We propose an integrated behavior and motion planning framework for the automated lane-merging problem. The behavior planner combines search-based planning with game theory to model the interaction between vehicles and select multi-vehicle trajectories. Inspired by human drivers, we model the lane-merging problem as a gap selection process. To overcome the challenge of multi-modal driving behavior exhibited by the surrounding vehicles, we formulate the trajectory selection as a matrix game and compute some equilibrium solutions. In practice, however, the surrounding vehicles might deviate from the computed equilibrium trajectories. Thus, we introduce a branch model predictive control (BMPC) framework to account for the uncertain behavior modes of the surrounding vehicles. A tailored numerical solver is developed to enhance computational efficiency by leveraging the tree structure inherent in BMPC. Finally, we validate our proposed integrated planner using real traffic data and demonstrate its effectiveness in handling interactions in dense traffic scenarios.

翻译：我们提出了一种面向自动车道汇入问题的集成行为与运动规划框架。行为规划器将基于搜索的规划与博弈论相结合，以建模车辆间的交互并选择多车轨迹。受人类驾驶员行为启发，我们将车道汇入问题建模为间隙选择过程。为克服周围车辆表现出的多模式驾驶行为带来的挑战，我们将轨迹选择问题建模为矩阵博弈并计算若干均衡解。然而实际中，周围车辆可能偏离计算得到的均衡轨迹。为此，我们引入分支模型预测控制（BMPC）框架，以应对周围车辆不确定的行为模式。通过利用BMPC固有的树形结构，我们开发了定制化数值求解器以提升计算效率。最后，基于真实交通数据验证了所提出集成规划器的有效性，并论证了其在密集交通场景中处理交互行为的实际效能。