A Test Scenario Automatic Generation Strategy for Intelligent Driving Systems

Feng Gao, Jianli Duan, Yingdong He and Zilong Wang

Mathematical Problems in Engineering, 2019, vol. 2019, 1-10

Abstract:

In this paper, a methodology of automatic generation of test scenarios for intelligent driving systems is proposed, which is based on the combination of the test matrix (TM) and combinatorial testing (CT) methods together. With a hierarchical model of influence factors, an evaluation index for scenario complexity is designed. Then an improved CT algorithm is proposed to make a balance between test efficiency, condition coverage, and scenario complexity. This method can ensure the required combinational coverage and at the same time increase the overall complexity of generated scenarios, which is not considered by CT. Furthermore, the way to find the best compromise between efficiency and complexity and the bound of scenario number has been analyzed theoretically. To validate the effectiveness, it has been applied in the hardware-in-the-loop (HIL) test of a lane departure warning system (LDW). The results show that the proposed method can ensure required coverage with a significantly improved scenario complexity, and the generated test scenario can find system defects more efficiently.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3737486

DOI: 10.1155/2019/3737486

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