Publication Type
Journal Article
Version
acceptedVersion
Publication Date
3-2023
Abstract
Autonomous vehicle (AV) systems must be comprehensively tested and evaluated before they can be deployed. High-fidelity simulators such as CARLA or LGSVL allow this to be done safely in very realistic and highly customizable environments. Existing testing approaches, however, fail to test simulated AVs systematically, as they focus on specific scenarios and oracles (e.g., lane following scenario with the "no collision" requirement) and lack any coverage criteria measures. In this paper, we propose AVUnit, a framework for systematically testing AV systems against customizable correctness specifications. Designed modularly to support different simulators, AVUnit consists of two new languages for specifying dynamic properties of scenes (e.g. changing pedestrian behaviour after waypoints) and fine-grained assertions about the AV's journey. AVUnit further supports multiple fuzzing algorithms that automatically search for test cases that violate these assertions, using robustness and coverage measures as fitness metrics. We evaluated the implementation of AVUnit for the LGSVL+Apollo simulation environment, finding 19 kinds of issues in Apollo, which indicate that the open-source Apollo does not perform well in complex intersections and lane changing related scenarios.
Keywords
Autonomous Driving System, Testing, Specification Languages, Fuzzing, Coverage Criteria
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
IEEE Transactions on Software Engineering
First Page
1
Last Page
20
ISSN
0098-5589
Identifier
10.1109/TSE.2023.3254142
Publisher
Institute of Electrical and Electronics Engineers
Citation
ZHOU, Yuan; SUN, Yang; TANG, Yun; CHEN, Yuqi; SUN, Jun; POSKITT, Christopher M.; LIU, Yang; and YANG, Zijiang.
Specification-based autonomous driving system testing. (2023). IEEE Transactions on Software Engineering. 1-20.
Available at: https://ink.library.smu.edu.sg/sis_research/7772
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/TSE.2023.3254142