Lane merging is one of the critical tasks for self-driving cars, and how to perform lane-merge maneuvers effectively and safely has become one of the important standards in measuring the capability of autonomous driving systems. However, due to the ambiguity in driving intentions and right-of-way issues, the lane merging process in autonomous driving remains deficient in terms of maintaining or ceding the right-of-way and attributing liability, which could result in protracted durations for merging and problems such as trajectory oscillation. Hence, we present a rule-compliance path planner (RCPP) for lane-merge scenarios, which initially employs the extended responsibility-sensitive safety (RSS) to elucidate the right-of-way, followed by the potential field-based sigmoid planner for path generation. In the simulation, we have validated the efficacy of the proposed algorithm. The algorithm demonstrated superior performance over previous approaches in aspects such as merging time (Saved 72.3%), path length (reduced 53.4%), and eliminating the trajectory oscillation.

翻译：车道合入是自动驾驶汽车的关键任务之一，如何有效且安全地执行车道合入操作已成为衡量自动驾驶系统能力的重要标准。然而，由于驾驶意图歧义性和路权问题，自动驾驶中的车道合入过程在路权保持/让行及责任归属方面仍存在缺陷，可能导致合入时间延长及轨迹振荡等问题。为此，我们提出面向车道合入场景的合规性路径规划器（RCPP），该方法首先通过扩展的责任敏感安全（RSS）模型明确路权关系，再基于势场方法结合S形规划器生成路径。仿真实验验证了所提算法的有效性。该算法在合入时间（节省72.3%）、路径长度（减少53.4%）及消除轨迹振荡等方面均优于现有方法。