Interaction-Field Residual Learning for Calibrated Lane-Change Intent under Mixed Traffic
DOI:
https://doi.org/10.66857/jirt.v3i2.61Keywords:
Lane-change intent, mixed traffic, interaction field, residual learning, uncertainty calibrationAbstract
The prediction of lane-change intent in mixed traffic environments, characterized by the coexistence of human-driven vehicles and autonomous vehicles, presents a formidable challenge due to the stochastic nature of human driving behaviors and complex vehicular interactions. Conventional trajectory prediction and intent recognition models often struggle to capture the nuanced, continuous spatial-temporal dynamics of such environments, frequently resulting in overconfident or poorly calibrated predictions. To address these critical shortcomings, this paper proposes a novel framework termed Interaction-Field Residual Learning for Calibrated Lane-Change Intent. The framework introduces a continuous interaction-field representation that models traffic participants as sources of physical potentials, thereby capturing both repulsive and attractive social forces in a unified topographical space. Built upon this representation, a residual learning architecture is deployed to separate the deterministic vehicle dynamics from the highly stochastic, socially driven residual movements. Furthermore, a post-hoc calibration module is integrated to ensure that the probabilistic outputs accurately reflect the true likelihood of lane-change maneuvers, reducing critical false-positive rates in safety-critical scenarios. Extensive evaluations on large-scale naturalistic driving datasets demonstrate that the proposed method significantly outperforms existing baseline models in both predictive accuracy and uncertainty calibration metrics. The findings offer profound implications for the design of safe, socially aware navigation systems in transitional mixed-traffic periods.
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1. Li, B., Zhang, R., Liang, H., Zhang, J., Zhang, J., Chen, X., ... & Wang, J. (2026). Interagent: Physics-based multi-agent command execution via diffusion on interaction graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 15253-15265).
2. Wang, X., Ma, X., Zhu, P., Ng, W. S., Zhang, H., Xia, X., ... & Au, K. W. S. (2024). Design, optimization, and experimental validation of a handheld nonconstant-curvature hybrid-structure robotic instrument for maxillary sinus surgery. IEEE/ASME Transactions on Mechatronics, 29(4), 3074-3082.
3. Wang, J., Malawade, A. V., Zhou, J., Yu, S. Y., & Al Faruque, M. A. (2024, January). Rs2g: Data-driven scene-graph extraction and embedding for robust autonomous perception and scenario understanding. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 7478-7487). IEEE.
4. Zhao, J., Zhang, C., Wang, C., & Yang, P. (2025). Learning from expert factors: Trajectory-level reward shaping for formulaic alpha mining. arXiv preprint arXiv:2507.20263.
5. Prescribed-time tracking control of MIMO nonlinear systems with nonvanishing uncertainties
6. Xing, Z. (2026). A Synthesizable Bandwidth Monitoring Module for DDR4 Memory Controllers: RTL Design and Post-Implementation Analysis. Microelectronics Journal, 107410.
7. Yao, J., Yao, C., Chen, Y., Liu, X., Huang, D., & Chu, W. (2026, April). Explainable AI Enhanced Traffic Forecasting for Proactive Autonomous Vehicle Routing. In 2026 IEEE Conference on Technologies for Sustainability (SusTech) (pp. 1-7). IEEE.
8. Wang, Y., & Ling, C. (2025). Controlling attributes of. xpt files generated by R. In PharmaSUG 2025 conference proceedings. San Diego, CA.
9. Cheng, Xiaoyuan, et al. "How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?." arXiv preprint arXiv:2602.02924 (2026).
10. Yang, Y., Wang, Q., Li, C., Hu, H., Wu, C., Jiang, Y., ... & Xu, B. (2025, May). Fewer may be better: Enhancing offline reinforcement learning with reduced dataset. In International Conference on Learning Representations (Vol. 2025, pp. 7076-7098).
11. Yang, Y., Hu, H., Li, W., Li, S., Yang, J., Zhao, Q., & Zhang, C. (2023, June). Flow to control: Offline reinforcement learning with lossless primitive discovery. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 9, pp. 10843-10851).
12. Yang, Y., Ma, X., Li, C., Zheng, Z., Zhang, Q., Huang, G., ... & Zhao, Q. (2021). Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 34, 10299-10312.
