Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
5-2025
Abstract
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.
Keywords
autonomous vehicles, autonomous driving systems, multimodal large language models, driving compliance
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Areas of Excellence
Digital transformation
Publication
ICSE'25: Proceedings of the 47th IEEE/ACM International Conference on Software Engineering, Ottawa, Canada, April 26 - May 6
First Page
1921
Last Page
1933
Identifier
10.1109/ICSE55347.2025.00216
Publisher
IEEE
City or Country
New York
Citation
SUN, Yang; POSKITT, Christopher M.; WANG, Kun; and SUN, Jun.
FixDrive: Automatically repairing autonomous vehicle driving behaviour for $0.08 per violation. (2025). ICSE'25: Proceedings of the 47th IEEE/ACM International Conference on Software Engineering, Ottawa, Canada, April 26 - May 6. 1921-1933.
Available at: https://ink.library.smu.edu.sg/sis_research/10242
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICSE55347.2025.00216