Utilizing Genetic Algorithms for Generating Critical Scenarios for Testing Autonomous Driving Functions

Finding critical scenarios is essential in testing autonomous driving and automated driving functions. Such scenarios describe a sequence of interactions between the autonomous vehicle or the vehicle equipped with automated driving functions and the environment, i.e., other cars, pedestrians, and the current road conditions, which challenge the system we want to test. In this paper, we present a search-based testing solution utilizing genetic algorithms for test generation coupled with a traffic simulator. As a fitness function, we rely on the amount of emergency braking required to prevent crashes. In addition, we compare two types of hyperparameter tuning. One type uses combinations of hyperparameters obtained from previous papers. The other is based on a design of experiment method. We show that the genetic algorithm using the design of experiments method for hyperparameter tuning outperforms the other implementation in terms of criticality (i.e., the time of emergency braking) and diversity. Furthermore, we show that both genetic algorithm implementations are superior to pure random testing in the application context of autonomous and automated driving.