Cross-Domain Intent-Aware Trajectory Prediction via Flexible Multi-Generator Spatiotemporal Graph Learning
Keywords:
trajectory prediction, spatiotemporal graph, multi-generator model, intent inference, cross-domain learning, socio-technical systems, fairness, governanceAbstract
This paper presents a comprehensive research framework for cross-domain intent-aware trajectory prediction using a flexible multi-generator spatiotemporal graph learning paradigm. The proposed architecture addresses fundamental limitations in existing trajectory forecasting systems, particularly their inability to generalize across diverse domains such as autonomous driving, pedestrian motion, drone navigation, and maritime route planning. By integrating multiple generative modules that each specialize in distinct motion regimes, the model achieves superior adaptability while maintaining computational tractability. Spatiotemporal graph structures encode relational dynamics among interacting agents, and an intent inference layer captures latent behavioral objectives through a probabilistic reasoning process. The paper emphasizes system-level considerations including infrastructure deployment, scalability under real-time constraints, robustness to noisy sensor inputs, and fairness across heterogeneous populations of agents. Governance and policy implications are discussed in the context of safety certification, liability assignment, and ethical deployment in public spaces. The flexible multi-generator architecture is analyzed with respect to its trade-offs between model capacity, inference latency, and data governance requirements. Sustainability aspects, such as energy consumption during training and inference, are also examined. Through illustrative case studies drawn from autonomous mobility and crowd management, the paper demonstrates how cross-domain intent-aware prediction can become a foundational component of future intelligent transportation and surveillance systems. The findings underscore the necessity of designing trajectory prediction systems that are not only accurate but also transparent, fair, and operationally robust across diverse socio-technical environments. This work contributes a holistic perspective that bridges algorithm design with real-world deployment constraints, offering actionable insights for researchers, engineers, and policymakers.
References
1. Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (2016). Social LSTM: Human trajectory prediction in crowded spaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 961-971.
2. Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018). Social GAN: Socially acceptable trajectories with generative adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2255-2264.
3. Liu, Y., Yan, Q., & Alahi, A. (2021). Social NCE: Contrastive learning of socially-aware motion representations. Proceedings of the IEEE/CVF International Conference on Computer Vision, 15118-15128.
4. Rhinehart, N., Kitani, K. M., & Vernaza, P. (2018). R2P2: A reparameterized pushforward policy for diverse, precise generative path forecasting. Proceedings of the European Conference on Computer Vision, 772-788.
5. Makansi, O., Ilg, E., Cicek, O., & Brox, T. (2019). Overcoming limitations of mixture density networks: A sampling and fitting framework for multimodal future prediction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7144-7153.
6. Li, J., Ma, H., & Tomizuka, M. (2020). Conditional generative adversarial network for trajectory prediction in autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 21(8), 3420-3431.
7. Zhang, S., Wang, Y., & Yeung, D. Y. (2019). Encoding social interactions with graph neural networks for human trajectory prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(2), 650-663.
8. Ziebart, B. D., Maas, A., Bagnell, J. A., & Dey, A. K. (2008). Maximum entropy inverse reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, 1433-1438.
9. Ivanovic, B., & Pavone, M. (2019). The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs. Proceedings of the IEEE/CVF International Conference on Computer Vision, 2375-2384.
10. Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538.
11. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. International Conference on Learning Representations.
12. Hu, Y., Chen, L., & Zhu, J. (2021). Cross-domain trajectory prediction with domain adversarial training. IEEE Robotics and Automation Letters, 6(3), 4582-4589.
13. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., ... & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1-35.
14. Salzmann, T., Ivanovic, B., Chakravarty, P., & Pavone, M. (2020). Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data. Proceedings of the European Conference on Computer Vision, 683-700.
15. Zhu, P., Han, F., & Deng, H. (2023, December). Flexible multi-generator model with fused spatiotemporal graph for trajectory prediction. In IET Conference Proceedings CP874 (Vol. 2023, No. 47, pp. 417-422). Stevenage, UK: The Institution of Engineering and Technology.
16. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4-24.
17. Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations.
18. Monti, F., Boscaini, D., Masci, J., Rodolà, E., Svoboda, J., & Bronstein, M. M. (2017). Geometric deep learning on graphs and manifolds using mixture model CNNs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5115-5124.
19. Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in Neural Information Processing Systems, 30.
20. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308-318.
21. Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, 618-626.
22. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4), 1-37.
23. Han, S., Mao, H., & Dally, W. J. (2016). Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. International Conference on Learning Representations.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Computer Science and Engineering Transactions
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
