Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2022
Abstract
Autonomous driving systems (ADSs) must be tested thoroughly before they can be deployed in autonomous vehicles. High-fidelity simulators allow them to be tested against diverse scenarios, including those that are difficult to recreate in real-world testing grounds. While previous approaches have shown that test cases can be generated automatically, they tend to focus on weak oracles (e.g. reaching the destination without collisions) without assessing whether the journey itself was undertaken safely and satisfied the law. In this work, we propose LawBreaker, an automated framework for testing ADSs against real-world traffic laws, which is designed to be compatible with different scenario description languages. LawBreaker provides a rich driver-oriented specification language for describing traffic laws, and a fuzzing engine that searches for different ways of violating them by maximising specification coverage. To evaluate our approach, we implemented it for Apollo+LGSVL and specified the traffic laws of China. LawBreaker was able to find 14 violations of these laws, including 173 test cases that caused accidents.
Keywords
Autonomous vehicles, Traffic laws, Fuzzing, STL, LGSVL, Apollo
Discipline
Software Engineering | Transportation | Transportation Law
Research Areas
Software and Cyber-Physical Systems
Publication
ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Rochester, MI, October 10-14
First Page
1
Last Page
12
ISBN
9781450394758
Identifier
10.1145/3551349.3556897
Publisher
ACM
City or Country
New York
Citation
SUN, Yang; POSKITT, Christopher M.; SUN, Jun; CHEN, Yuqi; and YANG, Zijiang.
LawBreaker: An approach for specifying traffic laws and fuzzing autonomous vehicles. (2022). ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Rochester, MI, October 10-14. 1-12.
Available at: https://ink.library.smu.edu.sg/sis_research/7745
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.1145/3551349.3556897
