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：在现代自动驾驶系统中，轨迹预测模块对于在存在其他移动代理时规划运动至关重要。然而，预测模块的故障可能会误导下游的规划器做出不安全的决策。事实上，轨迹预测任务自身具有较高的不确定性，因此这种错误预测经常发生。为了提高自动驾驶车辆的安全性而又不影响其性能，我们开发了一种概率运行时监控器，用于检测何时发生“有害”的故障，即任务相关故障检测。我们通过将轨迹预测误差传播到规划成本中来实现这一点，以推断其对自动驾驶车辆的影响。此外，我们的检测器配备了假阳性率和假阴性率的性能指标，并允许无需数据校准。在我们的实验中，我们将我们的检测器与其他各种检测器进行了比较，并发现我们的检测器具有最高的接受者操作特征曲线下面积。