Autonomous vehicles need to accomplish their tasks while interacting with human drivers in traffic. It is thus crucial to equip autonomous vehicles with artificial reasoning to better comprehend the intentions of the surrounding traffic, thereby facilitating the accomplishments of the tasks. In this work, we propose a behavioral model that encodes drivers' interacting intentions into latent social-psychological parameters. Leveraging a Bayesian filter, we develop a receding-horizon optimization-based controller for autonomous vehicle decision-making which accounts for the uncertainties in the interacting drivers' intentions. For online deployment, we design a neural network architecture based on the attention mechanism which imitates the behavioral model with online estimated parameter priors. We also propose a decision tree search algorithm to solve the decision-making problem online. The proposed behavioral model is then evaluated in terms of its capabilities for real-world trajectory prediction. We further conduct extensive evaluations of the proposed decision-making module, in forced highway merging scenarios, using both simulated environments and real-world traffic datasets. The results demonstrate that our algorithms can complete the forced merging tasks in various traffic conditions while ensuring driving safety.

翻译：自动驾驶车辆需要在与人类驾驶员交互的过程中完成其任务。因此，为自动驾驶车辆配备人工推理能力以更好地理解周围交通参与者的意图，从而促进任务完成至关重要。本文提出一种行为模型，将驾驶员的交互意图编码为潜在的社会心理参数。利用贝叶斯滤波器，我们开发了一种基于滚动时域优化的自动驾驶决策控制器，该控制器考虑了交互中驾驶员意图的不确定性。为实现在线部署，我们设计了一种基于注意力机制的神经网络架构，该架构通过在线估计的参数先验模仿行为模型。同时提出一种决策树搜索算法在线求解决策问题。通过真实轨迹预测能力评估所提出的行为模型，并在强制高速变道场景中，利用仿真环境与真实交通数据集对所提决策模块进行广泛评估。结果表明，我们的算法可在各种交通条件下完成强制变道任务，同时确保行车安全。