Generating Autonomous Driving Safety Violation Scenarios Based on Multi-Objective Optimization.

Autonomous vehicles play an important role in alleviating traffic congestion and eliminating traffic accidents. However, as safety-critical software, they must undergo testing before being deployed on public roads. Due to its scalability and repeatability, simulation testing has become a prominent research hotspots in autonomous driving testing techniques. In response to the issues of high similarity among generated scenarios and limited discovery of violation types in existing scenario testing methods, this paper proposed a method of generating autonomous driving safety violation scenarios based on multi-objective optimization (A V _ MOVS). The aim is to utilize a multi-objective genetic algorithm to guide the evolutionary search direction, thereby discovering a wider range of safety violation scenarios for autonomous driving systems and improving software testing efficiency. The simulation experiment results on the Baidu platform Apollo demonstrate that the efficiency of generating safety violation scenarios using the method proposed in this paper has been improved by 16.7% compared to other methods.