基于注意力与深度交互的周车多模态行为轨迹预测

doi:10.13229/j.cnki.jdxbgxb.20210904

Abstract

Abstract:

A vehicle deep-interaction coding model combined with a decoder based on the attention-mechanism to solve this problem was presented in this work. The model's multi-modal behavior predictions and trajectory predictions were output. The proposed model is evaluated by the public NGSIM US-101 and I-80 data sets. The results show that the model has a better root mean square error and achieves higher trajectory prediction accuracy while improving computational efficiency. This paper also shows a qualitative analysis of the prediction of multi-modal behavior maneuvering.

Key words: vehicle engineering, trajectory prediction, multimodal prediction, attention mechanism, gated recurrent unit

CLC Number:

U495

Yan-tao TIAN,Xing HUANG,Hui-qiu LU,Kai-ge WANG,Fu-qiang XU. Multi⁃mode behavior trajectory prediction of surrounding vehicle based on attention and depth interaction[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1474-1480.

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References 17

1	Lefèvre S, Vasquez D, Laugier C. A survey on motion prediction and risk assessment for intelligent vehicles [J]. Robomech Journal, 2014, 1(1): 1-14.
2	Ammous S, Nashashibi F. Real time trajectory prediction for collision risk estimation between vehicles [C]∥IEEE 5th Internationgal Conference on Intelligent Computer Communication and Proceding, Cluj-Napoca, Romania, 2009: 417-422.
3	Broadhurst A, Baker S, Kanade T. Monte carlo road safety reasoning[C]∥IEEE Intelligent Vehicles Symposium Proceedings,Las Vegas,USA,2005: 319-324.
4	Schreier M, Willert V, Adamy J. An integrated approach to maneuver-based trajectory prediction and criticality assessment in arbitrary road environments[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10): 2751-2766.
5	Zhang H, Geiger A, Urtasun R. Understanding high-level semantics by modeling traffic patterns[C]∥IEEE International Conference on Computer Vision, Sydney, Australia, 2013: 3056-3063.
6	Lefèvre S, Laugier C, Ibaez-Guzmàn J. Intention-aware risk estimation for general traffic situations, and application to intersection safety[J]. Hal Inria, 2013, 8379: 1-23.
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	Lee D, Kwon Y P, Mcmains S. Convolution neural network-based lane change intention prediction of surrounding vehicles for ACC[C]∥IEEE 20th International Conference on Intelligent Transportation Systems, Yokohama, Japan, 2017: 1-6.
9	Khosroshahi A, Ohn-Bar E, Trivedi M M. Surround vehicles trajectory analysis with recurrent neural networks[C]∥IEEE International Conference on Intelligent Transportation Systems, Rio de Janeiro, Cluj-Napoca, Romania, 2016: 2267-2272.
10	Phillps D J, Wheeler T A, Kochenderfer M J. Generalizable intention prediction of human drivers at intersections [C]∥IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, USA, 2017: 1665-1670.
11	Kim B, Kang C M, Kim J. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network [C]∥IEEE 20th International Conference on Intelligent Transportation Systems, Yokohama, Japan, 2017: 399-404.
12	Lee N, Choi W, Vernaza P. DESIRE: distant future prediction in dynamic scenes with interacting agents[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 2165-2174.
13	Deo N, Trivedi M M. Convolutional social pooling for vehicle trajectory prediction[C]∥IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, USA, 2018: 1549-1557.
14	Deo N, Trivedi M M. Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs[C]∥IEEE Intelligent Vehicles Symposium, Changshu, China, 2018: 1179-1184.
15	Zhang H, Wang Y, Liu J. A multi-modal states based vehicle descriptor and dilated convolutional social pooling for vehicle trajectory prediction[J]. SAE Technical Papers, arXiv: .
16	Alexiadis V, Colyar J, Halkias J. The next generation simulation program[J]. ITE Journal, 2004, 74(8): 22-26.
17	Thiemann C, Treiber M, Kesting A. Estimating acceleration and lane-changing dynamics based on NGSIM trajectory data[J]. Transportation Research Record Journal of the Transportation Research Board, 2008(2088): 90-101.

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模型	预测时间/s
模型	1	2	3	4	5
V-LSTM	0.68	1.65	2.91	4.46	6.27
C-VGMM+VIM	0.66	1.56	2.75	4.24	6.68
CS-LSTM	0.65	1.34	2.40	3.56	4.54
DCS-LSTM	0.58	1.26	2.21	3.46	4.43
ADI-DCS-GRU	0.55	1.24	2.10	3.12	4.23
ADI-DCS-GRU（M）	0.57	1.26	2.15	3.23	4.35

Multi⁃mode behavior trajectory prediction of surrounding vehicle based on attention and depth interaction

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