论文标题
P4P：在自动驾驶中进行计划的冲突感知运动预测
P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving
论文作者
论文摘要
运动预测对于在互动场景中为自动驾驶汽车实现安全运动计划至关重要。它允许计划者确定与其他交通代理的潜在冲突并生成安全计划。现有的运动预测因素通常集中在减少预测错误上，但对于它们如何帮助确定计划者的冲突仍然是一个悬而未决的问题。在本文中，我们通过新颖的与冲突相关的指标（例如识别冲突的成功率）评估最新的预测指标。令人惊讶的是，当我们测试交互式模拟器中的预测规划系统时，预测因子的成功率很低，因此导致很大一部分的碰撞。为了填补空白，我们提出了一种简单但有效的替代方案，该替代方案结合了基于物理的轨迹生成器和基于学习的关系预测指标，以识别冲突并推断冲突关系。我们证明，我们的预测因子P4P在Waymo Open Motion DataSet中实现了现实的交互式驾驶场景中的现有基于基于学习的预测指标。
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.
