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Traditional autonomous driving methods adopt a modular design, decomposing tasks into sub-tasks.
Congested Traffic States in Empirical Observations and Microscopic Simulations
M. Treiber, A. Hennecke, and D. Helbing · 2000
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A Framework for Behavioural Cloning , pages 103–129
M. Bain and C. Sammut · 2000
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Model based vehicle tracking for autonomous driving in urban environments
A. Petrovskaya and S. Thrun · 2008
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Autonomous driving in urban environments: Approaches, lessons and challenges
M. Campbell, M. Egerstedt, J. P. How, and R. M. Murray · 2010
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, June 2021
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby · 2010
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Driver Behavior Modeling: Developments and Future Directions
N. AbuAli and H. Abou-zeid · 2016
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End to End Learning for Self-Driving Cars, Apr. 2016
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba · 2016
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End-to-end learning of driving models from large-scale video datasets
H. Xu, Y. Gao, F. Yu, and T. Darrell · 2017
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Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks, Nov. 2018
Y. Hou, Z. Ma, C. Liu, and C. C. Loy · 2018
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End-to-end Driving via Conditional Imitation Learning, Mar. 2018
F. Codevilla, M. Müller, A. López, V. Koltun, and A. Dosovitskiy · 2018
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End-to-end driving via conditional imitation learning
F. Codevilla, M. Müller, A. López, V. Koltun, and A. Dosovitskiy · 2018
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Urban Driving with Conditional Imitation Learning, Dec. 2019
J. Hawke, R. Shen, C. Gurau, S. Sharma, D. Reda, N. Nikolov, P. Mazur, S. Micklethwaite, N. Griffiths, A. Shah, and A. Kendall · 2019
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An Agent-Based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles
V. De Silva, X. Wang, D. Aladagli, A. Kondoz, and E. Ekmekcioglu · 2019
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Scalability in Perception for Autonomous Driving: Waymo Open Dataset
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, V. Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, A. Joshi, Y. Zhang, J. Shlens, Z. Chen, and D. Anguelov · 2020
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Learning to Drive by Imitation: An Overview of Deep Behavior Cloning Methods
A. O. Ly and M. Akhloufi · 2020
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A Survey of Deep RL and IL for Autonomous Driving Policy Learning
Z. Zhu and H. Zhao · 2021
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Openbot: Turning smartphones into robots
M. Müller and V. Koltun · 2021
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BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation, June 2022
Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. Rus, and S. Han · 2022
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BEVFormer: Learning Bird’s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers, July 2022
Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Q. Yu, and J. Dai · 2022
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Towards Capturing the Temporal Dynamics for Trajectory Prediction: A Coarse-to-Fine Approach
X. Jia, L. Chen, P. Wu, J. Zeng, J. Yan, H. Li, and Y. Qiao · 2022
Cited alongside, same era.
Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer, Dec. 2022
H. Shao, L. Wang, R. Chen, H. Li, and Y. Liu · 2022
Cited alongside, same era.
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline, Oct. 2022
P. Wu, X. Jia, L. Chen, J. Yan, H. Li, and Y. Qiao · 2022
Cited alongside, same era.
Behavior Transformers: Cloning k k modes with one stone, Oct. 2022
N. M. M. Shafiullah, Z. J. Cui, A. Altanzaya, and L. Pinto · 2022
Cited alongside, same era.
Motion Transformer with Global Intention Localization and Local Movement Refinement, Mar. 2023
S. Shi, L. Jiang, D. Dai, and B. Schiele · 2023
Cited alongside, same era.
OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving, Nov. 2023
W. Zheng, W. Chen, Y. Huang, B. Zhang, Y. Duan, and J. Lu · 2023
Later among the works it cites.
