Motion prediction is crucial in enabling safe motion planning for autonomous vehicles in interactive scenarios. It allows the planner to identify potential conflicts with other traffic agents and generate safe plans. Existing motion predictors often focus on reducing prediction errors, yet it remains an open question on how well they help identify the conflicts for the planner. In this paper, we evaluate state-of-the-art predictors through novel conflict-related metrics, such as the success rate of identifying conflicts. Surprisingly, the predictors suffer from a low success rate and thus lead to a large percentage of collisions when we test the prediction-planning system in an interactive simulator. To fill the gap, we propose a simple but effective alternative that combines a physics-based trajectory generator and a learning-based relation predictor to identify conflicts and infer conflict relations. We demonstrate that our predictor, P4P, achieves superior performance over existing learning-based predictors in realistic interactive driving scenarios from Waymo Open Motion Dataset.

翻译：动态预测对于在互动情景下为自主车辆进行安全机动规划至关重要。 它让规划者能够识别与其他交通代理的潜在冲突并生成安全计划。 现有的运动预测者通常侧重于减少预测错误, 但对于它们如何帮助确定规划者的冲突,仍是一个未决问题。 在本文中,我们通过与冲突有关的新指标,例如确定冲突的成功率,评估最先进的预测者。 令人惊讶的是,预测者的成功率很低,因此当我们在互动模拟器中测试预测规划系统时,会导致很大比例的碰撞。 为了填补空白,我们提出了一个简单而有效的替代办法,将物理学轨道生成器和学习型关系预测器结合起来,以查明冲突和推断冲突关系。 我们证明我们的预测者P4P在Waymo Open Motion数据集的切合实际的互动驱动情景中比现有的基于学习的预测者取得优异的成绩。