In this paper, we propose a novel behavior planner that combines game theory with search-based planning for automated lane merging. Specifically, inspired by human drivers, we model the interaction between vehicles as a gap selection process. To overcome the challenge of multi-modal behavior exhibited by the surrounding vehicles, we formulate the trajectory selection as a matrix game and compute an equilibrium. Next, we validate our proposed planner in the high-fidelity simulator CARLA and demonstrate its effectiveness in handling interactions in dense traffic scenarios.

翻译：本文提出了一种新颖的行为规划器，该规划器将博弈论与基于搜索的规划相结合，用于自动车道合并。具体而言，受人类驾驶员启发，我们将车辆间的交互建模为一个间隙选择过程。为克服周围车辆表现出的多模态行为带来的挑战，我们将轨迹选择形式化为一个矩阵博弈并计算其均衡解。接下来，我们在高保真模拟器CARLA中验证所提出的规划器，并展示其在密集交通场景下处理交互的有效性。