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Research on Automatic Generation Method of Scenario Based on Panosim

Published: 17 April 2020 Publication History

Abstract

With the development of science and technology, L3 intelligent vehicles are gradually entering the mass production phase. Traditional testing tools and methods can hardly meet the requirements for multiple dimensions, high standard and big data of self-driving vehicles. The scenario-based simulation test method has great technical advantages in terms of test efficiency, verification cost and versatility, and is an important means for automatic driving test verification. However, it has shortcomings such as long scenario construction period and large repeatability. This paper is compiled based on secondary development of the automatic driving simulation software Panosim and presenting the automatic inputting of scenario and rapid adjustment of parameters through the digital twinning technology. In addition, the natural driving scenario database of China Automotive Technology and Research Center is used for verification. The results show that this method can improve the efficiency and accuracy of scenario construction, and greatly shorten the cycle of simulation test.

References

[1]
Google. What We're Driving at [EB/OL]. (2010-10-1)[2019-03-17]. http:googleblog.blogspot.com/2010/10/whatwere-driving-at.html.
[2]
Zhu Bing, Zhang Peixing, Zhao Jian. Research progress on virtual testing of automatic driving vehicles based on scenarios [J]. China Journal of Highway and Transport, 32(6): 1--19.
[3]
Gay K. Connected and automated vehicle research in the United States [R/OL]. US Department of Transportation, (2014-11-16). http://www.unece.org/fileadmin/DAM/trans/events/2014/Joint_BELGIUM-UNECE_ITS/02_ITS_Nov2014_Kevin_Gay_US_DOT.pdf
[4]
LI Shengbo, LI Keqiang, WANG Jianqiang, et al. Modeling and verification of heavy-duty truck drivers' car-following characteristics [J]. Int'l J Automotive Tech, 2010, 11(1): 81--87.
[5]
WANG Jianqiang, LI Shengbo, HUANG Xiaoyu, et al. Driving simulation platform applied to develop driving assistance systems [J]. IET Intell Transp Syst, 2010, 4(2):121--127.
[6]
Zanten A, Erhardt R, Pfaff G, et al. Control aspects of the Bosch-VDC [C] // Int Sympo on Adva Vehi Control, Aachen, Germany, 1996.
[7]
LI Daofei, DU Shangqian, YU Fan. Integrated vehicle chassis control based on direct yaw moment, active steering and active stabilizer [J]. Vehi Syst Dyna, 2008, 46(1): 341--351.
[8]
Lee C, Lee. Object recognition algorithm for adaptive cruise control of vehicle using laser scanning sensor [C] // 2000 IEEE Intell Trans Syst Conf, Dearborn, USA, Oct.1-3, 2000.[
[9]
Zhang Dezhao, Wang Jianqiang, Liu Jiaxi, et. al. Mode switch policy of continuous acceleration adaptive cruise control [J]. Journal of Tsinghua University (Science and Technology) [J], 2010, 50 (8): 1277~1281.
[10]
GUO J, DENG H, ZHANG S, et al. A novel method of radar modeling for vehicle intelligence [J].SAE International Journal of Passenger Cars-Electronic and Electrical Systems, 2016, 10(1):50--56.
[11]
BROGGI A, BUZZONI M, DEBATTISTI S, et al. Extensive tests of autonomous driving technologies [J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(3): 1403--1415.
[12]
HUANG W L, WANG K, LV Y, et al. Autonomous vehicles testing method review [C]//IEEE, Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), New York, IEEE, 2016: 163--168.
[13]
Chen Xudong. Design and research on virtual scenario of automobile simulation driving [D]. Chengdu: Southwest Jiaotong University, 2016.
[14]
LOPER M M, BLACK M J. OpenDR: An approximate differentiable renderer [C]//Springer. European Conference on Computer Vision. Berlin:Springer, 2014: 154--169.
[15]
JULLIEN J M, MARTEL C, VIGNOLLET L, et al. OpenScenario: A flexible integrated environment to develop educational activities based on pedagogical scenarios [C]//IEEE. Ninth IEEE International Conference on Advanced Learning Technologies. New York: IEEE, 2009:509--513.

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ICCDA '20: Proceedings of the 2020 4th International Conference on Compute and Data Analysis
March 2020
224 pages
ISBN:9781450376440
DOI:10.1145/3388142
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2020

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Author Tags

  1. Panosim
  2. automatic driving
  3. automatic generation
  4. digital twinning technology
  5. secondary development
  6. simulation test

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