FixDrive: Automatically Repairing Autonomous Vehicle Driving Behaviour for $0.08 per Violation
SE for AI
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 $\mu$Drive, a high-level domain-specific language for specifying driving behaviours according 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 $\mu$Drive 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.
Slides (ICSE-2025.pdf) | 1020KiB |
Thu 1 MayDisplayed time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | SE for AI 3Research Track / SE in Society (SEIS) / Journal-first Papers at 215 Chair(s): Lina Marsso École Polytechnique de Montréal | ||
14:00 15mTalk | Dissecting Global Search: A Simple yet Effective Method to Boost Individual Discrimination Testing and RepairSE for AI Research Track Lili Quan Tianjin University, Li Tianlin NTU, Xiaofei Xie Singapore Management University, Zhenpeng Chen Nanyang Technological University, Sen Chen Nankai University, Lingxiao Jiang Singapore Management University, Xiaohong Li Tianjin University Pre-print | ||
14:15 15mTalk | FixDrive: Automatically Repairing Autonomous Vehicle Driving Behaviour for $0.08 per ViolationSE for AI Research Track Yang Sun Singapore Management University, Chris Poskitt Singapore Management University, Kun Wang Zhejiang University, Jun Sun Singapore Management University Link to publication DOI Pre-print File Attached | ||
14:30 15mTalk | MARQ: Engineering Mission-Critical AI-based Software with Automated Result Quality AdaptationSE for AI Research Track Uwe Gropengießer Technical University of Darmstadt, Elias Dietz Technical University of Darmstadt, Florian Brandherm Technical University of Darmstadt, Achref Doula Technical University of Darmstadt, Osama Abboud Munich Research Center, Huawei, Xun Xiao Munich Research Center, Huawei, Max Mühlhäuser Technical University of Darmstadt | ||
14:45 15mTalk | An Empirical Study of Challenges in Machine Learning Asset ManagementSE for AI Journal-first Papers Zhimin Zhao Queen's University, Yihao Chen Queen's University, Abdul Ali Bangash Software Analysis and Intelligence Lab (SAIL), Queen's University, Canada, Bram Adams Queen's University, Ahmed E. Hassan Queen’s University | ||
15:00 15mTalk | A Reference Model for Empirically Comparing LLMs with HumansSE for AI SE in Society (SEIS) Kurt Schneider Leibniz Universität Hannover, Software Engineering Group, Farnaz Fotrousi Chalmers University of Technology and University of Gothenburg, Rebekka Wohlrab Chalmers University of Technology | ||
15:15 7mTalk | Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-PracticeSE for AI Journal-first Papers Bentley Oakes Polytechnique Montréal, Michalis Famelis Université de Montréal, Houari Sahraoui DIRO, Université de Montréal DOI Pre-print File Attached |