Test scenario generation method for autonomous vehicles based on combinatorial testing and Bayesian network

Abstract

A test scenario generation method based on combinatorial testing (CT) and Bayesian Network for autonomous vehicles is proposed in this paper. Firstly, some parameters are selected to describe the test scenarios which are classified according to road types and driving tasks. Then, the constraint sets for the scenarios with forbidden tuples are established to avoid the generated cases do not conform to the reality, in which the construct constraint set (CCS) algorithm is utilized to compute implied constraints. Furthermore, the Bayesian networks is used as the probabilistic models of the scenarios, where the traffic participants are represented as object nodes and the relative relationships between the participants are converted into the network structures. Finally, an improved automatic efficient test case generator (AETG) is developed to generate test cases. By considering both probability and frequency, the select function is designed for determining the values of scenario parameters. And the generation mode can be changed by modifying the weight and target parameters. The effectiveness of the proposed method is evaluated by generating six typical test scenarios. Compared with other algorithms, the numbers of test cases in the sets generated by this method are less and the probability deviations are smaller.