Autonomous vehicle safety testing acceleration: A new diffusion-reachability framework for high-risk powered two-wheeler interactions.

Abstract

Safe operation of Autonomous Vehicles (AVs) in complex traffic scenarios remains a challenge, particularly in interactions with Powered Two-Wheelers (PTWs), whose riders represent a common and vulnerable group of road users. This study proposes DRIVE (Diffusion-Reachability-based Interaction and Validation for Efficient scenario testing), an integrated framework for risk-oriented accelerated safety testing in car-PTW interactions. The framework combines diffusion-based trajectory generation, backward reachability analysis, and reachability-guided sampling to construct and prioritize safety-critical scenarios. Based on 314 reconstructed real-world crashes from the in-depth crash database, a diffusion model produced 6280 realistic interaction trajectories. The generated trajectories were represented using a truncated Gaussian mixture model and organized into five risk levels through reachability analysis. A reachability-guided sampling scheme then allocated simulation effort to dynamically critical regions. Simulation tests with a production-level automated driving system show that DRIVE yields crash rates of 84.00%-94.47% in the targeted risk sets, compared with 15.29% for replayed crashes and 12.53%-24.80% for baseline generative methods. At the same time, DRIVE covers a wider range of severe crashes. These results show that DRIVE improves failure discovery and targeted stress-testing efficiency under a fixed simulation budget, so that comparable safety conclusions can be drawn from far fewer simulations in PTW interaction scenarios.