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We address the decision-making capability within an end-to-end planning framework that focuses on motion prediction, decision-making, and trajectory planning.
Springer Science & Business Media, 2006
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2017
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B. Amos, I. Jimenez, J. Sacks, B. Boots, and J. Z. Kolter, “Differentiable MPC for end-to-end planning and control,” Advances in neural information processing systems
2018
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2019
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Q. Wang, L. Chen, B. Tian, W. Tian, L. Li, and D. Cao, “End-to-End Autonomous Driving: An Angle Branched Network Approach,” IEEE Transactions on Vehicular Technology
2019
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W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun, “End-to-End Interpretable Neural Motion Planner,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2019
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S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, “A Survey of Deep Learning Techniques for Autonomous Driving,” Journal of Field Robotics
2020
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Y. Pan, C.-A. Cheng, K. Saigol, K. Lee, X. Yan, E. A. Theodorou, and B. Boots, “Imitation Learning for Agile Autonomous Driving,” The International Journal of Robotics Research
2020
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Y. Xiao, F. Codevilla, A. Gurram, O. Urfalioglu, and A. M. López, “Multimodal End-to-End Autonomous Driving,” IEEE Transactions on Intelligent Transportation Systems
2020
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H. Cui, T. Nguyen, F.-C. Chou, T.-H. Lin, J. Schneider, D. Bradley, and N. Djuric, “Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions,” in 2020 IEEE International Conference on Robotics and Automation (ICRA)
2020
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Z. Zhu and H. Zhao, “A Survey of Deep RL and IL for Autonomous Driving Policy Learning,” IEEE Transactions on Intelligent Transportation Systems
2021
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F. Eiras, M. Hawasly, S. V. Albrecht, and S. Ramamoorthy, “A Two-Stage Optimization-Based Motion Planner for Safe Urban Driving,” IEEE Transactions on Robotics
2021
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J. Zhou, R. Wang, X. Liu, Y. Jiang, S. Jiang, J. Tao, J. Miao, and S. Song, “Exploring Imitation Learning for Autonomous Driving with Feedback Synthesizer and Differentiable Rasterization,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2021
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P. Donti, D. Rolnick, and J. Z. Kolter, “DC3: A learning method for optimization with hard constraints,” in International Conference on Learning Representations
2021
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2022
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2023
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P. Karkus, B. Ivanovic, S. Mannor, and M. Pavone, “Diffstack: A differentiable and modular control stack for autonomous vehicles,” in Conference on robot learning
2023
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Z. Huang, S. Shen, and J. Ma, “Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM,” IEEE Transactions on Intelligent Transportation Systems
2023
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P. Wu, F. Gao, X. Tang, and K. Li, “An Integrated Decision and Motion Planning Framework for Automated Driving on Highway,” IEEE Transactions on Vehicular Technology
2023
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2022
Cited alongside, same era.
J. Ma, Z. Cheng, X. Zhang, Z. Lin, F. L. Lewis, and T. H. Lee, “Local Learning Enabled Iterative Linear Quadratic Regulator for Constrained Trajectory Planning,” IEEE Transactions on Neural Networks and Learning Systems
2022
Cited alongside, same era.
J. Ma, Z. Cheng, X. Zhang, M. Tomizuka, and T. H. Lee, “Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving,” IEEE Transactions on Intelligent Transportation Systems
2022
Cited alongside, same era.
L. Anzalone, P. Barra, S. Barra, A. Castiglione, and M. Nappi, “An End-to-End Curriculum Learning Approach for Autonomous Driving Scenarios,” IEEE Transactions on Intelligent Transportation Systems
2022
Cited alongside, same era.
L. Le Mero, D. Yi, M. Dianati, and A. Mouzakitis, “A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles,” IEEE Transactions on Intelligent Transportation Systems
2022
Cited alongside, same era.
2022
Cited alongside, same era.
L. Pineda, T. Fan, M. Monge, S. Venkataraman, P. Sodhi, R. T. Chen, J. Ortiz, D. DeTone, A. Wang, S. Anderson, et al
2022
Cited alongside, same era.
P. S. Chib and P. Singh, “Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey,” IEEE Transactions on Intelligent Vehicles
2023
Cited alongside, same era.
2024
Closest in time.
L. Chen, P. Wu, K. Chitta, B. Jaeger, A. Geiger, and H. Li, “End-to-End Autonomous Driving: Challenges and Frontiers,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2024
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X. Zhou, Z. Peng, Y. Xie, M. Liu, and J. Ma, “Game-Theoretic Driver Modeling and Decision-Making for Autonomous Driving with Temporal-Spatial Attention-Based Deep Q-Learning,” IEEE Transactions on Intelligent Vehicles
2024
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2024
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H. Liu, Z. Huang, Z. Zhu, Y. Li, S. Shen, and J. Ma, “Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication,” IEEE Transactions on Intelligent Vehicles
2024
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T.-H. Wang, A. Maalouf, W. Xiao, Y. Ban, A. Amini, G. Rosman, S. Karaman, and D. Rus, “Drive Anywhere: Generalizable End-to-End Autonomous Driving with Multi-Modal Foundation Models,” in 2024 IEEE International Conference on Robotics and Automation (ICRA)
2024
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Y. Chen, J. Cheng, L. Gan, S. Wang, H. Liu, X. Mei, and M. Liu, “IR-STP: Enhancing Autonomous Driving with Interaction Reasoning in Spatio-Temporal Planning,” IEEE Transactions on Intelligent Transportation Systems
2024
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A. Tagliabue and J. P. How, “Efficient Deep Learning of Robust Policies from MPC Using Imitation and Tube-Guided Data Augmentation,” IEEE Transactions on Robotics
2024
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K. Yuan, Y. Huang, S. Yang, M. Wu, D. Cao, Q. Chen, and H. Chen, “Evolutionary Decision-Making and Planning for Autonomous Driving: A Hybrid Augmented Intelligence Framework,” IEEE Transactions on Intelligent Transportation Systems
2024
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B. Lang, X. Li, and M. C. Chuah, “BEV-TP: End-to-End Visual Perception and Trajectory Prediction for Autonomous Driving,” IEEE Transactions on Intelligent Transportation Systems
2024
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