Evaluation of Different Fitness Functions for the Evolutionary Testing of an Autonomous Parking System

The method of evolutionary functional testing allows for the automation of testing by transforming the test case design into an optimization problem. For this aim it is necessary to define a suitable fitness function. In this paper two different fitness functions are compared for the testing of an autonomous parking system. The autonomous parking system is executed with the generated test scenarios, the fitness for each test scenario is calculated on basis of an evaluation of the quality of the parking maneuver calculated by the autonomous parking system. A numerical analysis shows, that the proposed area criterion supports a faster convergence of the optimization compared to the proposed distance criterion and that the proposed area criterion describes an efficient method to find functional errors in the system in an automated way.