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,对于自主车辆(AVs)的安全、智能决策至关重要。然而,大多数运动预测模型忽略了AV行动的影响,规划模块必须将其他物剂视为无法改变的移动障碍。为了解决这一问题,本文件提议了一个互动了解运动预测模型,以便能够根据自我代理未来计划预测其他物剂的未来轨迹,即他们对自我行为的反应。具体地说,我们使用变压器对驾驶场进行有效编码,并纳入AV计划对预测的轨迹进行解码。为准确预测其他物剂反应而培训模型,我们开发一个在线学习框架,让自负代理探索环境,收集其他物剂对自己的反应。我们用三个高度互动的模拟驱动情景来验证决策和学习框架。结果显示,我们的决策方法大大超出了数据效率和性能方面的强化学习方法。我们还发现,在数据效率和性能方面,使用变压模型可以提高性能,而不是改进非交互性能测试过程。