DrPlanner: Diagnosis and Repair of Motion Planners for Automated Vehicles Using Large Language Models
Yuanfei Lin, Chenran Li, Mingyu Ding, Masayoshi Tomizuka, Wei Zhan, Matthias Althoff
Abstract
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathtt {DrPlanner}}$</tex-math></inline-formula>, the first framework designed to automatically <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</u>iagnose and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u>epair motion <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">planner</u>s 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.