Specific Scenario Generation Method for Trustworthiness Testing of Autonomous Vehicles Based on Interaction Coding

In response to the problems of rough modeling and insufficient coverage of edge interaction scenarios in autonomous driving tests, this paper proposes a scene generation method based on interaction coding. The method constructs a hierarchical parameter system of function–logic–specific scene, uses the time difference of arrival at interaction points (TTC_diff) to determine the critical state of interaction, and realizes the efficient generation and iterative optimization of high-risk scenes. Taking the unprotected left turn at the signal intersection of urban roads as an example, the interaction coding combination is determined in combination with real traffic data, the test scene compatible with OpenSCENARIO is generated, and CARLA0.9.15 is called for test verification. The results show that the interaction intensity is significantly negatively correlated with the trustworthiness score (−0.815), the generated scene has high coverage, and both safety and challenge are taken into account. Compared with the simulated annealing method, the method in this paper performs better in terms of iteration efficiency, scene difficulty control, and score stability, which provides an efficient and reliable test strategy for the trustworthiness evaluation of autonomous driving.