Learning to Drive Anywhere, Sept. 2023
R. Zhu, P. Huang, E. Ohn-Bar, and V. Saligrama · 2023
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Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving, May 2023
X. Jia, P. Wu, L. Chen, J. Xie, C. He, J. Yan, and H. Li · 2023
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DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving, Aug. 2023
X. Jia, Y. Gao, L. Chen, J. Yan, P. L. Liu, and H. Li · 2023
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Improved baselines with visual instruction tuning, 2023
H. Liu, C. Li, Y. Li, and Y. J. Lee · 2023
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MiniGPT-v2: Large language model as a unified interface for vision-language multi-task learning, Nov. 2023
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HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding, July 2023
X. Jia, P. Wu, L. Chen, Y. Liu, H. Li, and J. Yan · 2023
Cited alongside, same era.
Parting with Misconceptions about Learning-based Vehicle Motion Planning, Nov. 2023
D. Dauner, M. Hallgarten, A. Geiger, and K. Chitta · 2023
Cited alongside, same era.
Driver behavior modeling toward autonomous vehicles: Comprehensive review
N. M. Negash and J. Yang · 2023
Cited alongside, same era.
LLM4Drive: A Survey of Large Language Models for Autonomous Driving, Dec. 2023
Z. Yang, X. Jia, H. Li, and J. Yan · 2023
Cited alongside, same era.
LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning, Dec. 2023
S. P. Sharan, F. Pittaluga, V. K. B. G, and M. Chandraker · 2023
Cited alongside, same era.
LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving, Oct. 2023
H. Sha, Y. Mu, Y. Jiang, L. Chen, C. Xu, P. Luo, S. E. Li, M. Tomizuka, W. Zhan, and M. Ding · 2023
Cited alongside, same era.
Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles, Sept. 2023
C. Cui, Y. Ma, X. Cao, W. Ye, and Z. Wang · 2023
Cited alongside, same era.
J. Chen, D. Zhu, X. Shen, X. Li, Z. Liu, P. Zhang, R. Krishnamoorthi, V. Chandra, Y. Xiong, and M. Elhoseiny · 2023
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End-to-end Autonomous Driving: Challenges and Frontiers, Apr. 2024
L. Chen, P. Wu, K. Chitta, B. Jaeger, A. Geiger, and H. Li · 2024
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Efficient collaboration with unknown agents: Ignoring similar agents without checking similarity
Y. Li and S. Han · 2024
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LeGo-Drive: Language-enhanced Goal-oriented Closed-Loop End-to-End Autonomous Driving, Mar. 2024
P. Paul, A. Garg, T. Choudhary, A. K. Singh, and K. M. Krishna · 2024
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A Survey on Multimodal Large Language Models for Autonomous Driving
C. Cui, Y. Ma, X. Cao, W. Ye, Y. Zhou, K. Liang, J. Chen, J. Lu, Z. Yang, K.-D. Liao, T. Gao, E. Li, K. Tang, Z. Cao, T. Zhou, A. Liu, X. Yan, S. Mei, J. Cao, Z. Wang, and C. Zheng · 2024
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DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models, Feb. 2024
L. Wen, D. Fu, X. Li, X. Cai, T. Ma, P. Cai, M. Dou, B. Shi, L. He, and Y. Qiao · 2024
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Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving, Mar. 2024
M. Azarafza, M. Nayyeri, C. Steinmetz, S. Staab, and A. Rettberg · 2024
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An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning, Apr. 2024
Y. Luo, Z. Yang, F. Meng, Y. Li, J. Zhou, and Y. Zhang · 2024
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Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2), July 2024
Q. Li, X. Jia, S. Wang, and J. Yan · 2024
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GPT-4 Technical Report, Mar. 2024
O. et al · 2024
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Openbot-fleet: A system for collective learning with real robots
M. Müller, S. Brahmbhatt, A. Deka, Q. Leboutet, D. Hafner, and V. Koltun · 2024
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Llava-llama-3-8b: A reproduction towards llava-3 based on llama-3-8b llm backbone, 2024
W. Wang · 2024
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A Review on Autonomous Vehicles: Progress, Methods and Challenges
D. Parekh, N. Poddar, A. Rajpurkar, M. Chahal, N. Kumar, G. P. Joshi, and W. Cho · 2079
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