Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (1): 273-284.doi: 10.13229/j.cnki.jdxbgxb20210493

Previous Articles    

Driver's lane change intention recognition in snow and ice environment based on neural network

Hui-qiu LU1(),Feng ZHAO1,Bo XIE1,Yan-tao TIAN1,2   

  1. 1.College of Communication Engineering，Jilin University，Changchun 130012，China
    2.Key Laboratory of Bionic Engineering，Ministry of Education，Jilin University，Changchun 130022，China
  • Received:2021-06-01 Online:2023-01-01 Published:2023-07-23

RICH HTML

 7   

PDF (PC)

181

Abstract:

The process of driving a car can be affected by many factors. Different drivers have different operating habits and different driving ways under different road conditions. Therefore， in this paper， driving characteristics in snow and ice covered environment were analyzed. Based on Carmaker， the hardware-in-the-loop driving simulation platform， data such as driver behavior parameters and vehicle parameters were collected under this condition （including the bifurcated road surface）， characteristic parameters were analyzed and selected， and data sample database was established. The intention recognition model was established based on neural network， and the model was verified in single condition and compound condition respectively. The performance of the model was compared and analyzed. It can be obtained that the model can accurately identify the driver's intention on the bifurcated road.

Key words: engineering of communication and transportation system, driver intention, neural network, driving simulation, the simulation analysis

CLC Number: 

  • U491.2

Fig.1

Man-machine cooperative system structure"

Fig.2

Simulation driving experimental platform"

Fig.3

Experimental roadmap"

Fig.4

Road environment and traffic flow"

Fig.5

Lane keeping and left-right lane changing conditions"

Fig.6

Carmaker parameter setting interface"

Fig.7

Comparison diagram A of vehicle parameters"

Fig.8

Comparison diagram B of vehicle parameters"

Fig.9

Speed change during lane change"

Fig.10

Boxplot of original distribution characteristics of driving state variable data"

Fig.11

Neural network topology"

Fig.12

Activation function"

Fig.13

Neural network structure of intention recognition model"

Fig.14

Driving intention recognition process"

Fig.15

Lateral displacement trajectories of vehicles on different road surfaces"

Fig.16

Result of intention model recognition with normal road data"

Fig.17

Result of intention model recognition with off road data"

Fig.18

Model recognition results of lane keeping condition"

Fig.19

Recognition results of left lane change working condition model"

Fig. 20

Model recognition results of right-lane change working conditions"

Fig.21

Recognition results of composite working condition model"

