[1] [1] SEETHARAMAN G, LAKHOTIA A, BLASCH E P. Unmanned vehicles come of age: the DARPA grand challenge[J]. Computer, 2006, 39(12): 26-29.

[3] [3] LI S S, LI N, GIRARD A, et al. Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control[C]//Proceedings of the 58th IEEE Conference on Decision and Control. Washington D.C., USA: IEEE Press, 2019: 2181-2187.

[4] [4] CHANDRA R, MANOCHA D. GamePlan: game-theoretic multi-agent planning with human drivers at intersections, round abouts, and merging[J]. IEEE Robotics and Automation Letters, 2022, 7(2): 2676-2683.

[5] [5] SHU K Q, YU H L, CHEN X X, et al. Autonomous driving at intersections: a behavior-oriented critical-turning-point approach for decision making[J]. ASME Transactions on Mechatronics, 2021, 27(1): 234-244.

[7] [7] WIEST J, HOFFKEN M, KRESEL U, et al. Probabilistic trajectory prediction with Gaussian mixture models[C]//Proceedings of IEEE Intelligent Vehicles Symposium. Washington D.C., USA: IEEE Press, 2012: 141-146.

[8] [8] GINDELE T, BRECHTEL S, DILLMANN R. Learning driver behavior models from traffic observations for decision making and planning[J]. IEEE Intelligent Transportation Systems Magazine, 2015, 7(1): 69-79.

[9] [9] KIM B, KANG C M, KIM J, et al. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network[C]//Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems. Washington D.C., USA: IEEE Press, 2017: 399-404.

[10] [10] LEE N, CHOI W, VERNAZA P, et al. DESIRE: distant future prediction in dynamic scenes with interacting agents[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2017: 336-345

[12] [12] PAN J C, SUN H Y, XU K C, et al. Lane-attention: predicting vehicles' moving trajectories by learning their attention over lanes[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Washington D.C., USA: IEEE Press, 2020: 7949-7956.

[13] [13] TANG C, SALAKHUTDINOV R R. Multiple futures prediction[C]//Proceedings of Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2019: 32-41.

[15] [15] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.

[16] [16] PARK S H, KIM B, KANG C M, et al. Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture[C]//Proceedings of IEEE Intelligent Vehicles Symposium. Washington D.C., USA: IEEE Press, 2018: 1672-1678.

[17] [17] FERNANDO T, DENMAN S, SRIDHARAN S, et al. Soft + Hardwired attention: an LSTM framework for human trajectory prediction and abnormal event detection[J]. Neural Networks, 2018, 108: 466-478.

[18] [18] ZHANG P, OUYANG W L, ZHANG P F, et al. SR-LSTM: state refinement for LSTM towards pedestrian trajectory prediction[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2019: 12085-12094.

[19] [19] 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, 2021, 14(2): 197-208.

[20] [20] LECUN Y, BENGIO Y. Convolutional networks for images, speech, and time series[J]. The Handbook of Brain Theory and Neural Networks, 1995, 3361(10): 1995.

[21] [21] ZHANG K P, ZHANG Z P, LI Z F, et al. Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Processing Letters, 2016, 23(10): 1499-1503.

[22] [22] SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2015: 1-9.

[23] [23] ALAHI A, GOEL K, RAMANATHAN V, et al. Social LSTM: human trajectory prediction in crowded spaces[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2016: 961-971.

[24] [24] DEO N, TRIVEDI M M. Convolutional social pooling for vehicle trajectory prediction[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2018: 1468-1476.

[25] [25] DAI S Z, LI L, LI Z H. Modeling vehicle interactions via modified LSTM models for trajectory prediction[J]. IEEE Access, 2019, 7: 38287-38296.

[26] [26] ZHAO T Y, XU Y F, MONFORT M, et al. Multi-agent tensor fusion for contextual trajectory prediction[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2019: 12126-12134.