Volume 22 Issue 3
Jun.  2022
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(3): 115-125
SHI Yu-chen, YAN Song, YAO Dan-ya, ZHANG Yi. SVM-LSTM-based car-following behavior recognition and information credibility discirmination[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 115-125. doi: 10.19818/j.cnki.1671-1637.2022.03.009
Citation: SHI Yu-chen, YAN Song, YAO Dan-ya, ZHANG Yi. SVM-LSTM-based car-following behavior recognition and information credibility discirmination[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 115-125. doi: 10.19818/j.cnki.1671-1637.2022.03.009
shu

SVM-LSTM-based car-following behavior recognition and information credibility discirmination

doi: 10.19818/j.cnki.1671-1637.2022.03.009
  • 1.

    School of Information Science and Technology, Tsinghua University, Beijing 100084, China

  • 2.

    Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China

  • 3.

    Collaborative Innovation Center of Modern Urban Traffic Technologies, Southest University, Nanjing 210096, Jiangsu, China

  • 4.

    Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, Guangdong, China

Funds:

National Key Research and Development Program of China 2018YFB1600600

More Information
    Abstract
  • To effectively improve the traffic system security by using the real-time interaction information in intelligent vehicle-infrastructure cooperative systems (i-VICS), a credibility discrimination approach for traffic information based on the traffic business features was proposed. In particular, the model for the car-following behavior recognition and the information credibility discrimination was built based on the support vector machine (SVM) and long short-term memory (LSTM) neural network. It was composed of the SVM-based car-following behavior recognition model and the LSTM neural network-based car-following speed prediction model. The feature vector representing the vehicle driving states was set, and the vehicle driving states were divided into the following and non-following by the SVM-based car-following behavior recognition model. For following vehicles, their speeds were predicted by the LSTM neural network-based car-following speed prediction model according to the history data. With the SVM-LSTM-based information credibility discrimination model, the credibility of vehicle data was judged by checking whether the difference between the predicted speed and the actual speed of the following vehicles was within the reasonable range, and in this way, the information credibility discrimination was achieved. The public dataset was employed to train and test the proposed models, and several abnormal test datasets of various abnormity types and abnormity amplitude were built to verify the SVM-LSTM neural network-based model for the car-following behavior recognition and the information credibility discrimination. Research results show that the vehicle driving behavior recognition accuracy of the SVM-based car-following behavior recognition model is up to 99%, and the predicted car-following speed precision with an order of magnitude of cm·s-1 can be achieved by the LSTM neural network-based car-following speed prediction model. The discrimination accuracy of the SVM-LSTM neural network-based model for the car-following behavior recognition and information credibility discrimination is up to 97% on the normal test datasets and multiple abnormal test datasets. Thus, the proposed approach can be applied for the real-time information credibility discriminations of road side units (RSUs) to on-board units (OBUs) and between OBUs. 8 tabs, 9 figs, 30 refs.

     

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    • References(30)
  • [1]
    张毅, 姚丹亚, 李力, 等. 智能车路协同系统关键技术与应用[J]. 交通运输系统工程与信息, 2021, 21(5): 40-51. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202105006.htm

