Stress Testing Autonomous Racing Overtake Maneuvers with RRT
This addresses safety verification for autonomous racing systems, which is an incremental improvement in testing methods for a domain-specific application.
The paper tackled the problem of ensuring safety in high-performance autonomous systems by stress testing autonomous racing overtake maneuvers using an RRT-based approach, resulting in the focused RRT search finding several times more crashes than random strategies, with tens to hundreds of times more crashes for certain planners in specific track sections.
High-performance autonomy often must operate at the boundaries of safety. When external agents are present in a system, the process of ensuring safety without sacrificing performance becomes extremely difficult. In this paper, we present an approach to stress test such systems based on the rapidly exploring random tree (RRT) algorithm. We propose to find faults in such systems through adversarial agent perturbations, where the behaviors of other agents in an otherwise fixed scenario are modified. This creates a large search space of possibilities, which we explore both randomly and with a focused strategy that runs RRT in a bounded projection of the observable states that we call the objective space. The approach is applied to generate tests for evaluating overtaking logic and path planning algorithms in autonomous racing, where the vehicles are driving at high speed in an adversarial environment. We evaluate several autonomous racing path planners, finding numerous collisions during overtake maneuvers in all planners. The focused RRT search finds several times more crashes than the random strategy, and, for certain planners, tens to hundreds of times more crashes in the second half of the track.