Articles | Volume 16, issue 1
https://doi.org/10.5194/ms-16-87-2025
https://doi.org/10.5194/ms-16-87-2025
Research article
 | 
06 Feb 2025
Research article |  | 06 Feb 2025

Interactive trajectory prediction for autonomous driving based on Transformer

Rui Xu, Jun Li, Shiyi Zhang, Lei Li, Hulin Li, Guiying Ren, and Xinglong Tang

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Cited articles

Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., and Savarese, S.: Social LSTM: Human Trajectory Prediction in Crowded Spaces, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016, IEEE, 961–971, https://doi.org/10.1109/CVPR.2016.110, 2016. 
Bhat, M., Francis, J., and Oh, J.: Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving, arXiv [preprint], https://doi.org/10.48550/arXiv.2011.14910, 30 November 2020. 
Chang, M. F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., and Ramanan, D.: Argoverse: 3D Tracking and Forecasting With Rich Maps, arXiv [preprint], https://doi.org/10.48550/arXiv.1911.02620, 6 November 2019. 
Cui, Z., Henrickson, K., Ke, R., and Wang, Y.: Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting, IEEE T. Intell. Transp., 21, 4883–4894, https://doi.org/10.1109/TITS.2019.2950416, 2020. 
Dai, S., Li, L., and Li, Z.: Modeling vehicle interactions via modified LSTM models for trajectory prediction, IEEE Access, 7, 38287–38296, https://doi.org/10.1109/ACCESS.2019.2907000, 2019. 
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Short summary
A trajectory prediction model based on Transfomer has been proposed to address the issue of long-term prediction accuracy in complex traffic environments. Optimizing multi-head attention based on knowledge of the scene context and vehicle position generates interactions between maps and agents, as well as between agents themselves. Its effectiveness has been evaluated on the basis of the outdoor dataset, and higher precision was achieved.
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