Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 674-681.doi: 10.13229/j.cnki.jdxbgxb20221434

Previous Articles    

Expected trajectory prediction of vehicle considering surrounding vehicle information

Yan-tao TIAN1(),Fu-qiang XU1,Kai-ge WANG1,Zi-xu HAO1,2   

  1. 1.College of Communication Engineering，Jilin University，Changchun 130022，China
    2.College of Transportation，Jilin University，Changchun 130022，China
  • Received:2022-11-12 Online:2023-03-01 Published:2023-03-29

RICH HTML

 4   

PDF (PC)

113

Abstract:

Aiming at the problem of driver's expected trajectory prediction， a vehicle expected trajectory prediction model considering the information of surrounding vehicles was designed. Separate encoders of self vehicle and surrounding vehicles historical track information is established， and the encoded self vehicle historical track information is sent to the intention recognition module to identify the driver's intention. The self vehicle and surrounding vehicles coding information is processed through the attention mechanism， the result of intention recognition is used as the input of the decoder module， and the future position of the vehicle is output. Finally,the data set is used to train the model， and the effectiveness of the model is verified.

Key words: vehicle engineering, trajectory prediction, neural network, attention mechanism, preview point

CLC Number: 

  • U495

Fig.1

Overall framework of the model"

Fig.2

Schematic diagram of vehicle identification"

Fig.3

GRU cycle unit structure diagram"

Fig.4

Schematic diagram of prediction results"

Fig.5

Explanation of track classification basis"

Table 1

Comparison of prediction ability of different models"

预测时间/sRMSE/m
I-ENDEIA-ENDES-ENDED-ENDE
10.310.280.260.25
20.550.500.470.43
30.960.830.810.78
41.311.241.211.18

Fig.6

Comparison of prediction ability of different models"

Fig.7

Lane change prediction trajectory results"

Fig.8

Lane change prediction attention distribution"

Fig.9

Importance scores of surrounding vehicles"

Fig.10

Lane keeping prediction trajectory results and importance score"