13. Li, Y., Du, P., Ren, H., Xi, T., Gao, K., Kang, Z., ... & Zhang, Y. (2026). Mobile sensing discovery of when where and why vulnerable road users break traffic rules.npj Sustainable Mobility and Transport,3(1), 1.
14. Yu, Jiangbo, Raphaël Frank, Luis Miranda-Moreno, Sasan Jafarnejad, Jônatas Augusto Manzolli, Jiyao Wang, and Ali Eslami. Agentic Vehicles for Human-Centered Mobility: Definition, Prospects, and System Implications. 2026.
15. Wang, H., Li, Z., Zhang, Y., He, Z., Jiang, L., Li, K., ... & Gu, D. (2026). ConsistNav: Closing the Action Consistency Gap in Zero-Shot Object Navigation with Semantic Executive Control. arXiv preprint arXiv:2605.09869.
16. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., et al. (2016). End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316.
17. LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical Report, Iowa State University.
18. Kavraki, L. E., Švestka, P., Latombe, J.-C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580.
19. Mur-Artal, R., & Tardós, J. D. (2017). ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics, 33(5), 1255–1262.
20. Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., et al. (2023). PaLM-E: An embodied multimodal language model. In Proceedings of ICML.
21. Levine, S., Pastor, P., Krizhevsky, A., & Quillen, D. (2018). Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. International Journal of Robotics Research, 37(4–5), 421–436.
22. Ross, S., Gordon, G., & Bagnell, D. (2011). A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of AISTATS.
23. Schulman, J., Levine, S., Abbeel, P., Jordan, M., & Moritz, P. (2015). Trust region policy optimization. In Proceedings of ICML.
24. Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., et al. (2018). QT-Opt: Scalable deep reinforcement learning for vision-based robotic manipulation. arXiv preprint arXiv:1806.10293.
25. Hutchinson, S., Hager, G. D., & Corke, P. I. (1996). A tutorial on visual servo control. IEEE Transactions on Robotics and Automation, 12(5), 651–670.
26. Li, Q., Ye, Q., Zhang, N., Zhang, W., & Hu, F. (2025). Digital-twin-enabled industrial IoT: Vision, framework, and future directions. IEEE Wireless Communications, 32(6), 173-181.
27. Liu, X., Yu, Z., Yao, Y., Yang, K., Zeng, H., & Chen, C. P. (2026). A Comprehensive Survey of Cross-Modal Retrieval: Breaking Through Modal Barriers. ACM Computing Surveys.
28. Lin, C. C., Li, G., Hung, C. C., Kuo, Y. C., & Peng, W. C. (2026). Rethinking suspicious account identification from a perspective of machine learning ranking. International Journal of Machine Learning and Cybernetics, 17(3), 84.
29. Sha, L., Wang, Y., Meng, P., Deng, Y., Chen, T., Zhang, X., ... & Xu, Q. (2025). Pharmacological inhibition of PSPH reduces serine levels and epileptic seizures. Nature Chemical Biology, 21(11), 1742-1753.
30. Yang, Z., Ji, W., Guo, Q., & Wang, Z. (2023, October). Javp: Joint-aware video processing with edge-cloud collaboration for dnn inference. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 9152-9160).
31. Peng, Y., & Li, H. (2023). Benthic foraminiferal morphogroups at the end-Guadalupian extinction in eastern Sichuan Basin, China. Palaeogeography, Palaeoclimatology, Palaeoecology, 618, 111522.
32. Zhang, Y., Huang, Z., Zhao, M., Zhang, C., Lu, Y., Ji, Y., ... & Zeng, A. (2025). Learning unbiased cluster descriptors for interpretable imbalanced concept drift detection. IEEE Transactions on Emerging Topics in Computational Intelligence.
33. Jia, Y., Chen, Q., Mu, W., & Zhang, W. (2023). Managing the value co-creation of peer service providers in the sharing economy: the perspective of customer incivility. Heliyon, 9(6).
34. Zhu, R., Jiang, B., Mei, L., Yang, F., Wang, L., Gao, H., ... & Zhang, D. (2025). Adaptflow: Adaptive workflow optimization via meta-learning. arXiv preprint arXiv:2508.08053.
35. Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. In Proceedings of ICLR.
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