  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.
