考虑定位不确定性的自动驾驶风险评估新方法
Belief-Space Residual Risk for Automated Driving under Localization Uncertainty
- 将车辆自身位置不确定性建模为高斯分布，扩展风险计算至信念空间
- 通过粒子滤波框架融合自身与障碍物的不确定性，提升碰撞概率估算精度
- 适用于城市复杂环境和恶劣天气下的自动驾驶安全评估
残余风险指标被用于评估自动驾驶系统的安全性。现有方法通常假设本车位姿确定，主要关注周围物体感知误差和延迟效应。然而在实际中，自动驾驶车辆在复杂城市环境和恶劣天气条件下面临显著的定位不确定性。本文将空间残余风险公式拓展至信念空间，显式地将本车位姿不确定性建模为高斯分布。残余风险被重新定义为在本车位姿信念分布上，由不确定性导致的风险期望值。在基于粒子的风险估计框架中，通过融合本车与目标物体的不确定性协方差，将定位不确定性纳入碰撞概率的计算。
原文摘要 · Abstract (English)
Residual risk metrics have recently been introduced to assess the safety implications of automated driving systems. Existing approaches typically assume a deterministic ego pose and concentrate mainly on perception errors related to surrounding objects and latency effects. In practice, however, automated vehicles operate under considerable localization uncertainty, especially in complex urban settings and in adverse weather conditions. This work extends the spatial residual risk formulation to the belief space by explicitly modeling ego pose uncertainty as a Gaussian distribution. Residual risk is reformulated as the expected degradation-induced risk over the ego pose belief distribution. Within a particle-based risk estimation framework, localization uncertainty is incorporated into the computation of collision probabilities through covariance fusion of ego and object uncertainties.
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