ABSTRACT
Accurate prediction of future traffic flow trends is essential to solve urban transportation problems. However, traffic flow prediction faces great challenges due to the multimodal nature of pedestrian behavior and the complexity of the traffic environment. Although a large number of studies have been conducted to investigate these issues in depth, there are still some limitations. In order to address these challenges more effectively, we propose a pedestrian trajectory prediction model based on long-short-term memory networks (LSTMs): the DIR-LSTM. The model introduces an innovative generalized direction mechanism and a self-attention mechanism, which captures pedestrian movement patterns more comprehensively and accurately by predicting overall directional movements first and then gradually subdividing them into individual directions of movement. The DIR-LSTM is designed to address the challenges posed by the diversity of pedestrian behaviors and the complexity of urban environments. To validate the state-of-the-art of the model, we conducted experiments using the publicly available ETH [10] and UCY [9] datasets. The experiments demonstrate that DIR-LSTM performs better in terms of accuracy compared to other models, providing a more reliable prediction tool for future urban traffic management.
- A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei,and S. Savarese. 2016. Social lstm: Human trajectory prediction incrowded spaces. In CVPR, pages 961–971Google Scholar
- Tomoya Ono and Takashi Kanamaru. 2021. Prediction of data 2021, The 21st International Conference on Control, Automation and Systems (ICCAS 2021)Google Scholar
- Jack M Wang, David J Fleet, and Aaron Hertzmann. 2007. Gaussian process dynamical models for human motion. IEEE transactions on pattern analysis and machine intelligence, 30(2):283–298Google Scholar
- Fernando T, Denman S, Sridharan S, 2018. Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection. Neural Networks, 108:466-478Google ScholarCross Ref
- J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. ArXiv preprint, arXiv:1412.3555Google Scholar
- S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735–1780Google Scholar
- A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi. 2018. Social gan: Socially acceptable trajectories with generative adversarial networks. In CVPR.Google Scholar
- H. Su, J. Zhu, Y. Dong and B. Zhang, 2017. Forecast the plausible paths in crowd scenes. in IJCAI, pp. 2772-2778.Google Scholar
- A. Lerner, Y. Chrysanthou, and D. Lischinski. Crowds by example. 2007. In Computer Graphics Forum, volume 26, pages 655–664. Wiley Online LibraryGoogle Scholar
- S. Pellegrini, A. Ess, K. Schindler, and L. Van Gool. 2009. You'll never walk alone: Modeling social behavior for multi-target tracking. In Computer Vision, 2009 IEEE 12th International Conference on, pages 261–268. IEEEGoogle Scholar
- S. Kim, S. J. Guy, W. Liu, D. Wilkie, R. W. Lau, M. C. Lin, 2015. BRVO: Predicting pedestrian trajectories using velocity-space reasoning. The International Journal of Robotics Research, vol. 34, no. 2, pp. 201-217, 2015.Google ScholarDigital Library
- I. Hasan, F. Setti, T. Tsesmelis, A. Del Bue, F. Galasso, and M. Cristani. 2018. Mx-lstm: mixing tracklets and vislets to jointly forecast trajectories and head poses. arXiv preprint arXiv:1805.00652Google Scholar
- Vemula A, Muelling K, Oh J. 2018. Social Attention: Modeling Attention in Human Crowds. IEEE International Conference on Robotics and Automation (ICRA), Brisbane, AustraliaGoogle Scholar
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