Autonomous vehicles (AVs) are being increasingly deployed in urban environments. In order to operate safely and reliably, AVs need to account for the inherent uncertainty associated with perceiving the world through sensor data and incorporate that into their decision-making process. Uncertainty-aware planners have recently been developed to account for upstream perception and prediction uncertainty. However, such planners may be sensitive to prediction uncertainty miscalibration, the magnitude of which has not yet been characterized. Towards this end, we perform a detailed analysis on the impact that perceptual uncertainty propagation and calibration has on perception-based motion planning. We do so by comparing two novel prediction-planning pipelines with varying levels of uncertainty propagation on the recently-released nuPlan planning benchmark. We study the impact of upstream uncertainty calibration using closed-loop evaluation on the nuPlan challenge scenarios. We find that the method incorporating upstream uncertainty propagation demonstrates superior generalization to complex closed-loop scenarios.

翻译：自动驾驶车辆正越来越多地部署于城市环境中。为确保安全可靠运行，自动驾驶车辆必须考虑通过传感器数据感知世界时固有的不确定性，并将其纳入决策过程。近期已开发出能够处理上游感知与预测不确定性的不确定性感知规划器。然而，此类规划器可能对预测不确定性的校准误差较为敏感，而此类误差的影响程度尚未得到系统量化。为此，我们针对感知不确定性传播及其校准对基于感知的运动规划的影响进行了详细分析。我们在最新发布的nuPlan规划基准上，通过比较两种具有不同不确定性传播层级的新型预测-规划流程展开研究。我们利用nuPlan挑战场景的闭环评估，系统研究了上游不确定性校准的影响。研究发现，采用上游不确定性传播的方法在复杂闭环场景中展现出更优越的泛化性能。