1 Doshi A, Trivedi M M. On the roles of eye gaze and head dynamics in predicting driver's intent to change lanes[J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(3): 453-462.
2 Ohn-Bar E, Trivedi M M. Looking at humans in the age of self-driving and highly automated vehicles[J]. IEEE Transactions on Intelligent Vehicles, 2016, 1 ( 1 ) : 90-104.
3 郭应时, 付锐, 赵凯, 等. 驾驶人换道意图实时识别模型评价及测试 [J]. 吉林大学学报: 工学版, 2016, 46(6): 1836-1844.
Guo Ying-shi, Fu Rui, Zhao Kai, et al. Evaluation and test of real-time identification models of driver's lane change intention[J]. Journal of Jilin University (Engineering and Technology Edition), 2016, 46(6): 1836-1844.
4 Li K, Wang X, Xu Y, et al. Lane changing intention recognition based on speech recognition models[J].Transportation Research Part C-emerging Technologies, 2016, 69: 497-514.
5 Jain A, Koppula H S, Raghavan B, et al. Car that knows before you do: anticipating maneuvers via learning temporal driving models[C]∥2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3182-3190.
6 Driggs-Campbell K, Bajcsy R. Identifying modes of intent from driver behaviors in dynamic environments[C]∥2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 2015: 739-744.
7 Deo N, Rangesh A, Trivedi M M. How would surround vehicles move? A unified framework for maneuver classification and motion prediction[J]. IEEE Transactions on Intelligent Vehicles, 2018, 3(2): 129-140.
8 Bahram M, Hubmann C, Lawitzky A, et al. A combined model and learning-based framework for interaction-aware maneuver prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(6): 1538-1550.
9 Bahram M, Lawitzky A, Friedrichs J, et al. A game-theoretic approach to replanning-aware interactive scene prediction and planning[J]. IEEE Transactions on Vehicular Technology, 2016, 65(6): 3981-3992.
10 Shalev-Shwartz S, Ben-Zrihem N, Cohen A, et al. Long-term planning by short-term prediction[J/OL]. [2022-10-15].
11 季学武, 费聪, 何祥坤, 等. 基于LSTM网络的驾驶意图识别及车辆轨迹预测[J]. 中国公路学报, 2019, 32(6): 34-42.
Ji Xue-wu, Fei Cong, He Xiang-kun, et al. Intention recognition and trajectory prediction for vehicles using LSTM network[J]. China Journal of Highway and Transport, 2019, 32(6): 34-42.
12 Deo N, Trivedi M M. Convolutional social pooling for vehicle trajectory prediction[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA,2018:1549-1557.
13 Park S H, Kim B, Kang C M, et al. Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture[C]∥2018 IEEE Intelligent Vehicles Symposium, Changshu, China, 2018:1672-1678.
14 柴进. 基于聚类和LSTM算法的车辆轨迹预测模型研究[D]. 北京: 北京交通大学电子信息工程学院, 2020.
Chai Jin. On vehicle trajectory prediction model based on combined clustering and LSTM algorithm [D]. Beijing:School of Electronic and Information Engineering, Beijing Jiaotong University, 2020.
15 Graves A, Jaitly N. Towards end-to-end speech recognition with recurrent neural networks[C]∥International Conference on Machine Learning, Beijing, China, 2014: 1764-1772.
16 Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks[C]∥International Conference on Neural Information Processing Systems, Montreal, Canada, 2014: 3104-3112.
17 Cho K, van Merriënboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J/OL]. [2022-10-18].
[1] Ke HE,Hai-tao DING,Xuan-qi LAI,Nan XU,Kong-hui GUO. Wheel odometry error prediction model based on transformer [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 653-662.
[2] Ke HE,Hai-tao DING,Nan XU,Kong-hui GUO. Enhanced localization system based on camera and lane markings [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 663-673.
[3] 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.
[4] 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.
[5] Deng-feng WANG,Hong-li CHEN,Jing-xin NA,Xin CHEN. Failure comparison of single and double lap joints after high temperature aging [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 346-354.
[6] Pei ZHANG,Zhi-wei WANG,Chang-qing DU,Fu-wu YAN,Chi-hua LU. Oxygen excess ratio control method of proton exchange membrane fuel cell air system for vehicle [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 1996-2003.
[7] Ke-yong WANG,Da-tong BAO,Su ZHOU. Data-driven online adaptive diagnosis algorithm towards vehicle fuel cell fault diagnosis [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2107-2118.
[8] Qi-ming CAO,Hai-tao MIN,Wei-yi SUN,Yuan-bin YU,Jun-yu JIANG. Hydrothermal characteristics of proton exchange membrane fuel cell start⁃up at low temperature [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2139-2146.
[9] Hai-lin KUI,Ze-zhao WANG,Jia-zhen ZHANG,Yang LIU. Transmission ratio and energy management strategy of fuel cell vehicle based on AVL⁃Cruise [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2119-2129.
[10] Yan LIU,Tian-wei DING,Yu-peng WANG,Jing DU,Hong-hui ZHAO. Thermal management strategy of fuel cell engine based on adaptive control strategy [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2168-2174.
[11] Cheng LI,Hao JING,Guang-di HU,Xiao-dong LIU,Biao FENG. High⁃order sliding mode observer for proton exchange membrane fuel cell system [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2203-2212.
[12] Xun-cheng CHI,Zhong-jun HOU,Wei WEI,Zeng-gang XIA,Lin-lin ZHUANG,Rong GUO. Review of model⁃based anode gas concentration estimation techniques of proton exchange membrane fuel cell system [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 1957-1970.
[13] Yao-wang PEI,Feng-xiang CHEN,Zhe HU,Shuang ZHAI,Feng-lai PEI,Wei-dong ZHANG,Jie-ran JIAO. Temperature control of proton exchange membrane fuel cell thermal management system based on adaptive LQR control [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2014-2024.
[14] Yong-ming LI,Xiao-xuan PEI,Shu-dong YI. Adaptive neural network optimal control of hybrid electric vehicle power battery [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2063-2068.
[15] Guang-di HU,Hao JING,Cheng LI,Biao FENG,Xiao-dong LIU. Multi⁃objective sliding mode control based on high⁃order fuel cell model [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2182-2191.
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