    ZHANG Yi, YAO Dan-ya, LI Li, et al. Technologies and applications for intelligent vehicle-infrastructure cooperation systems[J]. Transportation Systems Engineering and Information Technology, 2021, 21(5): 40-51. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202105006.htm
    [2]
    MIYAJIMA C, NISHIWAKI Y, OZAWA K, et al. Driver modeling based on driving behavior and its evaluation in driver identification[J]. Proceedings of the IEEE, 2007, 95(2): 427-437. doi: 10.1109/JPROC.2006.888405
    [3]
    WANG Da-jun, PEI Xin, LI Li, et al. Risky driver recognition based on vehicle speed time series[J]. IEEE Transactions on Human-Machine Systems, 2018, 48(1): 63-71. doi: 10.1109/THMS.2017.2776605
    [4]
    NEWELL G F. A simplified car-following theory: a lower order model[J]. Transportation Research Part B: Methodological, 2002, 36(3): 195-205. doi: 10.1016/S0191-2615(00)00044-8
    [5]
    YEO H. Asymmetric microscopic driving behavior theory[D]. Berkeley: University of California, 2008.
    [6]
    WEI Da-li, LIU Hong-chao. Analysis of asymmetric driving behavior using a self-learning approach[J]. Transportation Research Part B: Methodological, 2013, 47: 1-14. doi: 10.1016/j.trb.2012.09.003
    [7]
    WONG W, HUANG Shi-hong, FENG Yi-heng, et al. Trajectory-based hierarchical defense model to detect cyber-attacks on transportation infrastructure[R]. Washington DC: Transportation Research Board, 2019.
    [8]
    HUANG S E, FENG Yi-heng, LIU H X. A data-driven method for falsified vehicle trajectory identification by anomaly detection[J]. Transportation Research Part C: Emerging Technologies, 2021, 128: 103196. doi: 10.1016/j.trc.2021.103196
    [9]
    祝俪菱, 刘澜, 赵新朋, 等. 基于支持向量机的车辆驾驶行为识别研究[J]. 交通运输系统工程与信息, 2017, 17(1): 91-97. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201701015.htm

    ZHU Li-ling, LIU Lan, ZHAO Xin-peng, et al. Driver behavior recognition based on support vector machine[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(1): 91-97. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201701015.htm
    [10]
    戢晓峰, 卢梦媛, 覃文文. 货车移动遮断影响下的小客车驾驶行为识别[J]. 交通运输系统工程与信息, 2021, 21(5): 174-182. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202105018.htm

    JI Xiao-feng, LU Meng-yuan, QIN Wen-wen, et al. Passenger cars driving behaviors recognition under truck movement interruption[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 174-182. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202105018.htm
    [11]
    BRACKSTONE M, MCDONALD M. Car-following: a historical review[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 1999, 2(4): 181-196. doi: 10.1016/S1369-8478(00)00005-X
    [12]
    王殿海, 金盛. 车辆跟驰行为建模的回顾与展望[J]. 中国公路学报, 2012, 25(1): 115-127. doi: 10.3969/j.issn.1001-7372.2012.01.018

    WANG Dian-hai, JIN Sheng. Review and outlook of modeling of car following behavior[J]. China Journal of Highway and Transport, 2012, 25(1): 115-127. (in Chinese) doi: 10.3969/j.issn.1001-7372.2012.01.018
    [13]
    CHANDLER R E, HERMAN R, MONTROLL E W. Traffic dynamics: studies in car following[J]. Operations Research, 1958, 6(2): 165-184. doi: 10.1287/opre.6.2.165
    [14]
    GIPPS P G. A behavioural car-following model for computer simulation[J]. Transportation Research Part B: Methodological, 1981, 15(2): 105-111. doi: 10.1016/0191-2615(81)90037-0
    [15]
    KIKUCHI S, CHAKROBORTY P. Car-following model based on fuzzy inference system[J]. Transportation Research Record, 1992(1365): 82-91.
    [16]
    杨达, 蒲云, 祝俪菱, 等. 改进的基于安全距离的车辆跟驰模型[J]. 北京工业大学学报, 2013, 39(9): 1335-1342. https://www.cnki.com.cn/Article/CJFDTOTAL-BJGD201309009.htm

    YANG Da, PU Yun, ZHU Li-ling, et al. Improved safe distance car-following model[J]. Journal of Beijing University of Technology, 2013, 39(9): 1335-1342. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BJGD201309009.htm
    [17]
    杨龙海, 赵顺, 罗沂. 基于期望跟驰间距的跟驰模型研究[J]. 武汉理工大学学报(交通科学与工程版), 2016, 40(4): 580-584, 591. doi: 10.3963/j.issn.2095-3844.2016.04.003

    YANG Long-hai, ZHAO Shun, LUO Yi. Car-following model based on expected headway[J]. Journal of Wuhan University of Technology (Transportation Science and Engineering), 2016, 40(4): 580-584, 591. (in Chinese) doi: 10.3963/j.issn.2095-3844.2016.04.003
    [18]
    杨龙海, 王晖, 李帅, 等. 改进分子动力学的车辆跟驰模型[J]. 重庆大学学报, 2021, 44(7): 26-33. https://www.cnki.com.cn/Article/CJFDTOTAL-FIVE202107003.htm

