论文标题
自动驾驶汽车中轨迹预测指标的任务与任务相关的故障检测
Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehicles
论文作者
论文摘要
在现代自治堆栈中，预测模块对于在其他移动代理的存在下计划动作至关重要。但是，预测模块的失败会误导下游计划者做出不安全的决定。确实，轨迹预测任务固有的高度不确定性确保了这种错误预测经常发生。由于需要提高自动驾驶汽车安全性而不会损害其性能的动机，我们开发了一个概率的运行时监视器，该监视器检测到何时发生“有害”预测故障发生，即，与任务相关的失败检测器。我们通过将轨迹预测错误传播到计划成本来推理其对AV的影响来实现这一目标。此外，我们的检测器还配备了假阳性和假阴性速率的性能度量，并允许进行无数据校准。在我们的实验中，我们将检测器与其他各种检测器进行了比较，发现我们的检测器在接收器操作员特征曲线下具有最高的面积。
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.
