用扩散模型生成碰撞噪声，提升自动驾驶在路口的鲁棒性
Robust Planning for Autonomous Vehicles with Diffusion-Based Failure Samplers
- 用1000步扩散模型生成导致碰撞的传感器噪声序列
- 将模型压缩为单步推理，速度更快且采样质量相近
- 用于规划器实时采样潜在故障，降低事故率与延迟
高风险交通区域如十字路口是事故的主要原因。本研究利用深度生成模型提升自动驾驶车辆在四向交叉路口的行车安全。我们训练了一个1000步去噪扩散概率模型，根据当前入侵车辆的相对位置和速度，生成可能导致碰撞的传感器噪声序列。通过生成对抗架构，将该1000步模型蒸馏为单步去噪扩散模型，在保持相似采样质量的同时实现快速推理。我们展示了该单步模型在构建鲁棒规划器中的一个应用：规划器基于当前交通状态，高效采样潜在故障情形以辅助决策。仿真实验表明，该鲁棒规划器相比基线智能驾驶员模型控制器，显著降低了失败率与延迟率。
原文摘要 · Abstract (English)
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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