A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
IEEE/CAA Journal of Automatica Sinica
| Citation: | R. Wang, Z. Pan, Y. Xia, and J. Li, “LID-driver: Lightweight interactive diffusion-based lane change decision for autonomous driving,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.125882
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K. Liu, N. Li, H. E. Tseng, et al, “Interaction-aware trajectory prediction and planning for autonomous vehicles in forced merge scenarios,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 1, pp. 474–488, Jan. 2023. doi: 10.1109/TITS.2022.3216792
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K. Zhang, L. Zhao, C. Dong, et al, “AI-TP: Attention-based interaction-aware trajectory prediction for autonomous driving,” IEEE Trans. Intell. Veh., vol. 8, no. 1, pp. 73–83, Jan. 2023. doi: 10.1109/TIV.2022.3155236
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H. Caesar, J. Kabzan, K. S. Tan, et al, “NuPlan: A closed-loop ml-based planning benchmark for autonomous vehicles,” arXiv preprint arXiv: 2106.11810, 2022.
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R. Wang, Y. Xia, Y. Wei, et al, “MADDPG-based distributed cooperative search strategy for heterogeneous agents system,” in Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control, J. Yu, Y. Liu, Q. Li, Eds. Singapore, Singapore: Springer, 2023, pp. 292–305.
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J. Liu, J. Yin, Z. Jiang, et al, “Attention-based distributional reinforcement learning for safe and efficient autonomous driving,” IEEE Robot. Autom. Lett., vol. 9, no. 9, pp. 7477–7484, Sep. 2024. doi: 10.1109/LRA.2024.3427551
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Q. Liang, J. Liu, Z. Jiang, et al, “Limited information aggregation for collaborative driving in multi-agent autonomous vehicles,” IEEE Robot. Autom. Lett., vol. 9, no. 7, pp. 6624–6631, Jul. 2024. doi: 10.1109/LRA.2024.3410159
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Z. Li, K. Li, S. Wang, et al, “Hydra-MDP: End-to-end multimodal planning with multi-target hydra-distillation,” arXiv preprint arXiv: 2406.06978, 2024.
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Z. Huang, H. Liu, C. Lv, “GameFormer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Paris, France, 2023, pp. 3880–3890.
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X. Tian, J. Gu, B. Li, et al, “DriveVLM: The convergence of autonomous driving and large vision-language models,” in Proc. 8th Conf. Robot Learning, Munich, Germany, 2025, pp. 4698–4726.
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B. Jiang, S. Chen, Q. Zhang, et al, “AlphaDrive: Unleashing the power of VLMs in autonomous driving via reinforcement learning and reasoning,” arXiv preprint arXiv: 2503.07608, 2025.
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C. Jiang, A. Cornman, C. Park, et al, “Motiondiffuser: Controllable multi-agent motion prediction using diffusion,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 9644–9653.
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Y. Chen, P. Karkus, B. Ivanovic, et al, “Tree-structured policy planning with learned behavior models,” in Proc. IEEE Int. Conf. Robotics and Automation, London, UK, 2023, pp. 7902–7908.
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Z. Huang, P. Karkus, B. Ivanovic, et al, “DTPP: Differentiable joint conditional prediction and cost evaluation for tree policy planning in autonomous driving,” in Proc. IEEE Int. Conf. Robotics and Automation, Yokohama, Japan, 2024, pp. 6806–6812.
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W. Mao, C. Xu, Q. Zhu, et al, “Leapfrog diffusion model for stochastic trajectory prediction,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 5517–5526.
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