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Automatic Generation Method of Test Scenario for ADAS Based on Complexity
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 23, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
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.
CitationXia, Q., Duan, J., Gao, F., Chen, T. et al., "Automatic Generation Method of Test Scenario for ADAS Based on Complexity," SAE Technical Paper 2017-01-1992, 2017, https://doi.org/10.4271/2017-01-1992.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
- Kwon, S. W., Chun, J., Jang, S., Suh, M., “Driving performance analysis of the adaptive cruise controlled vehicle with a virtual reality simulation system,” Journal of Mechanical Science and Technology 20(1): 29-41, 2006, doi: 10.1007/BF02916197.
- Ploeg, J., Hendriks, F., Schouten, N. J., “Towards Nondestructive Testing of Pre-crash Systems in a HIL Setup,” presented at IEEE Intelligent Vehicles Symposium, Netherlands, June 4-6, 2008.
- Bock, T., Maurer, M., Farber, G., “Vehicle in the Loop (VIL) - A new simulator set-up for testing Advanced Driving Assistance Systems,” presented at DSC 2007, Iowa City, September, 2007.
- Karl, I., Berg, G., Ruger, F., Farber, B., “Driving Behavior and Simulator Sickness While Driving the Vehicle in the Loop: Validation of Longitudinal Driving Behavior,” IEEE Intelligent Transportation Systems Magazine 5(1): 42-57, 2013, doi:10.1109/MITS.2012.2217993.
- Mugur, T., “Enhancing ADAS Test and Validation with Automated Search for Critical Situations,” presented at DSC 2015, Berlin, September 16-18, 2015.
- Gunia, D., Schuling, J., “Model-based testing of Ford’s lane keeping system,” Auto Tech Review 1(6): 48-51, 2012, doi:10.1365/s38311-011-0112-6.
- Zhang, Q., Chen, D. X., Li, Y. S., Li, K. Q., “Research on Performance Test Method of Lane Departure Warning System with PreScan,” SAE Proceedings of SAE-China Congress 2014: Selected Papers :445-453, 2015, doi: 10.1007/978-3-662-45043-7_45.
- Cohen, D., Dalal, S. R., Fredman, M. L., Patton, G. C., “The AETG system: An approach to testing based on combinatorial design,” IEEE Transactions on Software Engineering 23(7): 437-444, 1997, doi: 10.1109/32.605761.
- Sangeeta, S., Manuj, A., “A novel approach for deriving interactions for combinatorial testing,” Engineering Science and Technology an International Journal 20(2017): 59-71, 2016.
- Kuhn, D. R., Reilly, M. J., “An Investigation of the Applicability of Design of Experiments to Software Testing,” annual Software Engineering Workshop, USA, December 4-6, 2002.
- Kuhn, R., Lei, Y., Kacker, R. N., “Practical Combinatorial Testing: Beyond Pairwise Article in IT Professional,” It Professional 10(3):19-23, 2008, doi: 10.1109/MITP.2008.54.
- Aczel, J., Saaty, T. L., “Procedures for Synthesizing Ratio Judgments,” Journal of Mathematical Psychology 27(1):93-102, 1983, doi: 10.1016/0022-2496(83)90028-7
- Okoli, C., Pawlowski, S. D., “The Delphi Method as a Research Tool: An Example, Design Considerations and Applications,” Information and Management 42(1):15-29, 2004, doi: 10.1016/j.im.2003.11.002.
- Benitez, J., Delgadogalvan, X., Lzquierdo, J., Perezgarcia, R., “Improving Consistency in AHP Decision-making Processes,” Applied Mathematics and Computation 219(5):2432-2441, 2012, doi: 10.1016/j.amc.2012.08.079.
- Saaty, R. W., “The analytic hierarchy process-what it is and how it is used,” Mathematical Modelling :161-176, 1987, doi: 10.1016/0270-0255(87)90473-8.
- Meng, K. W., Zhao, Y. N., Gao, L., Tan, H. C., “Evaluation of the Intelligent Behaviors of Unmanned Ground Vehicles Based on Information Theory,” presented at CICTP 2015, Beijing, July 25-27, 2015, doi:10.1061/9780784479292.037.
- Czerwonka, J., “Pairwise testing in real world,” presented at Pacific Northwest Software Quality Conference, October, 2006.
- Zhao, D., Lam, H., Peng, H., Bao, S., et al. “Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios based on Importance Sampling Techniques,” IEEE Transactions on Intelligent Transportation Systems 1-13, 2016, doi: 10.1109/TISI.20162582208.
- Lemmen, P., Fagerlind, H., Unselt, T., Rodarius, C., et al. “Assessment of Integrated Vehicle Safety Systems for improved vehicle,” Procedia - Social and Behavioral Science 48(0):1632-1641, 2012, doi: 10.1016/j.sbspro.2012.06.1138.
- Xiong, G. M., Gao, L., Wu, S. B., Zhao, Y. N., et al. “Intelligent Behaviors and Test and Evaluation for Unmanned Ground Vehicles,” (Beijing, BEIJING INSTITUTE OF TECHNOLOGY PRESS, 2015), 121-125, ISBN:978-7-5682-1496-4.
- Wulf, J., “The Flattened Firm,” California Management Review 55(1): 5-23, 2012, doi: 10.1525/cmr.2012.55.1.5.
- Amine, A., Elberrichi, Z., Simonet, M., “Evaluation of Text Clustering Methods Using WordNet,” The International Arab Journal of Information Technology 7(4): 349-356, 2010.
- Zoubi, M. B., Rawi, M. A., “An Efficient Approach for Computing Silhouette Coefficients,” Journal of Computer Science 4(3): 252-255, 2008, doi:10.3844/jcssp.2008.252.255.
- Gietelink, O. J., Ploeg, J., De Schutter, B., Verhaegen, M., “Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations,” Vehicle System Dynamics 44(7):569-590, 2006, doi: 10.1080/00423110600563338.
- Gietelink, O. J., Verburg, D. J., Labibes, K., Oostendorp, A. F., “Pre-crash system validation with PRESCAN and VEHIL,” presented at IEEE Intelligent Vehicles Symposium, Italy, June 14-17, 2004, doi: 10.1109/IVS.2004.1336507.