1 李克强.汽车技术的发展动向及我国的对策[J]. 汽车工程,2009, 31(11): 1005-1016.
Li Ke-qiang. Development trend of automobile technology and my country's countermeasures[J]. Automotive Engineering, 2009, 31(11): 1005-1016.
2 Yun M, Zhao J, Zhao J, et al. Impact of in-vehicle navigation information on lane-change behavior in urban expressway diverge segments[J]. Accident: Analysis and prevention, 2017, 106: 53-66.
3 秦恩国. 我国道路交通事故现状与对策研究[J]. 内燃机与配件, 2018 (16): 184-185.
Qin En-guo. Research on the current situation and countermeasures of road traffic accidents in my country[J]. Internal Combustion Engine & Parts, 2018 (16): 184-185.
4 孙亮. 模式识别原理[M]. 北京: 北京工业大学出版社, 2009.
5 李航. 统计学习方法[M]. 北京: 清华大学出版社, 2012.
6 Jang Y, Mallipeddi R, Lee M. Driver's lane-change intent identification based on pupillary variation[C]// IEEE International Conference on Consumer Electronics, Shenzhen, China, 2014: 197-198.
7 李慧, 谭盛彬, 张硕. 支持向量机纯电动客车驾驶意图识别[J]. 长春工业大学学报, 2017, 38(2): 133-137.
Li Hui, Tan Sheng-bin, Zhang Shuo. Support vector machine pure electric bus driving intention recognition[J]. Journal of Changchun University of Technology, 2017, 38(2): 133-137.
8 宗长富, 杨肖, 王畅, 等. 汽车转向时驾驶人驾驶意图辨识与行为预测[J]. 吉林大学学报:工学版, 2009,49(1): 27-32.
Zong Chang-fu, Yang Xiao, Wang Chang, et al. Driver's driving intention recognition and behavior prediction when a car is turning[J]. Journal of Jilin University(Engineering and Sciences Edition), 2009,49(1): 27-32.
9 Xu G, Liu L, Song Z. Driver behavior analysis based on Bayesian network and multiple classifiers[C]//Intelligent Computing and Intelligent Systems, Beijing,China,2010: 663-668.
10 王庆年, 王俊, 陈慧勇, 等. 混合动力车辆中的加速与制动意图识别[J]. 吉林大学学报:工学版, 2014,44(1): 281-286.
Wang Qing-nian, Wang Jun, Chen Hui-yong, et al. Acceleration and braking intention recognition in hybrid electric vehicles[J]. Journal of Jilin University(Engineering and Sciences Edition), 2014,44(1): 281-286.
11 Kim I, Bong J, Park J, et al. Prediction of driver's intention of lane change by augmenting sensor information using machine learning techniques[J]. Sensors, 2017, 17(6): 1350.
12 李青林, 彭金栓, 付锐, 等. 基于驾驶人操纵特性和交通环境状态的换道行为预测[J]. 科学技术与工程, 2019, 19(12): 368-377.
Li Qing-lin, Peng Jin-shuan, Fu Rui, et al. Prediction of lane changing behavior based on driver's handling characteristics and traffic environment state[J]. Science Technology and Engineering, 2019, 19(12): 368-377.
[1] Feng LYU,Nian LI,Zhuang-zhuang FENG,Yang-hang ZHANG. Method of collaborative filtering recommendation of personalized product-service system based on user [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 1935-1942.
[2] Wei-tiao WU,Kun ZENG,Wei ZHOU,Peng LI,Wen-zhou JIN. Deep learning method for bus passenger flow prediction based on multi-source data and surrogate-based optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 2001-2015.
[3] Peng-ju LIU. Design of automatic identification algorithm for Internet of Things security situation based on deep neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 2121-2126.
[4] Lei CHEN,Yang WANG,Zhi-sheng DONG,Ya-qi SONG. A vehicle agility control strategy based on steering intent [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1257-1263.
[5] Pei-yong LIU,Jie DONG,Luo-feng XIE,Yang-yang ZHU,Guo-fu YIN. Surface defect detection algorithm of magnetic tiles based on multi⁃branch convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1449-1457.
[6] Zhen-hai ZHANG,Kun JI,Jian-wu DANG. Crack identification method for bridge based on BCEM model [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1418-1426.
[7] Can-can SONG,Di-fei JING,Jun-feng XIE,Ke-xin KANG. Analysis on driving behaviors on flat curved sections of highways with advertising signs [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1345-1354.
[8] Hong-fei JIA,Ying-jun XU,Li-li YANG,Nan WANG. League member selection and benefit distribution of commercial vehicles multi⁃modal transportation [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1060-1069.
[9] Chao SUN,Hao-wei YIN,Wen-yun TANG,Zhao-ming CHU. Sensor deployment strategy and expansion inference of mobile phone data for traffic demand estimation [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1070-1077.
[10] Yan-tao TIAN,Fu-qiang XU,Kai-ge WANG,Zi-xu HAO. Expected trajectory prediction of vehicle considering surrounding vehicle information [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 674-681.
[11] Ya-jing YU,Jian GUO,Rong-hao WANG,Wei QIN,Ming-wu SONG,Zheng-rong XIANG. Time⁃varying formation control of multi⁃quadrotor unmanned aerial vehicles based on state observer [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 871-882.
[12] Shuai-shuai ZHANG,Yan-fang YIN,Lin-jing XIAO,Shuai JIANG. Static gait planning method for quadruped robot based on gate recurrent neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 902-912.
[13] Rong-han YAO,Wen-tao XU,Wei-wei GUO. Drivers' takeover behavior and intention recognition based on factor and long short⁃term memory [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 758-771.
[14] Jin-Zhen Liu,Guo-Hui Gao,Hui Xiong. Multi⁃scale attention network for brain tissue segmentation [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 576-583.
[15] Hui-zhen ZHANG,Zheng-kai GAO,Jian-qiang LI,Chen-xi WANG,Yu-biao PAN,Cheng WANG,Jing WANG. Short⁃term passenger flow forecasting of urban rail transit based on recurrent neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 430-438.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd