Understanding the intention of vehicles in the surrounding traffic is crucial for an autonomous vehicle to successfully accomplish its driving tasks in complex traffic scenarios such as highway forced merging. In this paper, we consider a behavioral model that incorporates both social behaviors and personal objectives of the interacting drivers. Leveraging this model, we develop a receding-horizon control-based decision-making strategy, that estimates online the other drivers' intentions using Bayesian filtering and incorporates predictions of nearby vehicles' behaviors under uncertain intentions. The effectiveness of the proposed decision-making strategy is demonstrated and evaluated based on simulation studies in comparison with a game theoretic controller and a real-world traffic dataset.

翻译：理解周围交通车辆意图对于自主车辆在复杂交通场景（如高速公路强制合流）中成功完成驾驶任务至关重要。本文考虑了一种融合交互驾驶员社会行为与个人目标的驾驶行为模型。基于该模型，我们提出了一种基于滚动时域控制的决策策略，该策略通过贝叶斯滤波在线估计其他驾驶员的意图，并融合了对周围车辆在不确定意图下的行为预测。通过仿真实验对比博弈论控制器及真实交通数据集，验证并评估了所提决策策略的有效性。