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Intelligent Vehicle Test and User Condition Correlation Evaluation Model |
LI Wen-liang1, SONG Yi2, ZHANG Lu1, ZHOU Wei1, ZHANG Jin-ling2 |
1. Key Laboratory of operation safety technology on transport vehicles, Ministry of Transport, PRC, Beijing 100088, China; 2. Beijing University of Posts and Telecommunications, Beijing 100876, China |
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Abstract To research the quantitative evaluation of the effectiveness of intelligent vehicles from user operating conditions to test operating conditions in proving ground, a matching model and evaluation model of intelligent vehicle test condition and user condition was presented on the basis of theory of human-vehicle-road cooperative driving risk field by comprehensively considering the acceleration coefficient, risk coverage, maximum risk, and risk distribution of user conditions and test site conditions. With the vehicle following scene taken as an example and with the JT/T 1242-2019, GB/T 33577-2017, and ISO 22839 standards used as reference, the validity of the actual operating condition data of users and the operating condition data of the test site was analyzed and evaluated by using the correlation evaluation model. Results showed that the research results can be used to quantitatively evaluate the effectiveness of different intelligent vehicle test conditions.
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Received: 13 December 2019
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Fund:Supported by the National Key R&D Program of China(No, 2017YFC0804808) |
Corresponding Authors:
LI Wen-liang
E-mail: wl.li@rioh.cn
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[1] BIAN Ming-yuan, LI Ke-qiang. Strategic Analysis on Establishing an Automobile Power in China Based on Itelligent & Connected Vehicles[J]. Strategic Study of CAE,2018,20(1):52-58. (in Chinese) [2] LI Ke-qiang, DAI Yi-fan, LI Sheng-bo, et al. State-of-the-art and Technical Trends of Intelligent and Connected Vehicles[J]. Journal of Automotive Safety and Energy,2017,8(1):1-14. (in Chinese) [3] JIANG Li-jun. Research on Test and Evaluation Method of Autonomous Emergency Breaking System[D]. Shanghai:Tongji University, 2014. (in Chinese) [4] HUANG Li. Study on Field Test and Evaluation Methods of Partial Automated Vehicles[D]. Chongqing:Chongqing University, 2018. (in Chinese) [5] JT/T 1242-2019,Performance Requirements and Test Procedures for Advanced Emergency Braking System for Commercial Vehicle[S]. (in Chinese) [6] ZHOU Wei, LI Wen-liang, GUO Zhi-ping, et al. Study on Enhancement Coefficient of Washboard Road of Automobile Proving Ground[J].Journal of Highway and Transportation Research and Development, 2008, 25(11):140-144. (in Chinese) [7] CAI Tuan-jie, GUO Xiao-fen, ZHOU Wei, et al. Research on Simulation of Vehicle Reliability Road Test[J]. Journal of Highway and Transportation Research and Development,2009,26(8):149-152,158. (in Chinese) [8] ZHU An-ding, LIU Kang, CHEN Jin-yun,et al. Study of Enhancement Relationship of Automotive Proving Ground Road Based on Rain-flow Counting Method[J].Journal of Hefei University of Technology:Natural Science Edition,2013,36(12):1418-1421. (in Chinese) [9] GRUNDER B,SPECKERT M, POMPETZKI M. Design of Durability Sequences Based on Rainflow Matrix Optimization[R]. Detroit:SAE,1998. [10] MATTETTIA M,MOLARI G,VERTUA A. New Methodology for Accelerating the Four-post Testing of Tractors Using Wheel Hub Displacements[J]. Biosystems Engineering,2015,29:307-314. [11] YU Hai-bo. Research on Reliability Test Method of Proving Ground Correlated with Customers for Automotive Load Bearing System[D]. Changchun:Jilin University, 2008. (in Chinese) [12] MEN Yu-zhuo,LI Xian-sheng,YU Hai-bo. New Method for Automobile Reliability Test Correlated with Customers[J]. Chinese Journal of Mechanical Engineering,2008,44(2):223-229. (in Chinese) [13] MEN Yu-zhuo,YU Hai-bo,HAN Yu. New Method of Reliability Test for Powertrain of a Vehicle Correlated with Customers[J]. Journal of Vibration and Shock,2013,32(11):25-29,34. (in Chinese) [14] ELGHARBAWY M, SCHERHAUFER I, OBERHOLLENZER K,et al. Adaptive Functional Testing for Autonomous Trucks[J]. International Journal of Transportation Science and Technology, 2019,8(2):202-218. [15] ELGHARBAWY M, BERNIER B,FREY M. et al. An Agile Verification Framework for Traffic Sign Classification Algorithms in Heavy Vehicles[C]//2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). Agadir, Morocco:IEEE, 2016. [16] WANG Jian-qiang,WU Jian,LI Yang. Concept, Principle and Modeling of Driving Risk Field Based on Driver-vehicle-road Interaction[J]. China Journal of Highway and Transport,2016,29(1):105-114. (in Chinese) [17] GB/T 33577-2017,Intelligent Transportation Systems-Forward Vehicle Collision Warning System-Performance Requirements and Test Procedures[S]. (in Chinese) [18] ISO 22839, Intelligent Transport Systems-Forward Vehicle Collision Mitigation Systems-Operation, Performance and Verification Requirements[S]. |
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