A Review of Vehicle Trajectory Prediction Based on Deep Learning

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A Review of Vehicle Trajectory Prediction Based on Deep Learning

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[1]LIU Kai,WANG Jiaqin,LI Hantao.A Review of Vehicle Trajectory Prediction Based on Deep Learning[J].Journal of Zhengzhou University (Engineering Science),2025,46(05):77-89.[doi:10.13705/j.issn.1671-6833.2025.02.006]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 05 Page number: 77-89 Column: Public date: 2025-08-10

Title:: A Review of Vehicle Trajectory Prediction Based on Deep Learning

Author(s):: LIU Kai; WANG Jiaqin; LI Hantao; School of Electronics and Information Engineering, Beihang University, Beijing 100191, China

Keywords:: vehicle trajectory prediction; deep learning; sequential network; graph neural network; generative model; grid method

CLC:: TP181TP391

DOI:: 10.13705/j.issn.1671-6833.2025.02.006

Abstract:: Vehicle trajectory prediction (VTP) was a significant research subject in the transportation technology field. Traditional VTP methods require extensive feature engineering and struggle to adapt to complex and dynamic environments in real-time. Deep learning (DL) overcomes the limitations of traditional methods by achieving efficient data representation through multi-layer neural networks. Therefore, in this study a comprehensive review of DL-based VTP methods was carried out to explore their applications and performance in VTP. Firstly, the traditional VTP and DL-based VTP methods were explored, and the main consideration problems and problem formulations in VTP were introduced. Secondly, various VTP schemes, including input data, output results and prediction methods were analyzed and compared. Subsequently, commonly used evaluation metrics was introduced, and the experimental results of these VTP approaches were compared, the applications of VTP were analyzed, and the superior performance of DL in VTP were demonstrated. Finally, future research directions of VTP are discussed in terms of datasets, modeling approaches, and computational efficiency. It identifies that vehicle interaction collaborative modeling, model generalization, and multimodal fusion would constitute the primary challenges and research frontiers in the field.

