Safety and performance are key enablers for autonomous driving: on the one hand we want our autonomous vehicles (AVs) to be safe, while at the same time their performance (e.g., comfort or progression) is key to adoption. To effectively walk the tight-rope between safety and performance, AVs need to be risk-averse, but not entirely risk-avoidant. To facilitate safe-yet-performant driving, in this paper, we develop a task-aware risk estimator that assesses the risk a perception failure poses to the AV's motion plan. If the failure has no bearing on the safety of the AV's motion plan, then regardless of how egregious the perception failure is, our task-aware risk estimator considers the failure to have a low risk; on the other hand, if a seemingly benign perception failure severely impacts the motion plan, then our estimator considers it to have a high risk. In this paper, we propose a task-aware risk estimator to decide whether a safety maneuver needs to be triggered. To estimate the task-aware risk, first, we leverage the perception failure - detected by a perception monitor - to synthesize an alternative plausible model for the vehicle's surroundings. The risk due to the perception failure is then formalized as the "relative" risk to the AV's motion plan between the perceived and the alternative plausible scenario. We employ a statistical tool called copula, which models tail dependencies between distributions, to estimate this risk. The theoretical properties of the copula allow us to compute probably approximately correct (PAC) estimates of the risk. We evaluate our task-aware risk estimator using NuPlan and compare it with established baselines, showing that the proposed risk estimator achieves the best F1-score (doubling the score of the best baseline) and exhibits a good balance between recall and precision, i.e., a good balance of safety and performance.

翻译：安全与性能是自主驾驶的关键推动因素：一方面我们希望自动驾驶车辆(autonomous vehicles, AVs)安全，同时其性能（如舒适性或行进效率）是部署应用的核心。为有效平衡安全与性能，自动驾驶车辆需具备风险规避意识，但并非完全风险回避。为实现安全而高效的驾驶，本文开发了一种面向任务的风险估计器，用于评估感知失效对自车运动规划所构成的风险。若感知失效对自车运动规划的安全性无影响，则无论该失效多么严重，所提出的面向任务风险估计器均将其视为低风险；反之，若看似微小的感知失效严重影响了运动规划，则估计器将其视为高风险。本文提出面向任务的风险估计器以判定是否需要触发安全操控。为估计该风险，首先利用感知监测器检测到的感知失效，合成车辆周围环境的替代合理模型。继而将感知失效导致的风险形式化为"相对"风险——即感知场景与替代合理场景对自车运动规划的风险差异。我们采用名为copula的统计工具来估计此风险，该工具可建模分布间的尾部依赖关系。copula的理论特性使我们能够计算风险的概率近似正确(PAC)估计值。我们基于NuPlan数据集对面向任务风险估计器进行评估，并与既有基线方法进行比较。结果表明，所提出的风险估计器取得了最优F1分数（是最佳基线得分的两倍），并在召回率与精确率之间展现出良好平衡，即实现了安全性与性能的有效平衡。