BehAVExplor: Behavior diversity guided testing for autonomous driving systems

Publication Type

Conference Proceeding Article

Publication Date

7-2023

Abstract

Testing Autonomous Driving Systems (ADSs) is a critical task for ensuring the reliability and safety of autonomous vehicles. Existing methods mainly focus on searching for safety violations while the diversity of the generated test cases is ignored, which may generate many redundant test cases and failures. Such redundant failures can reduce testing performance and increase failure analysis costs. In this paper, we present a novel behavior-guided fuzzing technique (BehAVExplor) to explore the different behaviors of the ego vehi- cle (i.e., the vehicle controlled by the ADS under test) and detect diverse violations. Specifically, we design an efficient unsupervised model, called BehaviorMiner, to characterize the behavior of the ego vehicle. BehaviorMiner extracts the temporal features from the given scenarios and performs a clustering-based abstraction to group behaviors with similar features into abstract states. A new test case will be added to the seed corpus if it triggers new behav- iors (e.g., cover new abstract states). Due to the potential conflict between the behavior diversity and the general violation feedback, we further propose an energy mechanism to guide the seed selec- tion and the mutation. The energy of a seed quantifies how good it is. We evaluated BehAVExplor on Apollo, an industrial-level ADS, and LGSVL simulation environment. Empirical evaluation results show that BehAVExplor can effectively find more diverse violations than the state-of-the-art.

Keywords

Apollo, Autonomous driving systems, Behavior diversity, Critical scenarios, Fuzzing

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, Seattle, Washington, US, July 17-21

First Page

488

Last Page

500

Identifier

10.1145/3597926.3598072

Publisher

ACM Digital Library

City or Country

New York, United States

Additional URL

https://doi.org/10.1145/3597926.3598072

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