Predicting the behaviors of other road users is crucial to safe and intelligent decision-making for autonomous vehicles (AVs). However, most motion prediction models ignore the influence of the AV's actions and the planning module has to treat other agents as unalterable moving obstacles. To address this problem, this paper proposes an interaction-aware motion prediction model that is able to predict other agents' future trajectories according to the ego agent's future plan, i.e., their reactions to the ego's actions. Specifically, we employ Transformers to effectively encode the driving scene and incorporate the AV's plan in decoding the predicted trajectories. To train the model to accurately predict the reactions of other agents, we develop an online learning framework, where the ego agent explores the environment and collects other agents' reactions to itself. We validate the decision-making and learning framework in three highly interactive simulated driving scenarios. The results reveal that our decision-making method significantly outperforms the reinforcement learning methods in terms of data efficiency and performance. We also find that using the interaction-aware model can bring better performance than the non-interaction-aware model and the exploration process helps improve the success rate in testing.

翻译：预测其他道路使用者的行为对实现安全、智能的自动驾驶决策至关重要。然而，现有大多数运动预测模型忽略了自车行为的影响，导致规划模块需将其他代理视为不可改变的移动障碍物。针对此问题，本文提出一种交互感知运动预测模型，该模型能根据自车未来规划（即其他代理对自车行为的反应）预测其未来轨迹。具体而言，我们采用Transformer有效编码驾驶场景，并在解码预测轨迹时融入自车规划。为训练模型准确预测其他代理的反应，我们开发了一种在线学习框架：自车探索环境并收集其他代理对自身的反应。我们在三个高交互性的模拟驾驶场景中验证了决策与学习框架。结果表明，我们的决策方法在数据效率与性能上显著优于强化学习方法。同时发现，采用交互感知模型比非交互感知模型能带来更优性能，且探索过程有助于提升测试成功率。