Being able to anticipate the motion of surrounding agents is essential for the safe operation of autonomous driving systems in dynamic situations. While various methods have been proposed for trajectory prediction, the current evaluation practices still rely on error-based metrics (e.g., ADE, FDE), which reveal the accuracy from a post-hoc view but ignore the actual effect the predictor brings to the self-driving vehicles (SDVs), especially in complex interactive scenarios: a high-quality predictor not only chases accuracy, but should also captures all possible directions a neighbor agent might move, to support the SDVs' cautious decision-making. Given that the existing metrics hardly account for this standard, in our work, we propose a comprehensive pipeline that adaptively evaluates the predictor's performance by two dimensions: accuracy and diversity. Based on the criticality of the driving scenario, these two dimensions are dynamically combined and result in a final score for the predictor's performance. Extensive experiments on a closed-loop benchmark using real-world datasets show that our pipeline yields a more reasonable evaluation than traditional metrics by better reflecting the correlation of the predictors' evaluation with the autonomous vehicles' driving performance. This evaluation pipeline shows a robust way to select a predictor that potentially contributes most to the SDV's driving performance.

翻译：在动态环境中，能够预测周围智能体的运动对于自动驾驶系统的安全运行至关重要。尽管已提出多种轨迹预测方法，当前的评估实践仍依赖于基于误差的指标（例如ADE、FDE），这些指标从后验角度揭示准确性，但忽略了预测器对自动驾驶车辆（SDV）的实际影响，尤其是在复杂的交互场景中：高质量的预测器不仅追求精度，还应捕捉邻近智能体可能移动的所有可能方向，以支持自动驾驶车辆的谨慎决策。鉴于现有指标难以考量这一标准，我们在工作中提出一个综合评估流程，通过两个维度自适应地评估预测器的性能：准确性与多样性。基于驾驶场景的临界性，这两个维度被动态结合，并生成预测器性能的最终评分。在基于真实世界数据集的闭环基准测试上进行的大量实验表明，我们的流程通过更好地反映预测器评估与自动驾驶车辆驾驶性能之间的相关性，提供了比传统指标更合理的评估。该评估流程展示了一种稳健的方法，以选择对自动驾驶车辆驾驶性能潜在贡献最大的预测器。