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Interactions between road agents present a significant challenge intrajectory prediction, especially in cases involving multiple agents. Becauseexisting diversity-aware predictors do not account for the interactive natureof multi-agent predictions, they may miss these important interaction outcomes.In this paper, we propose NashFormer, a framework for trajectory predictionthat leverages game-theoretic inverse reinforcement learning to improvecoverage of multi-modal predictions. We use a training-time game-theoreticanalysis as an auxiliary loss resulting in improved coverage and accuracywithout presuming a taxonomy of actions for the agents. We demonstrate ourapproach on the interactive split of the Waymo Open Motion Dataset, includingfour subsets involving scenarios with high interaction complexity. Experimentresults show that our predictor produces accurate predictions while covering$33\%$ more potential interactions versus a baseline model.

NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction