Ziyuan Zhong, Gail Kaiser and Baishakhi Ray. Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles. IEEE Transactions on Software Engineering (TSE), 49(4):1860-1875, April 2023. https://doi.org/10.1109/TSE.2022.3195640. (Also appeared at the 45th International Conference on Software Engineering (ICSE) as a journal-first paper, Melbourne Australia, May 2023.).

Self-driving cars and trucks, autonomous vehicles (AVs), should not be accepted by regulatory bodies and the public until they have much higher confidence in their safety and reliability — which can most practically and convincingly be achieved by testing. But existing testing methods are inadequate for checking the end-to-end behaviors of AV controllers against complex, real-world corner cases involving interactions with multiple independent agents such as pedestrians and human-driven vehicles. While test-driving AVs on streets and highways fails to capture many rare events, existing simulation-based testing methods mainly focus on simple scenarios and do not scale well for complex driving situations that require sophisticated awareness of the surroundings. To address these limitations, we propose a new fuzz testing technique, called AutoFuzz, which can leverage widely-used AV simulators’ API grammars to generate semantically and temporally valid complex driving scenarios (sequences of scenes). To efficiently search for traffic violations-inducing scenarios in a large search space, we propose a constrained neural network (NN) evolutionary search method to optimize AutoFuzz. Evaluation of our prototype on one state-of-the-art learning-based controller, two rule-based controllers, and one industrial-grade controller in five scenarios shows that AutoFuzz efficiently finds hundreds of traffic violations in high-fidelity simulation environments. For each scenario, AutoFuzz can find on average 10-39% more unique traffic violations than the best-performing baseline method. Further, fine-tuning the learning-based controller with the traffic violations found by AutoFuzz successfully reduced the traffic violations found in the new version of the AV controller software.

@article{AutoCop,
author = {Ziyuan Zhong and Gail Kaiser and Baishakhi Ray},
title = {{Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles}},
journal = {IEEE Transactions on Software Engineering (TSE)},
year = {2023},
month = {April},
volume = {49},
number = {4},
pages = {1860-1875},
url = {https://doi.org/10.1109/TSE.2022.3195640},
}