用核方法高效评估障碍物风险，提升自动驾驶避撞安全性。
MMD-OPT : Maximum Mean Discrepancy Based Sample Efficient Collision Risk Minimization for Autonomous Driving
- 基于核空间的分布差异度量，构建低样本下的碰撞风险代理模型。
- 在低样本场景下，避撞轨迹安全性优于基于CVaR的方法。
- 适用于动态障碍物预测分布不确定的自动驾驶决策场景。
我们提出MMD-OPT：一种在任意动态障碍物预测分布下实现样本高效的碰撞风险最小化方法。该方法基于再生核希尔伯特空间（RKHS）中的分布嵌入与最大均值差异（MMD）理论，构建了碰撞风险估计的高效替代指标。通过在合成数据和真实世界数据集上的大量仿真验证，证明了MMD-OPT的有效性。尤为重要的是，在低样本条件下，采用MMD-based风险代理进行轨迹优化，相比基于条件风险价值（CVaR）的主流方法，能生成更安全的行驶路径。
原文摘要 · Abstract (English)
We propose MMD-OPT: a sample-efficient approach for minimizing the risk of collision under arbitrary prediction distribution of the dynamic obstacles. MMD-OPT is based on embedding distribution in Reproducing Kernel Hilbert Space (RKHS) and the associated Maximum Mean Discrepancy (MMD). We show how these two concepts can be used to define a sample efficient surrogate for collision risk estimate. We perform extensive simulations to validate the effectiveness of MMD-OPT on both synthetic and real-world datasets. Importantly, we show that trajectory optimization with our MMD-based collision risk surrogate leads to safer trajectories at low sample regimes than popular alternatives based on Conditional Value at Risk (CVaR).
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