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Vehicle motion planning is an essential component of autonomous driving technology.
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2020
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2020
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2021
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2021
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2021
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2022
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2022
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2022
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Q. Zhang, Y. Gao, Y. Zhang, Y. Guo, D. Ding, Y. Wang, P. Sun, and D. Zhao, “TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 12, pp. 24 474–24 487, 2022
2022
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K. Chitta, A. Prakash, B. Jaeger, Z. Yu, K. Renz, and A. Geiger, “TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 11, pp. 12878–12895, 2022
2022
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O. Scheel, L. Bergamini, M. Wolczyk, B. Osiński, and P. Ondruska, “Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients,” CoRL , 2022
2022
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2023
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2023
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D. Li, Q. Zhang, Z. Xia, Y. Zheng, K. Zhang, M. Yi, W. Jin, and D. Zhao, “Planning-Inspired Hierarchical Trajectory Prediction via Lateral-Longitudinal Decomposition for Autonomous Driving,” IEEE Transactions on Intelligent Vehicles , vol. 9, no. 1, pp. 692–703, 2023
2023
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2023
Cited alongside, same era.
W. Huang, C. Wang, R. Zhang, Y. Li, J. Wu, and L. Fei-Fei, “VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models,” CoRL , 2023
2023
Cited alongside, same era.
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as Policies: Language Model Programs for Embodied Control,” ICRA , 2023
2023
Cited alongside, same era.
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2023
Cited alongside, same era.
J. Cheng, Y. Chen, X. Mei, B. Yang, B. Li, and M. Liu, “Rethinking Imitation-based Planner for Autonomous Driving,” ICRA , 2024
2024
Closest in time.
Z. Huang, H. Liu, J. Wu, and C. Lv, “Differentiable Integrated Motion Prediction and Planning With Learnable Cost Function for Autonomous Driving,” ICRA , 2024
2024
Closest in time.
Y. Liu, Q. Zhang, Y. Gao, and D. Zhao, “Deep Reinforcement Learning-Based Driving Policy at Intersections Utilizing Lane Graph Networks,” IEEE Transactions on Cognitive and Developmental Systems , pp. 1–16, doi: 10.1109/TCDS.2024.3384269, 2024
2024
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J. Wang, Q. Zhang, Y. Mu, D. Li, D. Zhao, Y. Zhuang, P. Luo, B. Wang, and J. Hao, “Prototypical Context-Aware Dynamics for Generalization in Visual Control With Model-Based Reinforcement Learning,” IEEE Transactions on Industrial Informatics , pp. 1–11, doi: 10.1109/TII.2024.3396525, 2024
2024
Closest in time.
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
S. Wang, Y. Zhu, Z. Li, Y. Wang, L. Li, and Z. He, “ChatGPT as Your Vehicle Co-Pilot: An Initial Attempt,” IEEE Transactions on Intelligent Vehicles , vol. 8, no. 12, pp. 4706–4721, 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, et al. , “Planning-oriented Autonomous Driving,” CVPR , 2023
2023
Cited alongside, same era.
B. Jiang, S. Chen, Q. Xu, B. Liao, J. Chen, H. Zhou, Q. Zhang, W. Liu, C. Huang, and X. Wang, “VAD: Vectorized Scene Representation for Efficient Autonomous Driving,” ICCV , 2023
2023
Cited alongside, same era.
X. Li, M. Zhang, Y. Geng, H. Geng, Y. Long, Y. Shen, R. Zhang, J. Liu, and H. Dong, “ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation,” CVPR , 2024
2024
Closest in time.
2024
Closest in time.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual Instruction Tuning,” NeurIPS , 2024
2024
Closest in time.
R. Zhang, J. Han, C. Liu, P. Gao, A. Zhou, X. Hu, S. Yan, P. Lu, H. Li, and Y. Qiao, “LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention,” ICLR , 2024
2024
Closest in time.
X. Li, E. Liu, T. Shen, J. Huang, and F.-Y. Wang, “ChatGPT-Based Scenario Engineer: A New Framework on Scenario Generation for Trajectory Prediction,” IEEE Transactions on Intelligent Vehicles , vol. 9, no. 3, pp. 4422–4431, 2024
2024
Closest in time.
Y. Wei, Z. Wang, Y. Lu, C. Xu, C. Liu, H. Zhao, S. Chen, and Y. Wang, “Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents,” CVPR , 2024
2024
Closest in time.
2024
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L. Wen, D. Fu, X. Li, X. Cai, T. Ma, P. Cai, M. Dou, B. Shi, L. He, and Y. Qiao, “DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models,” ICLR , 2024
2024
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2024
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2024
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B. Li, Y. Wang, J. Mao, B. Ivanovic, S. Veer, K. Leung, and M. Pavone, “Driving Everywhere with Large Language Model Policy Adaptation,” CVPR , 2024
2024
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M. Z. Hasan, J. Chen, J. Wang, M. S. Rahman, A. Joshi, S. Velipasalar, C. Hegde, A. Sharma, and S. Sarkar, “Vision-Language Models Can Identify Distracted Driver Behavior From Naturalistic Videos,” IEEE Transactions on Intelligent Transportation Systems , doi: 10.1109/TITS.2024.3381175, 2024
2024
Closest in time.
C. Pan, B. Yaman, T. Nesti, A. Mallik, A. Allievi, S. Velipasalar, and L. Ren, “VLP: Vision Language Planning for Autonomous Driving,” CVPR , 2024
2024
Closest in time.
H. Shao, Y. Hu, L. Wang, S. L. Waslander, Y. Liu, and H. Li, “LMDrive: Closed-Loop End-to-End Driving with Large Language Models,” CVPR , 2024
2024
Closest in time.
Y. Cui, S. Huang, J. Zhong, Z. Liu, Y. Wang, C. Sun, B. Li, X. Wang, and A. Khajepour, “DriveLLM: Charting the Path Toward Full Autonomous Driving With Large Language Models,” IEEE Transactions on Intelligent Vehicles , vol. 9, no. 1, pp. 1450–1464, 2024
2024
Closest in time.