Using genetic algorithms for automating automated lane-keeping system testing

In this paper, we outline an approach for automatically generating challenging road networks for virtual testing of an automated lane-keeping system. Based on a set of control points, we construct a parametric curve representing a road network, defining the dynamic driving task an automated lane-keeping system-equipped vehicle must perform. Changing control points has a global influence on the resulting road geometry. Our approach uses search to find control-point sets that result in a challenging road, eventually forcing the vehicle to leave the intended path. We apply our approach in different search variants to evaluate their performance regarding test efficiency and the diversity of failing tests. In addition, we evaluate different genetic algorithm control parameter configurations to investigate the most influential parameters and if specific configurations can be seen as optimal, leading to better results than others. For both studies, we consider another search-based test method and two different random test generators as a baseline for comparison. The empirical results indicate that specific control parameter settings increase the overall performance for each search variant. While the population size is the most influential control parameter for all methods, the performance improvement when using optimal settings is only significant for one method.