Rule-based searching for collision test cases of autonomous vehicles simulation
Research and development in the field of autonomous vehicles has increased along with related work on automated driving (AD) software. Thorough testing of AD software using simulations must be conducted in advance of testing AD cars on the road. Parameters of the many objects around an AD car, such as other cars, traffic lanes and pedestrians are required as inputs of the simulation. Therefore, the number of parameter combinations becomes extremely large. A combination of parameters is called a test case; hence, the challenge is to search collision test cases from the extremely large number of combinations. A rule-based method is the main focus because an explicit method of searching test cases is required in certain industries in the real world. In this study, a method of rule-based searching for collision test cases of autonomous vehicles simulations is proposed. Simulation models that have rules between an AD car and other cars are defined. Algorithms were also developed to search collision test cases that generate test cases incrementally. Experiments on AD simulations involving the simulation models of a three-lane highway and a signalised intersection were conducted. The results indicate the efficiency of the method.