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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.