References:: [1]JU C, WANG Z, LONG C, et al. Interaction-aware Kalman neural networks for trajectory prediction[C]∥2020 IEEE Intelligent Vehicles Symposium. Piscataway:IEEE, 2020: 1793-1800.
[2]LIU P F, FAN W. Extreme gradient boosting (XGBoost) model for vehicle trajectory prediction in connected and autonomous vehicle environment[J]. Promet-Traffic & Transportation, 2021, 33(5): 767-774.
[3]WANG J, WANG P, ZHANG C, et al. F-Net: fusion neural network for vehicle trajectory prediction in autonomous driving[C]∥2021 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2021: 4095-4099.
[4]KATARIYA V, BAHARANI M, MORRIS N, et al. DeepTrack: lightweight deep learning for vehicle trajectory prediction in highways[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(10): 1892718936.
[5]GULZAR M, MUHAMMAD Y, MUHAMMAD N. A survey on motion prediction of pedestrians and vehicles for autonomous driving[J]. IEEE Access, 2021, 9: 137957-137969.
[6]HU X H, ZHENG M. Research progress and prospects of vehicle driving behavior prediction[J]. World Electric Vehicle Journal, 2021, 12(2): 88.
[7]SHENG Z H, XU Y W, XUE S B, et al. Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (10): 17654-17665.
[8]LIU X L, WANG Y F, JIANG K, et al. Interactive trajectory prediction using a driving risk map-integrated deep learning method for surrounding vehicles on highways [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(10): 19076-19087.
[9]张三川, 马啸. 基于轨迹加权预测的主动避撞安全距离模型及算法[J]. 郑州大学学报(工学版), 2022, 43(3): 104-110.
ZHANG S C, MA X. A safe distance model and algorithm for active collision avoidance based on weighted prediction of trajectory[J]. Journal of Zhengzhou University (Engineering Science), 2022, 43(3): 104-110.
[10] ZHAO Z Y, FANG H W, JIN Z, et al. GISNet: graphbased information sharing network for vehicle trajectory prediction[C]∥2020 International Joint Conference on Neural Networks. Piscataway:IEEE, 2020: 1-7.
[11]WOO H, JI Y, KONO H, et al. Lane-change detection based on vehicle-trajectory prediction[J]. IEEE Robotics and Automation Letters, 2017, 2(2): 1109-1116.
[12] DAI S Z, LI Z H, LI L, et al. A flexible and explainable vehicle motion prediction and inference framework combining semi-supervised AOG and ST-LSTM[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(2): 840-860.
[13] CHEN J N, WANG Y, WU R H, et al. Spatial-temporal graph neural network for interaction-aware vehicle trajectory prediction[C]∥2021 IEEE 17th International Conference on Automation Science and Engineering. Piscataway: IEEE, 2021: 2119-2125.
[14] FENG X D, CEN Z P, HU J M, et al. Vehicle trajectory prediction using intention-based conditional variational autoencoder[C]∥2019 IEEE Intelligent Transportation Systems Conference. Piscataway: IEEE, 2019: 3514-3519.
[15] CHOI D, YIM J, BAEK M, et al. Machine learningbased vehicle trajectory prediction using V2V communications and on-board sensors[J]. Electronics, 2021, 10 (4): 420.
[16] CHEN L, ZHOU Q Y, CAI Y F, et al. CAE-GAN: a hybrid model for vehicle trajectory prediction[J]. IET Intelligent Transport Systems, 2022, 16(12): 1682-1696.
[17] JO E, SUNWOO M, LEE M. Vehicle trajectory prediction using hierarchical graph neural network for considering interaction among multimodal maneuvers[J]. Sensors, 2021, 21(16): 5354.
[18] BERNTORP K, HOANG T, DI CAIRANO S. Motion planning of autonomous road vehicles by particle filtering[J]. IEEE Transactions on Intelligent Vehicles, 2019, 4(2): 197-210.
[19] MESSAOUD K, DEO N, TRIVEDI M M, et al. Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation[C]∥2021 IEEE Intelligent Vehicles Symposium. Piscataway: IEEE, 2021: 165-170.
[20] CUI H G, RADOSAVLJEVIC V, CHOU F C, et al. Multimodal trajectory predictions for autonomous driving using deep convolutional networks[C]∥2019 International Conference on Robotics and Automation. Piscataway: IEEE, 2019: 2090-2096.
[21] ZHOU W, YANG L, YING T X, et al. Velocity prediction of intelligent and connected vehicles for a traffic light distance on the urban road[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(11): 4119-4133.
[22] ZHANG K P, FENG X L, WU L, et al. Trajectory prediction for autonomous driving using spatial-temporal graph attention transformer[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 2234322353.
[23] LIN L, LI W Z, BI H K, et al. Vehicle trajectory prediction using LSTMs with spatial-temporal attention mechanisms[J]. IEEE Intelligent Transportation Systems Magazine, 2022, 14(2): 197-208.
[24] YU J, ZHOU M, WANG X, et al. A dynamic and static context-aware attention network for trajectory prediction [J]. ISPRS International Journal of Geo-Information, 2021, 10(5): 336.
[25] ALTCHÉ F, DE LA FORTELLE A. An LSTM network for highway trajectory prediction[C]∥2017 IEEE 20th International Conference on Intelligent Transportation Systems. Piscataway:IEEE, 2017: 353-359.
[26] CHEN X B, ZHANG H J, ZHAO F, et al. Intentionaware vehicle trajectory prediction based on spatial-temporal dynamic attention network for Internet of vehicles [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(10): 19471-19483.
