图注意力模式下融合高精地图的周车轨迹预测

doi:10.13229/j.cnki.jdxbgxb20221259

Abstract

Abstract:

In order to accurately and reasonably predict the future trajectories of vehicles and understand the changes of surrounding traffic flow, a trajectory prediction method combined with high definition map in graph attention mode was proposed. The encoder-decoder framework based on LSTM network was designed, and the model structure with vehicle historical status and high-precision map information as input was established. A graph query mechanism combining local and global features of vehicles was proposed to output vehicle prediction trajectory. The results of experiments carried out on the nuScenes dataset show that the comprehensive prediction performance of our model is better than other state-of-the-art methods， such as Traj++， CoverNet， etc.， and it has good anti-interference.

Key words: vehicle engineering, trajectory prediction, long-short term memory network, graph attention network, high definition map

CLC Number:

U495

Yan-ran LIU,Qing-yu MENG,Hong-yan GUO,Jia-lin LI. Vehicle trajectory prediction combined with high definition map in graph attention mode[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 792-801.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Fig.6

Fig.7

Fig.8

References 21

1	Hou Lian, Xin Long, Li Sheng-bo, et al. Interactive trajectory prediction of surrounding road users for autonomous driving using structural-LSTM network[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(11): 4615-4625.
2	Ess A, Schindler K, Leibe B, et al. Object detection and tracking for autonomous navigation in dynamic environments[J]. The International Journal of Robotics Research, 2010, 29(14): 1707-1725.
3	李建平. 面向智能驾驶的交通车辆运动预测方法研究[D]. 长春: 吉林大学汽车工程学院, 2018.
	Li Jian-ping. Study on motion prediction of traffic vehicles for intelligent driving[D]. Changchun: College of Automotive Engineering, Jilin University, 2018.
4	Luber M, Stork J A, Tipaldi G D, et al. People tracking with human motion predictions from social forces[C]∥2010 IEEE International Conference on Robotics and Automation, Anchorage, USA, 2010: 464-469.
5	Alahi A, Goel K, Ramanathan V, et al. Social LSTM: human trajectory prediction in crowded spaces[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 961-971.
6	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.
7	Kim B, Kang C, Kim J, et al. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network[C]∥2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, 2017: 399-404.
8	Pan Jia-cheng, Sun Hong-yi, Xu Ke-cheng, et al. Lane-attention: predicting vehicles' moving trajectories by learning their attention over lanes[C]∥2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA, 2020: 7949-7956.
9	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J/OL]. [2022-09-20].
10	田彦涛, 黄兴, 卢辉遒, 等. 基于注意力与深度交互的周车多模态行为轨迹预测[J/OL]. [2022-09-20].
11	Guo Hong-yan, Meng Qing-yu, Cao Dong-pu, et al. Vehicle trajectory prediction method coupled with ego vehicle motion trend under dual attention mechanism[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-16.
12	Deo N, Trivedi M M. Convolutional social pooling for vehicle trajectory prediction[C]∥Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, USA, 2018: 1468-1476.
13	Li Zi-rui, Gong Jian-wei, Lu Chao, et al. Interactive behavior prediction for heterogeneous traffic participants in the urban road: a graph-neural-network-based multitask learning framework[J]. IEEE/ASME Transactions on Mechatronics, 2021, 26(3): 1339-1349.
14	Agrim G, Justin J, Li F F, et al. Social GAN: socially acceptable trajectories with generative adversarial networks[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 2255-2264.
15	Caesar H, Bankiti V, Lang A H, et al. Nuscenes: a multi-modal dataset for autonomous driving[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 11621-11631.
16	Salzmann T, Ivanovic B, Chakravarty P, et al. Trajectron++: dynamically-feasible trajectory forecasting with heterogeneous data[C]∥European Conference on Computer Vision, Glasgow, UK, 2020: 683-700.
17	Phan-minh T, Grigore E C, Boulton F A, et al. Covernet: multimodal behavior prediction using trajectory sets[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 14074-14083.
18	Wang Chu-hua, Wang Yu-chen, Xu Ming-ze, et al. Stepwise goal-driven networks for trajectory prediction[J]. IEEE Robotics and Automation Letters, 2022, 7(2): 2716-2723.
19	Messaoud K, Deo N, Trivedi M M, et al. Multi-head attention with joint agent-map representation for trajectory prediction in autonomous driving[J/OL]. [2022-09-25].
20	Gilles T, Sabatini S, Tsishkou D, et al. Gohome: graph-oriented heatmap output for future motion estimation[C]∥2022 International Conference on Robotics and Automation (ICRA), Philadelphia, USA, 2022: 9107-9114.
21	Luo C X, Sun L, Dabiri D, et al. Probabilistic multi-modal trajectory prediction with lane attention for autonomous vehicles[C]∥2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA, 2020: 2370-2376.

Related Articles 15

[1]	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.
[2]	Song GAO,Yu-qiong WANG,Yu-hai WANG,Yi XU,Ying-chao ZHOU,Peng-wei WANG. Longitudinal and lateral integrated feedback linearization control for intelligent vehicle [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 735-745.
[3]	Bo XIE,Rong GAO,Fu-qiang XU,Yan-tao TIAN. Stability control of human⁃vehicle shared steering system under low adhesion road conditions [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 713-725.
[4]	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.
[5]	Yan-tao TIAN,Yan-shi JI,Huan CHANG,Bo XIE. Deep reinforcement learning augmented decision⁃making model for intelligent driving vehicles [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 682-692.
[6]	Jian ZHANG,Jin-bo LIU,Yuan GAO,Meng-ke LIU,Zhen-hai GAO,Bin YANG. Localization algorithm of vehicular sensor based on multi⁃mode interaction [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 772-780.
[7]	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.
[8]	Bing ZHU,Tian-xin FAN,Jian ZHAO,Pei-xing ZHANG,Yu-hang SUN. Accelerate test method of automated driving system based on hazardous boundary search [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 704-712.
[9]	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.
[10]	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.
[11]	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.
[12]	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.
[13]	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.
[14]	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.
[15]	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.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

模型	常规工况						外界扰动
模型	K=5			K=10			K=5			K=10
	ADE/m	FDE/m	MR/%	ADE/m	FDE/m	MR/%	ADE/m	FDE/m	MR/%	ADE/m	FDE/m	MR/%
Traj++	1.86	-	0.70	1.51	-	0.57	1.91	-	0.77	1.74	-	0.71
CoverNet	1.97	-	0.76	1.92	-	0.64	2.16	-	0.85	2.03	-	0.76
SG-NET	1.85	3.87	0.67	1.32	2.50	0.52	1.94	4.03	0.78	1.53	2.71	0.66
MHA-JAM	1.76	3.69	0.58	1.24	2.21	0.45	1.83	3.94	0.75	1.46	2.58	0.61
GOHOME	1.59	-	0.46	1.15	-	0.47	1.75	-	0.61	1.44	-	0.55
CXX	1.63	4.15	0.69	1.29	2.47	0.60	2.07	5.88	0.79	2.24	4.08	0.72
本文	1.41	3.71	0.66	1.09	2.33	0.40	1.37	3.79	0.58	1.12	2.38	0.45

Vehicle trajectory prediction combined with high definition map in graph attention mode

RICH HTML

PDF (PC)