Robust Planning for Autonomous Vehicles with Diffusion-Based Failure Samplers

• URL: http://arxiv.org/abs/2507.11991v1
• Date: Wed, 16 Jul 2025 07:43:55 GMT
• Title: Robust Planning for Autonomous Vehicles with Diffusion-Based Failure Samplers
• Authors: Juanran Wang, Marc R. Schlichting, Mykel J. Kochenderfer,
• Abstract summary: High-risk traffic zones such as intersections are a major cause of collisions.<n>This study leverages deep generative models to enhance the safety of autonomous vehicles in an intersection context.
• Score: 36.896695278624776
• License: http://creativecommons.org/licenses/by/4.0/
• 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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