In the autonomous driving system, trajectory prediction plays a vital role in ensuring safety and facilitating smooth navigation. However, we observe a substantial discrepancy between the accuracy of predictors on fixed datasets and their driving performance when used in downstream tasks. This discrepancy arises from two overlooked factors in the current evaluation protocols of trajectory prediction: 1) the dynamics gap between the dataset and real driving scenario; and 2) the computational efficiency of predictors. In real-world scenarios, prediction algorithms influence the behavior of autonomous vehicles, which, in turn, alter the behaviors of other agents on the road. This interaction results in predictor-specific dynamics that directly impact prediction results. As other agents' responses are predetermined on datasets, a significant dynamics gap arises between evaluations conducted on fixed datasets and actual driving scenarios. Furthermore, focusing solely on accuracy fails to address the demand for computational efficiency, which is critical for the real-time response required by the autonomous driving system. Therefore, in this paper, we demonstrate that an interactive, task-driven evaluation approach for trajectory prediction is crucial to reflect its efficacy for autonomous driving.

翻译：在自动驾驶系统中，轨迹预测在保障安全性和实现平稳导航方面发挥着重要作用。然而，我们观察到预测器在固定数据集上的精度与其在下游任务中的驾驶性能之间存在显著差异。这一差异源于当前轨迹预测评估协议中两个被忽视的因素：1）数据集与真实驾驶场景之间的动态差距；2）预测器的计算效率。在现实场景中，预测算法会影响自动驾驶车辆的行为，进而改变道路上其他智能体的行为。这种交互作用会产生特定于预测器的动态特性，从而直接影响预测结果。由于其他智能体的响应在数据集中是预先确定的，因此在固定数据集上的评估与实际驾驶场景之间存在显著的动态差距。此外，仅关注精度无法满足计算效率的需求，而计算效率对于自动驾驶系统所需的实时响应至关重要。因此，在本文中，我们论证了采用交互式、任务驱动的评估方法对轨迹预测而言至关重要，以反映其对自动驾驶的有效性。