Vehicle Lane Change Intention Recognition Driven by Trajectory Data

被引：0
|
作者：
Zhao J.-D. [1 ,2 ]
Zhao Z.-M. [1 ]
Qu Y.-C. [3 ]
Xie D.-F. [1 ]
Sun H.-J. [1 ]
机构：
[1] School of Traffic and Transportation, Beijing Jiaotong University, Beijing
[2] Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing
[3] State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2022年 / 22卷 / 04期
基金：
中国国家自然科学基金;
关键词：
attention mechanism; data-driven; gated recurrent unit neural network; intelligent transportation; lane change intention recognition;
D O I：
10.16097/j.cnki.1009-6744.2022.04.007
中图分类号：
学科分类号：
摘要：
In order to accurately identify the vehicle's lane-changing intention and improve the driving safety of the vehicle, I comprehensively considered the spatiotemporal characteristics of the vehicle's lane-changing process and the influence of different characteristics on the vehicle, and proposed a lane-changing intention recognition model with attention mechanism, which is based on the combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit Neural Network (GRU). Firstly, I filtered and smoothed the vehicle trajectory data, and divided the vehicle trajectory data into three categories: left lane change, right lane change, and straight driving, so as to construct a sample set of lane change intention. Secondly, I built a CNN_GRU model that integrates attention mechanism to identify the sample set of lane change intention. Considering the interaction between vehicles during driving, I utilized the position, the speed information of the predicted vehicle and surrounding vehicles as the input of the model. After the CNN layer feature extraction, I then chose the extracted features as the input of GRU layer. And I also added different weight coefficients to different features through the attention mechanism layer, and leveraged the Softmax layer to identify the lane change intention. Finally, I verified the performance of CNN_GRU model with fused attention mechanism by using the trajectory data of US-101 dataset in NGSIM, and at the same time, compared and analyzed it with LSTM, GRU, CNN_GRU and CNN_LSTM_Att models. The results showed that the proposed model achieves an overall accuracy of 97.37% for vehicle lane change intention recognition with an iteration time of 6.66 s, which is at most 9.89% and at least 2.1% improvement in accuracy compared to other models. By analyzing the accuracy of intention recognition at different pre-determination times, we know that the intention to change lanes can be accurately recognized within 2 s before the vehicle changes lanes, and the accuracy rate is above 89%, so the model has good recognition performance. © 2022 Science Press. All rights reserved.
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页码：63 / 71
页数：8
相关论文
共 20 条
  • [1] LIU A, PENTLAND A., Towards real-time recognition of driver intentions, IEEE Conference on Intelligent Transportation System, Itsc. IEEE, pp. 236-241, (1997)
  • [2] ZHANG Y H, ZHANG F D., Recognition and analysis of driver's lane changing behavior based on HMM model, Electronic Science and Technology, 29, 6, pp. 34-36, (2016)
  • [3] QU W Q., Research on vehicle driving behavior recognition and trajectory prediction method, (2021)
  • [4] SONG X L, ZHENG Y Q, CAO H T., Research on driver's lane change intention recognition based on HMM-SVM, Journal of Electronic Measurement and Instrumentation, 30, 1, pp. 58-65, (2016)
  • [5] XU T, WEN C L, ZHANG X, Et al., Lane-changing behavior recognition of urban roads based on GMM-CHMM, Transportation System Engineering and Information, 20, 1, pp. 61-67, (2020)
  • [6] DOGAN U, EDELBRUNNER J, IOSSIFIDIS I., Autonomous driving: A comparison of machine learning techniques by measns of the prediction of lane change behavior, Karon Beach: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011), (2011)
  • [7] CHEN L, FENG Y C, LI Q R., Research on vehicle lane changing behavior recognition model based on Multi-class SVM, Journal of Safety and Environment, 20, 1, pp. 193-199, (2020)
  • [8] HEI K X, QU D Y, ZHOU J C, Et al., Lane-changing behavior recognition of driving vehicles based on random forest decision tree, Journal of Qingdao University of Technology, 41, 1, pp. 115-120, (2020)
  • [9] JIANG S Y, LI C, LEI Y, Et al., Research on driver's lane change decision recognition based on natural driving data, Automotive Technology, 2022, 1
  • [10] HU X., Identification of intelligent vehicle lane changing behavior based on function distribution characteristics, Intelligent Computer and Application, 11, 7, pp. 91-94, (2021)
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