Motion planners are essential for the safe operation of automated vehicles across various scenarios. However, no motion planning algorithm has achieved perfection in the literature, and improving its performance is often time-consuming and labor-intensive. To tackle the aforementioned issues, we present DrPlanner, the first framework designed to automatically diagnose and repair motion planners using large language models. Initially, we generate a structured description of the planner and its planned trajectories from both natural and programming languages. Leveraging the profound capabilities of large language models in addressing reasoning challenges, our framework returns repaired planners with detailed diagnostic descriptions. Furthermore, the framework advances iteratively with continuous feedback from the evaluation of the repaired outcomes. Our approach is validated using search-based motion planners; experimental results highlight the need of demonstrations in the prompt and the ability of our framework in identifying and rectifying elusive issues effectively.

翻译：运动规划器对于自动驾驶车辆在各种场景下的安全运行至关重要。然而，现有文献中尚无运动规划算法达到完美境界，且提升其性能往往耗时费力。为解决上述问题，本文提出DrPlanner——首个利用大语言模型自动诊断与修复运动规划器的框架。首先，我们从自然语言和编程语言两个维度，生成规划器及其规划轨迹的结构化描述。借助大语言模型在处理推理挑战方面的卓越能力，本框架可返回修复后的规划器及详细诊断说明。此外，该框架通过持续接收修复结果的评估反馈实现迭代进化。我们采用基于搜索的运动规划器验证了该方法；实验结果表明，提示中演示示例的必要性及本框架在有效识别和修正隐蔽问题方面的能力。