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.

Longitudinal Functional Safety Analysis and Algorithm Design of Traffic Jam Pilot

“…Xia Xianzhao et al applied the functional safety analysis method for failure analysis and hazard identification in adaptive cruise control (ACC) system to obtain safety goals, Automotive Safety Integration Level (ASIL) level, and derive safety constraints, and safety requirements [19]. In addition to the work of the researchers mentioned above, ISO 26262 is also used in other areas, such as automotive software development [20], power supply systems [21], robotic drives [22], design of the brakeby-wire [23], risk quantification for driving scenarios [24], and test scenario generation [25].…”