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NIU Run-xin, XIA Jing-ting, WANG Xiao-hua, MEI Tao. Tentacle algorithm of obstacle avoidence and autonomous driving for intelligent vehicle[J]. Journal of Traffic and Transportation Engineering, 2010, 10(6): 53-58. doi: 10.19818/j.cnki.1671-1637.2010.06.009
Citation: NIU Run-xin, XIA Jing-ting, WANG Xiao-hua, MEI Tao. Tentacle algorithm of obstacle avoidence and autonomous driving for intelligent vehicle[J]. Journal of Traffic and Transportation Engineering, 2010, 10(6): 53-58. doi: 10.19818/j.cnki.1671-1637.2010.06.009
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Tentacle algorithm of obstacle avoidence and autonomous driving for intelligent vehicle

doi: 10.19818/j.cnki.1671-1637.2010.06.009
  • 1.

    Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, Anhui, China

  • 2.

    State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China

More Information
  • Author Bio:

    NIU Run-xin(1971-),male,associate researcher,PhD,+86-551-5393191, rxniu@iim.ac.cn

  • Received Date: 2010-07-27
  • Publish Date: 2010-12-25
    Abstract
  • Obstacle map was established by using velodyne radar. Tentacle regeneration, autonomous driving and avoiding obstacle strategy were analyzed. The influence of sideslip angle on tentacle regeneration was considered. INS (inertial navigation system) sensors with GPS were integrated by Kalman filter, and the longitudinal velocity and lateral velocity of vehicle were obtained. Therefore, tentacle regeneration method corresponding to specific "speed sets" based on sideslip angle identification was promoted. At low-medium speed, trajectory error reduces from 0.40 m to 0.20 m. At high speed, because of using good-performance controller and appropriate fusion parameters, trajectory error reduces from 1.00 m to 0.75 m. Analysis result indicates that the promoted method effectively ensures obstacle avoidence and autonomous driving.

     

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    • References(13)
  • [1]
    YOUFeng. Study on autonomouslane changing and autonomous overtaking control method of intelligent vehicle[D]. Changchun: Jilin University, 2005. (in Chinese)
    [2]
    SHEN Zhong-jie, WANG Wu-hong, HOU Fu-guo, et al. Architecture and key techniques of digital driving system with in intelligent transport information network environment[J]. Journal of Traffic and Transportation Engineering, 2002, 2 (1): 96-100. (in Chinese) http://transport.chd.edu.cn/article/id/200201021
    [3]
    FLETCHER L, TELLERS, OLSONE, et al. The mit-cornellcollision and why it happened[J]. Journal of Field Robotics, 2008, 25 (10): 775-807. doi: 10.1002/rob.20266
    [4]
    MCBRIDE J R, I VANJ C, RHODE D S, et al. A perspec-tive on emerging automotive safety applications, derived from lessons learned through participation in the DARPA grandchallenges[J]. Journal of Field Robotics, 2008, 25 (10): 808-840. doi: 10.1002/rob.20264
    [5]
    PATZ B J, PAPELIS Y, PILLAT R, et al. A practical approach to robotic design for the DARPA urban challenge[J]. Journal of Field Robotics, 2008, 25 (8): 528-566. doi: 10.1002/rob.20251
    [6]
    URMSON C, ANHALT J, BAGNELL D, et al. Autono-mous driving in urban environments: boss and the urban challenge[J]. Journal of Field Robotics, 2008, 25 (8): 425-466. doi: 10.1002/rob.20255
    [7]
    SUKRJ K, JUNG H G, BAE K, et al. Outlier rejection for cameras on intelligent vehicle[J]. Pattern Recognition Letters, 2008, 29 (6): 828-840. doi: 10.1016/j.patrec.2007.11.019
    [8]
    ORDONEZ C, COLLI NS JR E G, SELEKWA M F, et al. The virtual wall approach to li mit cycle avoidance forunmanned ground vehicle[J]. Robotics and AutonomousSystems, 2008, 56 (8): 645-657.
    [9]
    THRUN S, MONTEMERLO M, DAHLKAMP H, et al. Stanley: the robot that won the DARPA grand challenge[J]. Journal of Field Robotics, 2006, 23 (9): 661-692. doi: 10.1002/rob.20147
    [10]
    KASTRI NAKI V, ZERVAKIS M, KALAITZAKIS K. Asurvey of video processing techniques for traffic applications[J]. I mage and Vision Computing, 2003, 21 (4): 359-381.
    [11]
    HORI UCHI S, OKADA K, NOHTOMI S. I mprovement ofvehicle handling by nonlinear integrated control of four wheelsteering and four wheel torque[J]. JSAE Review, 1999, 20 (4): 459-464. doi: 10.1016/S0389-4304(99)00051-X
    [12]
    GAO Tao, LI UZheng-guang, YUE Shi-hong, et al. Movingvehicle tracking algorithm used for intelligent traffic[J]. China Journal of Highway and Transport, 2010, 23 (3): 89-94. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201003015.htm
    [13]
    ZHENG Jian-xia. Infra-red intelligent vehicle classification counter[J]. Journal of Chang an University: Natural ScienceEdition, 2003, 23 (3): 85-87. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL200303024.htm
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