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arXiv · 2507.11991

Robust Planning for Autonomous Vehicles with Diffusion-Based Failure Samplers

Juanran Wang·Marc R. Schlichting·Mykel J. Kochenderfer
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Abstract

High-risk traffic zones such as intersections are a major cause of collisions. This study leverages deep generative models to enhance the safety of autonomous vehicles in an intersection context. We train a 1000-step denoising diffusion probabilistic model to generate collision-causing sensor noise sequences for an autonomous vehicle navigating a four-way intersection based on the current relative position and velocity of an intruder. Using the generative adversarial architecture, the 1000-step model is distilled into a single-step denoising diffusion model which demonstrates fast inference speed while maintaining similar sampling quality. We demonstrate one possible application of the single-step model in building a robust planner for the autonomous vehicle. The planner uses the single-step model to efficiently sample potential failure cases based on the currently measured traffic state to inform its decision-making. Through simulation experiments, the robust planner demonstrates significantly lower failure rate and delay rate compared with the baseline Intelligent Driver Model controller.

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Juanran Wang, Marc R. Schlichting, Mykel J. Kochenderfer. 2025-07-16. Robust Planning for Autonomous Vehicles with Diffusion-Based Failure Samplers. https://arxiv.org/abs/2507.11991

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