Autonomous vehicles operating in complex real-world environments require accurate predictions of interactive behaviors between traffic participants. While existing works focus on modeling agent interactions based on their past trajectories, their future interactions are often ignored. This paper addresses the interaction prediction problem by formulating it with hierarchical game theory and proposing the GameFormer framework to implement it. Specifically, we present a novel Transformer decoder structure that uses the prediction results from the previous level together with the common environment background to iteratively refine the interaction process. Moreover, we propose a learning process that regulates an agent's behavior at the current level to respond to other agents' behaviors from the last level. Through experiments on a large-scale real-world driving dataset, we demonstrate that our model can achieve state-of-the-art prediction accuracy on the interaction prediction task. We also validate the model's capability to jointly reason about the ego agent's motion plans and other agents' behaviors in both open-loop and closed-loop planning tests, outperforming a variety of baseline methods.

翻译：在复杂真实环境中运行的自动驾驶车辆需要准确预测交通参与者之间的交互行为。现有研究虽专注于基于历史轨迹建模智能体交互，但常忽略其未来交互。本文通过分层博弈理论形式化交互预测问题，并提出了GameFormer框架加以实现。具体而言，我们设计了一种新颖的Transformer解码器结构，利用前一级的预测结果与公共环境背景迭代优化交互过程。此外，我们提出一种学习机制，使当前级别的智能体能够响应上一级别其他智能体的行为进行调控。通过在大型真实驾驶数据集上的实验，我们证明了该模型在交互预测任务中达到了最先进的预测精度。同时，在开环与闭环规划测试中，模型能够联合推理自我智能体的运动规划与其他智能体的行为，性能优于多种基线方法。