In modern autonomy stacks, prediction modules are paramount to planning motions in the presence of other mobile agents. However, failures in prediction modules can mislead the downstream planner into making unsafe decisions. Indeed, the high uncertainty inherent to the task of trajectory forecasting ensures that such mispredictions occur frequently. Motivated by the need to improve safety of autonomous vehicles without compromising on their performance, we develop a probabilistic run-time monitor that detects when a "harmful" prediction failure occurs, i.e., a task-relevant failure detector. We achieve this by propagating trajectory prediction errors to the planning cost to reason about their impact on the AV. Furthermore, our detector comes equipped with performance measures on the false-positive and the false-negative rate and allows for data-free calibration. In our experiments we compared our detector with various others and found that our detector has the highest area under the receiver operator characteristic curve.

翻译：在现代自动驾驶技术栈中，预测模块对于在存在其他移动主体的情况下规划运动至关重要。然而，预测模块的故障可能误导下游规划器做出不安全决策。事实上，轨迹预测任务固有的高度不确定性确保了此类预测错误频繁发生。为在提升自动驾驶车辆安全性且不牺牲性能的需求驱动下，我们开发了一种概率运行时监控器，用于检测"有害"预测故障发生的情况，即一种任务相关故障检测器。我们通过将轨迹预测误差传播至规划成本，以推理其对自动驾驶车辆的影响来实现这一目标。此外，我们的检测器配备了关于假阳性率和假阴性率的性能度量，并支持无数据校准。在实验中，我们将所提检测器与多种其他检测器进行对比，发现其拥有最高的受试者工作特征曲线下面积。