[1]

Messaoud K, Yahiaoui I, Verroust-Blondet A, Nashashibi F. 2021. Attention based vehicle trajectory prediction. IEEE Transactions on Intelligent Vehicles 6(1):175−185

doi: 10.1109/TIV.2020.2991952
[2]

Morsali M, Frisk E, Åslund J. 2021. Spatio-temporal planning in multi-vehicle scenarios for autonomous vehicle using support vector machines. IEEE Transactions on Intelligent Vehicles 6(4):611−621

doi: 10.1109/TIV.2020.3042087
[3]

Feng Y, Yan X. 2022. Support vector machine based lane-changing behavior recognition and lateral trajectory prediction. Computational Intelligence and Neuroscience 2022:3632333

doi: 10.1155/2022/3632333
[4]

Li G, Fang S, Ma J, Cheng J. 2020. Modeling merging acceleration and deceleration behavior based on gradient-boosting decision tree. Journal of Transportation Engineering, Part A: Systems 146(7):05020005

doi: 10.1061/JTEPBS.0000386
[5]

Choi D, Yim J, Baek M, Lee S. 2021. Machine learning-based vehicle trajectory prediction using V2V communications and on-board sensors. Electronics 10(4):420

doi: 10.3390/electronics10040420
[6]

Wang W, Xia F, Nie H, Chen Z, Gong Z, et al. 2021. Vehicle trajectory clustering based on dynamic representation learning of Internet of vehicles. IEEE Transactions on Intelligent Transportation Systems 22(6):3567−3576

doi: 10.1109/TITS.2020.2995856
[7]

Xing Y, Lv C, Cao D. 2020. Personalized vehicle trajectory prediction based on joint time-series modeling for connected vehicles. IEEE Transactions on Vehicular Technology 69(2):1341−1352

doi: 10.1109/TVT.2019.2960110
[8]

Fujii R, Vongkulbhisal J, Hachiuma R, Saito H. 2021. A two-block RNN-based trajectory prediction from incomplete trajectory. IEEE Access 9:56140−56151

doi: 10.1109/ACCESS.2021.3072135
[9]

Lin L, Li W, Bi H, Qin L. 2022. Vehicle trajectory prediction using LSTMs with spatial–temporal attention mechanisms. IEEE Intelligent Transportation Systems Magazine 14(2):197−208

doi: 10.1109/MITS.2021.3049404
[10]

Qu D, Wang S, Liu H, Meng Y. 2022. A car-following model based on trajectory data for connected and automated vehicles to predict trajectory of human-driven vehicles. Sustainability 14(12):7045

doi: 10.3390/su14127045
[11]

Abdeljaber O, Younis A, Alhajyaseen W. 2020. Extraction of vehicle turning trajectories at signalized intersections using convolutional neural networks. Arabian Journal for Science and Engineering 45(10):8011−8025

doi: 10.1007/s13369-020-04546-y
[12]

Sheng Z, Xu Y, Xue S, Li D. 2022. Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving. IEEE Transactions on Intelligent Transportation Systems 23(10):17654−17665

doi: 10.1109/TITS.2022.3155749
[13]

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, et al. 2017. Attention is all you need. NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems 30:5998−6008

doi: 10.5555/3295222.3295349
[14]

Chen X, Zhang H, Zhao F, Cai Y, Wang H, et al. 2022. Vehicle trajectory prediction based on intention-aware non-autoregressive transformer with multi-attention learning for Internet of vehicles. IEEE Transactions on Instrumentation and Measurement 71:2513912

doi: 10.1109/TIM.2022.3192056
[15]

Tang Y, He H, Wang Y. 2024. Hierarchical vector transformer vehicle trajectories prediction with diffusion convolutional neural networks. Neurocomputing 580:127526

doi: 10.1016/j.neucom.2024.127526
[16]

Zheng O, Abdel-Aty M, Yue L, Abdelraouf A, Wang Z, et al. 2024. CitySim: a drone-based vehicle trajectory dataset for safety-oriented research and digital twins. Transportation Research Record: Journal of the Transportation Research Board 2678(4):606−621

doi: 10.1177/03611981231185768