Automatic Generation Method of Test Scenario for ADAS Based on Complexity

2017-01-1992

9/23/2017

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Abstract
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ADAS must be tested thoroughly before they can be deployed for series production. Comparing with road and field test, bench test has been widely used owing to its advantages of less labor costs, more controllable scenarios, etc. However, there is no satisfied systematic approach to generate high-efficiency and full-coverage test scenarios automatically because of its integration of human, vehicle and traffic. Most of the test scenarios generated by the existing methods are either too simple or too few to be able to achieve full coverage of requirements. Besides, the cost is high when the ET method is used. To solve the aforementioned problems, an automatic test scenario generation method based on complexity for bench test is presented in this paper. Firstly, considering the fact that the device is easier to malfunction under complex cases, an index measuring the complexity of test case is proposed by using the method of AHP. Based on the existing combinatorial test case generation algorithm, the proposed complexity index is used to evaluate the effectiveness and guide to generate test cases that are more effective. Furthermore, to improve the test efficiency, a clustering method is introduced to combine the discrete test cases into continuous scenarios. The effectiveness of the proposed method has been validated by applying to LDW. The results show that: (a) A more complex case is easier to find the faults of a system; (b) The generated scenarios can achieve full coverage of the specified N-wise combination; (c) The scenarios generated by the proposed method can detect the system malfunctions more efficiently with a more compact test suite.
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DOI
https://doi.org/10.4271/2017-01-1992
Citation
Xia, Q., Duan, J., Gao, F., Chen, T., et al., "Automatic Generation Method of Test Scenario for ADAS Based on Complexity," Intelligent and Connected Vehicles Symposium, Kunshan City, Jiangsu, China, September 26, 2017, https://doi.org/10.4271/2017-01-1992.
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Publisher
Published
9/23/2017
Product Code
2017-01-1992
Content Type
Technical Paper
Language
English