Abstract
Accurate and timely prediction of the future path of agents in the vicinity of an agent is the core of avoiding conflict in automated applications. The traditional method based on RNN model requires high computational cost in the process of prediction, especially for long series prediction. In order to obtain more efficient and accurate prediction trajectory, a channel spatio-temporal convolutional network framework, called CSTCN, is proposed in this paper. The framework models the spatial environment as a block of data input to the CSTCN and captures spatio-temporal interactions using an improved temporal convolutional network. Compared with the traditional model, the spatial and temporal modeling of the proposed model is calculated in each local time window so that it can be executed in parallel to obtain higher computational efficiency. Experimental results on 5 trajectory prediction benchmark datasets demonstrate that the proposed model is superior to other seven state-of-the-art models in both efficiency and accuracy.
This research was funded by the National Natural Science Foundation of China (Grant number 62272006), Natural Science Foundation of Anhui Province (Grant No. 2108085MF214) and the University Collaborative Innovation Project of Anhui Province (grant number GXXT-2022-049).
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References
Huang, Y., Du, J., Yang, Z., Zhou, Z., Zhang, L., Chen, H.: A survey on trajectory-prediction methods for autonomous driving. IEEE Trans. Intell. Veh. 7(3), 652–674 (2022)
Li, J., Ma, H., Tomizuka, M.: Conditional generative neural system for probabilistic trajectory prediction. In: Proceedings of IEEE/RSJ International Conference Intelligent Robots and System, pp. 6150–6156 (2019)
Zhang, X., Yang, X., Zhang, W., et al.: Crowd emotion evaluation based on fuzzy inference of arousal and valence. Neurocomputing 445, 194–205 (2021)
Rudenko, A., Palmieri, L., Herman, M., Kitani, K.M., Gavrila, D.M., Arras, K.O.: Human motion trajectory prediction: a survey. Int. J. Robot. Res. 39(8), 895–935 (2020)
Ghorai, P., Eskandarian, A., Kim, Y.-K., Mehr, G.: State estimation and motion prediction of vehicles and vulnerable road users for cooperative autonomous driving: a survey. IEEE Trans. Intell. Transp. Syst. 23(10), 16983–17002 (2022)
Korbmacher, R., Tordeux, A.: Review of pedestrian trajectory prediction methods: comparing deep learning and knowledge-based approaches. IEEE Trans. Intell. Transp. Syst. 23(12), 24126–24144 (2022)
Lv, K., Yuan, L.: SKGACN: social knowledge-guided graph attention convolutional network for human trajectory prediction. IEEE Trans. Instrum. Meas. 72, 1–11 (2023)
Yang, C., Pei, Z.: Long-short term spatio-temporal aggregation for trajectory prediction. IEEE Trans. Intell. Transp. Syst. 24(4), 4114–4126 (2023)
Kitani, K.M., Ziebart, B.D., Bagnell, J.A., Hebert, M.: Activity forecasting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 201–214. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_15
Xue, H., Huynh, D.Q., Reynolds, M.: SS-LSTM: a hierarchical LSTM model for pedestrian trajectory prediction. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1186–1194 (2018)
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 961–971 (2016)
Nikhil, N., Tran Morris, B.: Convolutional neural network for trajectory prediction. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 186–196 (2018)
Mohamed, A., Qian, K., Elhoseiny, M., Claudel, C.: Social-STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction. InL Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 14412–14420 (2020)
Song, X., et al.: Pedestrian trajectory prediction based on deep convolutional LSTM network. IEEE Trans. Intell. Transp. Syst. 22(6), 3285–3302 (2021)
Bera, A., Randhavane, T., Manocha, D.: Aggressive, tense, or shy? Identifying personality traits from crowd videos. In: Proceedings of the International Conference on Artificial Intelligence (IJCAI), pp. 112–118 (2017)
Gupta, A., Johnson, J. Fei-Fei, L., Savarese, S., Alahi, A.: Social GAN: socially acceptable trajectories with generative adversarial networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2255–2264 (2018)
Xu, Y., Piao, Z., Gao, S.: Encoding crowd interaction with deep neural network for pedestrian trajectory prediction. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5275–5284 (2018)
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling (2018). arXiv:1803.01271. [Online]. urlhttp://arxiv.org/abs/1803.01271
Wang, C., Cai, S., Tan, G.: GraphTCN: spatio-temporal interaction modeling for human trajectory prediction. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3450–3459 (2021)
Ziebart, B.D., et al.: Planning-based prediction for pedestrians. In: Proceedings of the IEEE International Conference on Intelligent Robots and System (IROS), pp. 3931–3936 (2009)
Elfring, J., Van De Molengraft, R., Steinbuch, M.: Learning intentions for improved human motion prediction. Robot. Auton. Syst. 62(4), 591–602 (2014)
Møgelmose, A., Trivedi, M.M., Moeslund, T.B.: Trajectory analysis and prediction for improved pedestrian safety: integrated framework and evaluations. In: 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Korea (South), pp. 330–335 (2015)
Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)
Farina, F., Fontanelli, D., Garulli, A., Giannitrapani, A., Prattichizzo, D.: Walking ahead: the headed social force model. PLoS ONE, 12(1), e0169734 (2017)
Ikeda, T., Chigodo, Y., Rea, D., Zanlungo, F., Shiomi, M., Kanda, T.: Modeling and prediction of pedestrian behavior based on the sub-goal concept. Science and Systems. In: Proceedings of Robotics (2012)
Shi, L., et al.: SGCN: sparse graph convolution network for pedestrian trajectory prediction. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 8994–9003 (2021)
Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of 31st International Conference on Machine Learning (ICML), pp. 2048–2057 (2015)
Wang, F., et al.: Residual attention network for image classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 6450–6458 (2017)
Hu, J., Li, S., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 7794–7803 (2018)
Shu, X., Yang, J., Yan, R., Song, Y.: Expansion-squeeze-excitation fusion network for elderly activity recognition. IEEE Trans. Circuits Syst. Video Technol. 32(8), 5281–5292 (2022)
Pellegrini, S., Ess, A., Schindler, K., van Gool, L.: You’ll never walk alone: Modeling social behavior for multi-target tracking. In: Proceedings of IEEE 12th International Conference on Computer Vision, pp. 261–268 (2009)
Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. In: Computer Graphics Forum, vol. 26, no. 3, pp. 655–664. Blackwell Publishing Ltd, Oxford, U.K (2007)
Zhao, T., et al.: et al.: Multi-agent tensor fusion for contextual trajectory prediction. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 12126–12134 (2019)
Acknowledgements
During the process of writing this paper, I would like to express my special gratitude to Ying Hu for her guidance and supervision, as well as for her understanding and tolerance. Thank you to Professor Yonglong Luo for providing guidance during the model design phase, and to the School of Computer Science and Technology at Anhui Normal University for providing me with a good learning environment.
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Lu, Z., Xu, L., Hu, Y., Sun, L., Luo, Y. (2024). Channel Spatio-Temporal Convolutional Network for Trajectory Prediction. In: Wang, G., Wang, H., Min, G., Georgalas, N., Meng, W. (eds) Ubiquitous Security. UbiSec 2023. Communications in Computer and Information Science, vol 2034. Springer, Singapore. https://doi.org/10.1007/978-981-97-1274-8_14
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