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.

自主车辆在交通中需要与人类驾驶员互动来完成任务，因此装备自主车辆具备人工推理以更好地理解周围交通的意图，从而促进任务完成。本文提出了一种行为模型，将驾驶员的互动意图编码为潜在的社会心理参数，并设计了基于贝叶斯滤波器的递进视界优化控制器以考虑互动驾驶员意图的不确定性，同时采用基于注意机制的神经网络架构来模仿行为模型和在线估计参数先验，并提出决策树搜索算法在线解决决策问题。本文对所提出的行为模型进行了真实世界轨迹预测能力的评估，并在模拟环境和真实世界交通数据集中进行了强制公路合并场景的决策模块的广泛评估，结果表明我们的算法能够在各种交通条件下完成强制合并任务并确保驾驶安全。