    YANG Long-hai, WANG Hui, LI Shuai, et al. Car-following model with improved molecular dynamics[J]. Journal of Chongqing University, 2021, 44(7): 26-33. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-FIVE202107003.htm
    [19]
    邱小平, 孙若晓, 杨达, 等. 考虑速度对期望间距影响的车辆跟驰模型研究[J]. 计算机应用研究, 2015, 32(6): 1649-1652. doi: 10.3969/j.issn.1001-3695.2015.06.011

    QIU Xiao-ping, SUN Ruo-xiao, YANG Da, et al. Consider impact of speed on desired distance car-following model[J]. Application Research of Computers, 2015, 32(6): 1649-1652. (in Chinese) doi: 10.3969/j.issn.1001-3695.2015.06.011
    [20]
    ELAMRANI ABOU ELASSAD Z, MOUSANNIF H, AL MOATASSIME H, et al. The application of machine learning techniques for driving behavior analysis: a conceptual framework and a systematic literature review[J]. Engineering Applications of Artificial Intelligence, 2020, 87: 103312. doi: 10.1016/j.engappai.2019.103312
    [21]
    WANG Xiao, JIANG Rui, LI Li, et al. Capturing car-following behaviors by deep learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3): 910-920. doi: 10.1109/TITS.2017.2706963
    [22]
    HUANG Xiu-ling, SUN Jie, SUN Jian. A car-following model considering asymmetric driving behavior based on long short-term memory neural networks[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 346-362. doi: 10.1016/j.trc.2018.07.022
    [23]
    HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. doi: 10.1162/neco.1997.9.8.1735
    [24]
    HE Zheng-bing. Research based on high-fidelity NGSIM vehicle trajectory datasets: a review[R]. Beijing: Beijing University of Technology, 2017.
    [25]
    THIEMANN C, CHRISTIAN M, KESTING A, et al. Estimating acceleration and lane-changing dynamics from next generation simulation trajectory data[J]. Transportation Research Record, 2008(2088): 90-101.
    [26]
    李依, 赵建有, 叶云涛. NGSIM轨迹重构研究综述[J]. 汽车实用技术, 2021, 46(4): 204-208. https://www.cnki.com.cn/Article/CJFDTOTAL-SXQC202104063.htm

    LI Yi, ZHAO Jian-you, YE Yun-tao. A review of NGSIM trajectory reconstruction[J]. Automobile Applied Technology, 2021, 46(4): 204-208. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SXQC202104063.htm
    [27]
    DING Chen-xi, WANG Wu-hong, WANG Xiao, et al. A neural network model for driver's lane-changing trajectory prediction in urban traffic flow[J]. Mathematical Problems in Engineering, 2013, 2013: 967358.
    [28]
    TANG Jun-jun, LIU Fang, ZHANG Wen-hui, et al. Lane-changes prediction based on adaptive fuzzy neural network[J]. Expert Systems with Applications, 2018, 91: 452-463. doi: 10.1016/j.eswa.2017.09.025
    [29]
    许伦辉, 胡三根, 伍帅, 等. 考虑车辆运行特性的双车道超车模型[J]. 华南理工大学学报(自然科学版), 2015, 43(4): 7-13, 27. https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG201504003.htm

    XU Lun-hui, HU San-gen, WU Shuai, et al. Overtaking model for two-lane highway considering vehicle running characteristics[J]. Journal of South China University of Technology (Natural Science Edition), 2015, 43(4): 7-13, 27. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG201504003.htm
    [30]
    李峰, 陈新. 基于LTE-V2X技术的PKI系统架构探讨[J]. 信息技术与网络安全, 2020, 39(7): 41-47. https://www.cnki.com.cn/Article/CJFDTOTAL-WXJY202007007.htm

    LI Feng, CHEN Xin. PKI infrastructure architecture based on LTE-V2X[J]. Information Technology and Network Security, 2020, 39(7): 41-47. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WXJY202007007.htm
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