Evolving traffic scenarios to test driver assistance systems in simulations

Abstract

Nowadays, driver assistance systems involve an ever increasing degree of automatization. Costly effort is put into testing the individual components to ensure proper functioning by the vehicle manufacturers. However, problems can also arise on a macroscopic scale, as vehicles and infrastructure are recently equipped with short range radio communication (“Car2X”). These problems caused by interaction are of even greater concern than “normal” bugs, as the final product might already have been deployed when the issues first become apparent. Multi-Agent System (MAS) research refers to such issues as “emergent misbehavior”. The said field also brought up an approach to automatically discover the worst consequences of the malfunctions. Hence, a given system under test can already be revised during development, saving a tremendous amount of resources. The approach from MAS is adapted to the domain of testing driver assistance systems in traffic simulations. A green-light optimal-speed advisory (GLOSA) algorithm is used as an example in which conceptual problems are discovered by the testing system after simpler issues are eliminated.