  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, our framework returns repaired planners with detailed
diagnostic descriptions. Furthermore, our framework advances iteratively with
continuous feedback from the evaluation of the repaired outcomes. Our approach
is validated using both search- and sampling-based motion planners for
automated vehicles; experimental results highlight the need for demonstrations
in the prompt and show the ability of our framework to effectively identify and
rectify elusive issues.
