Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents. Because existing diversity-aware predictors do not account for the interactive nature of multi-agent predictions, they may miss these important interaction outcomes. In this paper, we propose NashFormer, a framework for trajectory prediction that leverages game-theoretic inverse reinforcement learning to improve coverage of multi-modal predictions. We use a training-time game-theoretic analysis as an auxiliary loss resulting in improved coverage and accuracy without presuming a taxonomy of actions for the agents. We demonstrate our approach on the interactive split of the Waymo Open Motion Dataset, including four subsets involving scenarios with high interaction complexity. Experiment results show that our predictor produces accurate predictions while covering $33\%$ more potential interactions versus a baseline model.

翻译：道路智能体之间的交互给轨迹预测带来了显著挑战，特别是在涉及多个智能体的场景中。由于现有的多样性感知预测器未能考虑多智能体预测的交互特性，它们可能会遗漏这些重要的交互结果。本文提出NashFormer，一个通过博弈论逆强化学习提升多模态预测覆盖率的轨迹预测框架。我们利用训练阶段的博弈论分析作为辅助损失函数，从而在无需预设智能体动作分类的情况下提升覆盖率和准确性。我们在Waymo开放运动数据集的交互子集上验证了该方法，该子集包含四个涉及高交互复杂度的场景。实验结果表明，我们的预测器在保持预测准确性的同时，相比基线模型多覆盖了$33\%$的潜在交互。