References
[1]. Geiger, P. Lenz and R. Urtasun, "Are we ready for autonomous driving? The KITTI vision benchmark suite," 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012, pp. 3354-3361, doi: 10.1109/CVPR.2012.6248074.
[2]. D. -E. Kim and D. -S. Kwon, "Pedestrian detection and tracking in thermal images using shape features," 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Goyangi, Korea (South), 2015, pp. 22-25, doi: 10.1109/URAI.2015.7358920.
[3]. Y. Zeng, R. Zhang and T. J. Lim, "Wireless communications with unmanned aerial vehicles: opportunities and challenges," in IEEE Communications Magazine, vol. 54, no. 5, pp. 36-42, May 2016, doi: 10.1109/MCOM.2016.7470933.
[4]. H. Shafienya and A. Regan, "4D Flight Trajectory Prediction based on ADS-B data: A comparison of CNN-GRU models," 2022 IEEE Aerospace Conference (AERO), Big Sky, MT, USA, 2022, pp. 01-12, doi: 10.1109/AERO53065.2022.9843822.
[5]. S. Liu, H. Liu, H. Bi and T. Mao, "CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN," in IEEE Access, vol. 8, pp. 101662-101671, 2020, doi: 10.1109/ACCESS.2020.2987072.
[6]. Gaffney, S., & Smyth, P. (1999, August). Trajectory clustering with mixtures of regression models. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 63-72).
[7]. Oh, C., & Kim, T. (2010). Estimation of rear-end crash potential using vehicle trajectory data. Accident Analysis & Prevention, 42(6), 1888-1893.
[8]. Türkcan, S., & Masson, J. B. (2013). Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories. PloS one, 8(12), e82799.
[9]. Chan, S., Reddy, V., Myers, B., Thibodeaux, Q., Brownstone, N., & Liao, W. (2020). Machine learning in dermatology: current applications, opportunities, and limitations. Dermatology and therapy, 10, 365-386.
[10]. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[11]. Goodfellow IJ, Pouget-Abadie J, Mirza M (2014) Generative adversarial networks. Adv Neural Inf Process Syst 3(11):2672–2680
[12]. Sadeghian A, Kosaraju V, Sadeghian A, Hirose N, Rezatofighi H, Savarese S (2019) Sophie: an attentive gan for predicting paths compliant to social and physical constraints. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
[13]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
[14]. Zhang, J., Shi, X., Xie, J., Ma, H., King, I., & Yeung, D. Y. (2018). GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018.
[15]. Li, L., Pagnucco, M., & Song, Y. (2022). Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2231-2241).
[16]. Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80.
[17]. Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
[18]. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903.
[19]. Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018). Social gan: Socially acceptable trajectories with generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2255-2264).
[20]. Yan, S., Xiong, Y., & Lin, D. (2018, April). Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32, No. 1).
[21]. Karlik, B., & Olgac, A. V. (2011). Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1(4), 111-122.
[22]. Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471.
[23]. Castro-Neto, M., Jeong, Y. S., Jeong, M. K., & Han, L. D. (2009). Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert systems with applications, 36(3), 6164-6173.
[24]. Li, Y., Zheng, Y., Zhang, H., & Chen, L. (2015, November). Traffic prediction in a bike-sharing system. In Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems (pp. 1-10).
Cite this article
Pan,H. (2023). TrajTransGCN: Enhancing trajectory prediction by fusing transformer and graph neural networks. Applied and Computational Engineering,29,43-56.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Geiger, P. Lenz and R. Urtasun, "Are we ready for autonomous driving? The KITTI vision benchmark suite," 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012, pp. 3354-3361, doi: 10.1109/CVPR.2012.6248074.
[2]. D. -E. Kim and D. -S. Kwon, "Pedestrian detection and tracking in thermal images using shape features," 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Goyangi, Korea (South), 2015, pp. 22-25, doi: 10.1109/URAI.2015.7358920.
[3]. Y. Zeng, R. Zhang and T. J. Lim, "Wireless communications with unmanned aerial vehicles: opportunities and challenges," in IEEE Communications Magazine, vol. 54, no. 5, pp. 36-42, May 2016, doi: 10.1109/MCOM.2016.7470933.
[4]. H. Shafienya and A. Regan, "4D Flight Trajectory Prediction based on ADS-B data: A comparison of CNN-GRU models," 2022 IEEE Aerospace Conference (AERO), Big Sky, MT, USA, 2022, pp. 01-12, doi: 10.1109/AERO53065.2022.9843822.
[5]. S. Liu, H. Liu, H. Bi and T. Mao, "CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN," in IEEE Access, vol. 8, pp. 101662-101671, 2020, doi: 10.1109/ACCESS.2020.2987072.
[6]. Gaffney, S., & Smyth, P. (1999, August). Trajectory clustering with mixtures of regression models. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 63-72).
[7]. Oh, C., & Kim, T. (2010). Estimation of rear-end crash potential using vehicle trajectory data. Accident Analysis & Prevention, 42(6), 1888-1893.
[8]. Türkcan, S., & Masson, J. B. (2013). Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories. PloS one, 8(12), e82799.
[9]. Chan, S., Reddy, V., Myers, B., Thibodeaux, Q., Brownstone, N., & Liao, W. (2020). Machine learning in dermatology: current applications, opportunities, and limitations. Dermatology and therapy, 10, 365-386.
[10]. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[11]. Goodfellow IJ, Pouget-Abadie J, Mirza M (2014) Generative adversarial networks. Adv Neural Inf Process Syst 3(11):2672–2680
[12]. Sadeghian A, Kosaraju V, Sadeghian A, Hirose N, Rezatofighi H, Savarese S (2019) Sophie: an attentive gan for predicting paths compliant to social and physical constraints. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
[13]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
[14]. Zhang, J., Shi, X., Xie, J., Ma, H., King, I., & Yeung, D. Y. (2018). GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018.
[15]. Li, L., Pagnucco, M., & Song, Y. (2022). Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2231-2241).
[16]. Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80.
[17]. Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
[18]. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903.
[19]. Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018). Social gan: Socially acceptable trajectories with generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2255-2264).
[20]. Yan, S., Xiong, Y., & Lin, D. (2018, April). Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32, No. 1).
[21]. Karlik, B., & Olgac, A. V. (2011). Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1(4), 111-122.
[22]. Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471.
[23]. Castro-Neto, M., Jeong, Y. S., Jeong, M. K., & Han, L. D. (2009). Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert systems with applications, 36(3), 6164-6173.
[24]. Li, Y., Zheng, Y., Zhang, H., & Chen, L. (2015, November). Traffic prediction in a bike-sharing system. In Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems (pp. 1-10).