A Quantitative Approach of Generating Challenging Testing Scenarios Based on Functional Safety Standard

Abstract

With the rapid development of intelligent vehicle safety verification, scenario-based testing methods have received increasing attention. As the space of driving scenarios is vast, the challenge in scenario-based testing is the generation and selection of high-value testing scenarios to reduce the development and validation time. This paper proposes a method for generating challenging test scenarios. Our method quantifies the challenges in these scenarios by estimating the risks based on ISO 26262. We formulate the problem as a Markov decision process and quantify the challenges in the current state using the three risk factors provided in ISO 26262: exposure, severity, and controllability. We then employ reinforcement learning algorithms to identify the challenges and use the state–action value matrix to select motions for a background vehicle to generate critical scenarios. The effectiveness of the approach is validated by testing the generated challenge scenarios using a simulation model. The results show that our method can ensure both accuracy and coverage, and the larger the state space is, the more accident-prone the generated scenarios are. Our proposed method is general and easily adaptable to other cases.

Keywords

• testing scenario generation
• functional safety
• reinforcement learning