An Accelerated Filter for Critical Scenario Identification in Automated Driving Function Testing: A Model-Free Approach

Abstract

Automated Vehicle (AV) safety is a critical issue and appeals to worldwide focus. To ensure AV safety, AV functions should be tested and evaluated in an enormous number of scenarios. Since such AV testing is time-consuming, scenario filters have been developed to identify safety-critical scenarios and omit ordinary ones. However, the scenarios identified by these filters do not uniquely match the AV function to be tested and are most likely not critical for the AV function. Therefore, an enhanced scenario filter is proposed in this paper. It bears the following features: 1) Automated-driving-function-specific scenario identification; 2) High coverage of critical scenarios; 3) Enhanced identification efficiency by avoiding adopting a surrogate model; 4) High reliability of critical scenario identification. To enable the above features, the proposed filter formulates the identification problem into an optimization problem and solves it with a model-free approach. Experiments have been conducted to evaluate and validate the proposed filter. The results confirm that the proposed filter is able to improve coverage of critical scenarios, efficiency of identification, and reliability of identification compared to the state-of-the-art filter. Specifically, the proposed filter improves coverage by up to 70 percent, efficiency by up to 97 percent, and reliability by up to 22 percent. The results also reveal that the proposed filter shows an increasing advantage for testing AV functions with higher complexity.