redoing complex calculations.

Contribution

The paper proposes a novel approach to online safety assurance by precomputing parameter-conditioned reachable sets, enabling efficient real-time safety updates for evolving autonomous systems.

Findings

Efficient online safety updates via precomputed reachable sets.

Maintains safety despite system and environment changes.

Validated through simulation studies.

Abstract

Hamilton-Jacobi (HJ) reachability analysis is a powerful tool for analyzing the safety of autonomous systems. However, the provided safety assurances are often predicated on the assumption that once deployed, the system or its environment does not evolve. Online, however, an autonomous system might experience changes in system dynamics, control authority, external disturbances, and/or the surrounding environment, requiring updated safety assurances. Rather than restarting the safety analysis from scratch, which can be time-consuming and often intractable to perform online, we propose to compute \textit{parameter-conditioned} reachable sets. Assuming expected system and environment changes can be parameterized, we treat these parameters as virtual states in the system and leverage recent advances in high-dimensional reachability analysis to solve the corresponding reachability problem offline. This results in a family of reachable sets that is parameterized by the environment and system factors. Online, as these factors change, the system can simply query the corresponding safety function from this family to ensure system safety, enabling a real-time update of the safety assurances. Through various simulation studies, we demonstrate the capability of our approach in maintaining system safety despite the system and environment evolution.