一句话：提出 FixDrive 框架，用多模态大模型从违规记录生成 μDrive 驾驶策略修复，减少违规与碰撞。
自动驾驶车辆在适应性与性能上仍落后于人类驾驶员，常过于保守甚至违反交规。运行时强制修复虽可缓解，但缺乏透明性且难以泛化。本文提出 FixDrive 框架，从近失或违规驾驶记录中分析并可视化关键时刻，利用多模态大语言模型（MLLM）零样本生成 μDrive 程序。μDrive 是一种事件触发式描述驾驶行为的高层领域特定语言，使修复可解释且可泛化。该框架在自动驾驶系统 Apollo 上实现。作者在多种基准场景中评估，发现生成的修复改善了车辆遵守交规、避免碰撞和成功到达目的地的表现；修复成本为 15 分钟离线分析与每次违规 0.08 美元，实践中可接受。
Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability and performance, often exhibiting overly conservative behaviours and occasionally violating traffic laws. Existing solutions, such as runtime enforcement, mitigate this by automatically repairing the AV's planned trajectory at runtime, but such approaches lack transparency and should be a measure of last resort. It would be preferable for AV repairs to generalise beyond specific incidents and to be interpretable for users. In this work, we propose Fixdrive, a framework that analyses driving records from near-misses or law violations to generate AV driving strategy repairs that reduce the chance of such incidents occurring again. These repairs are captured in μDrive, a high-level domain-specific language for specifying driving behaviours in response to event-based triggers. Implemented for the state-of-the-art autonomous driving system Apollo, Fixdrive identifies and visualises critical moments from driving records, then uses a Multimodal Large Language Model (MLLM) with zero-shot learning to generate μDrive programs. We tested Fixdrive on various benchmark scenarios, and found that the generated repairs improved the AV's performance with respect to following traffic laws, avoiding collisions, and successfully reaching destinations. Furthermore, the direct costs of repairing an AV—15 minutes of offline analysis and $0.08 per violation-are reasonable in practice.
DOI 原文 ·
@inproceedings{paperbot8737,
title = {FIXDRIVE: Automatically Repairing Autonomous Vehicle Driving Behaviour for $0.08 per Violation},
author = {Yang Sun and Christopher M. Poskitt and Kun Wang and Jun Sun},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00216}
}