Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles

• URL: http://arxiv.org/abs/2109.06126v1
• Date: Mon, 13 Sep 2021 17:05:43 GMT
• Title: Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles
• Authors: Ziyuan Zhong, Gail Kaiser, Baishakhi Ray
• Abstract summary: Existing testing methods are inadequate for checking the end-to-end behaviors of autonomous vehicles. We propose a new fuzz testing technique, called AutoFuzz, which can leverage widely-used AV simulators' API grammars. AutoFuzz efficiently finds hundreds of realistic traffic violations resembling real-world crashes.
• Score: 15.702721819948623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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). AutoFuzz is guided by a constrained Neural Network (NN) evolutionary search over the API grammar to generate scenarios seeking to find unique traffic violations. Evaluation of our prototype on one state-of-the-art learning-based controller and two rule-based controllers shows that AutoFuzz efficiently finds hundreds of realistic traffic violations resembling real-world crashes. 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.

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