Trajectory prediction plays a vital role in the performance of autonomous driving systems, and prediction accuracy, such as average displacement error (ADE) or final displacement error (FDE), is widely used as a performance metric. However, a significant disparity exists between the accuracy of predictors on fixed datasets and driving performance when the predictors are used downstream for vehicle control, because of a dynamics gap. In the real world, the prediction algorithm influences the behavior of the ego vehicle, which, in turn, influences the behaviors of other vehicles nearby. This interaction results in predictor-specific dynamics that directly impacts prediction results. In fixed datasets, since other vehicles' responses are predetermined, this interaction effect is lost, leading to a significant dynamics gap. This paper studies the overlooked significance of this dynamics gap. We also examine several other factors contributing to the disparity between prediction performance and driving performance. The findings highlight the trade-off between the predictor's computational efficiency and prediction accuracy in determining real-world driving performance. In summary, an interactive, task-driven evaluation protocol for trajectory prediction is crucial to capture its effectiveness for autonomous driving. Source code along with experimental settings is available online.

翻译：轨迹预测对自主驾驶系统的性能至关重要，平均位移误差（ADE）或最终位移误差（FDE）等预测准确性指标被广泛用作性能度量。然而，由于动力学差异，预测器在固定数据集上的准确性与将其下游用于车辆控制时的驾驶性能之间存在显著差距。在现实世界中，预测算法会影响自车的驾驶行为，进而影响附近其他车辆的行为。这种交互作用会产生直接影响预测结果的预测器特异性动力学。在固定数据集中，由于其他车辆的响应是预设的，这种交互效应会丢失，从而导致显著的动力学差异。本文研究了这一被忽视的动力学差异的重要性，同时考察了导致预测性能与驾驶性能差距的其他若干因素。研究发现揭示了预测器的计算效率与预测准确性在决定实际驾驶性能之间存在权衡。总之，对于轨迹预测而言，交互式、任务驱动的评估协议对于捕捉其在自主驾驶中的有效性至关重要。源代码及实验设置已在线公开。