[27]WU J B, CHEN X H, BIE Y M, et al. A co-evolutionary lane-changing trajectory planning method for automated vehicles based on the instantaneous risk identification[J]. Accident Analysis & Prevention, 2023, 180: 106907.
[28]WANG J Q, WANG S C. Geographical information enhanced recognition of traffic modes and behavior patterns [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(4): 3777-3782.
[29] KIM B, KANG C M, 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. Piscataway: IEEE, 2017: 399-404.
[30] XING Y, LV C, CAO D P. Personalized vehicle trajectory prediction based on joint time-series modeling for connected vehicles[J]. IEEE Transactions on Vehicular Technology, 2020, 69(2): 1341-1352.
[31] ZHOU H, REN D C, XIA H X, et al. AST-GNN: an attention-based spatio-temporal graph neural network for interaction-aware pedestrian trajectory prediction[J]. Neurocomputing, 2021, 445: 298-308.
[32] MO X Y, HUANG Z Y, XING Y, et al. Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 9554-9567.
[33] GUI Z M, WANG X, LI W Z. Dynamic perception-based vehicle trajectory prediction using a memory-enhanced spatio-temporal graph network[J]. ISPRS International Journal of Geo-Information, 2024, 13(6): 172.
[34] LI J C, MA H B, ZHANG Z H, et al. Spatio-temporal graph dual-attention network for multi-agent prediction and tracking[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 10556-10569.
[35] SINGH D, SRIVASTAVA R. Graph neural network with RNNs based trajectory prediction of dynamic agents for autonomous vehicle[J]. Applied Intelligence, 2022, 52(11): 12801-12816.
[36] MO X Y, XING Y, LV C. Graph and recurrent neural network-based vehicle trajectory prediction for highway driving[C]∥2021 IEEE International Intelligent Transportation Systems Conference. Piscataway:IEEE, 2021: 1934-1939.
[37] LI X, ROSMAN G, GILITSCHENSKI I, et al. Vehicle trajectory prediction using generative adversarial network with temporal logic syntax tree features[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 3459-3466.
[38] HEGDE C, DASH S, AGARWAL P. Vehicle trajectory prediction using GAN[C]∥2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud). Piscataway: IEEE, 2020: 502-507.
[39] ROY D, ISHIZAKA T, MOHAN C K, et al. Vehicle trajectory prediction at intersections using interaction based generative adversarial networks[C]∥2019 IEEE Intelligent Transportation Systems Conference. Piscataway: IEEE, 2019: 2318-2323.
[40] GUO H Y, MENG Q Y, ZHAO X M, et al. Mapenhanced generative adversarial trajectory prediction method for automated vehicles[J]. Information Sciences, 2023, 622: 1033-1049.
[41] NEUMEIER M, BOTSCH M, TOLLKÜHN A, et al. Variational autoencoder-based vehicle trajectory prediction with an interpretable latent space[C]∥2021 IEEE International Intelligent Transportation Systems Conference. Piscataway:IEEE, 2021: 820-827.
[42] LI C N, FENG G W, LI Y N, et al. DiffTAD: denoising diffusion probabilistic models for vehicle trajectory anomaly detection [J]. Knowledge-Based Systems, 2024, 286: 111387.
[43] LI Z Y, LIANG H W, WANG H Q, et al. A multi-modal vehicle trajectory prediction framework via conditional diffusion model: a coarse-to-fine approach[J]. Knowledge-Based Systems, 2023, 280: 110990.
[44] TANG Y J, HE H W, WANG Y. Hierarchical vector transformer vehicle trajectories prediction with diffusion convolutional neural networks [J]. Neurocomputing, 2024, 580: 127526.
[45]WESTNY T, OLOFSSON B, FRISK E. Diffusion-based environment-aware trajectory prediction [EB/OL]. (2024-03-18)[2024-09-03]. https:∥doi. org/10. 48550/arXiv.2403.11643.
[46] HAJRASOULIHA A, GHAHFAROKHI B S. Dynamic geo-based resource selection in LTE-V2V communications using vehicle trajectory prediction[J]. Computer Communications, 2021, 177: 239-254.
[47] OMAMA M, INANI P, PAUL P, et al. ALT-Pilot: autonomous navigation with language augmented topometric maps [EB/OL]. (2023-10-03)[2024-09-03].https:∥ doi.org/10.48550/arXiv.2310.02324.
[48] PENG M X, GUO X S, CHEN X D, et al. LC-LLM: explainable lane-change intention and trajectory predictions with large language models [EB/OL]. (2024-03-27) [2024-09-03]. http:∥arxiv.org/abs/2403.18344.
[49] LAN Z X, LI H B, LIU L S, et al. Traj-LLM: a new exploration for empowering trajectory prediction with pretrained large language models [EB/OL]. (2024-0508)[2024-09-03]. https:∥doi. org/10.48550/arXiv. 2405.04909.
[50] LI X, LIU E L, SHEN T Y, et al. ChatGPT-based scenario engineer: a new framework on scenario generation for trajectory prediction[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(3): 4422-4431.
[51]崔建明, 蔺繁荣, 张迪, 等. 基于有向图的强化学习自动驾驶轨迹预测[J]. 郑州大学学报(工学版), 2023, 44(5): 53-61.
CUI J M, LIN F R, ZHANG D, et al. Reinforcement learning autonomous driving trajectory prediction based on directed graph[J]. Journal of Zhengzhou University (Engineering Science), 2023, 44(5): 53-61.

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Last Update: 2025-09-19