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In an era marked by the rapid scaling of foundation models, autonomous driving technologies are approaching a transformative threshold where end-to-end autonomous driving (E2E-AD) emerges due to its potential of scaling up in the data-driven manner.
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Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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Attention is all you need
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End-to-end driving via conditional imitation learning
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Causal confusion in imitation learning
Pim De Haan, Dinesh Jayaraman, and Sergey Levine · 2019
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Exploring the limitations of behavior cloning for autonomous driving
Felipe Codevilla, Eder Santana, Antonio M López, and Adrien Gaidon · 2019
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Yinda Xu, Zeyu Wang, Zuoxin Li, Ye Yuan, and Gang Yu · 2020
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nuscenes: A multimodal dataset for autonomous driving
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Julian Bernhard, Klemens Esterle, Patrick Hart, and Tobias Kessler · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
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Marin Toromanoff, Emilie Wirbel, and Fabien Moutarde · 2020
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Dian Chen, Brady Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2020
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Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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On the opportunities and risks of foundation models
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Xiaosong Jia, Liting Sun, Masayoshi Tomizuka, and Wei Zhan · 2021
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Argoverse 2: Next generation datasets for self-driving perception and forecasting
Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, and James Hays · 2021
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End-to-end urban driving by imitating a reinforcement learning coach
Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu, and Luc Van Gool · 2021
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Kashyap Chitta, Aditya Prakash, and Andreas Geiger · 2021
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Multi-modal fusion transformer for end-to-end autonomous driving
Aditya Prakash, Kashyap Chitta, and Andreas Geiger · 2021
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Autonomy 2.0: Why is self-driving always 5 years away?
Ashesh Jain, Luca Del Pero, Hugo Grimmett, and Peter Ondruska · 2021
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Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline
Penghao Wu, Xiaosong Jia, Li Chen, Junchi Yan, Hongyang Li, and Yu Qiao · 2022
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Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers
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Multi-agent trajectory prediction by combining egocentric and allocentric views
Xiaosong Jia, Liting Sun, Hang Zhao, Masayoshi Tomizuka, and Wei Zhan · 2022
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Learning from all vehicles
Dian Chen and Philipp Krähenbühl · 2022
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Meta-reward-net: Implicitly differentiable reward learning for preference-based reinforcement learning
Runze Liu, Fengshuo Bai, Yali Du, and Yaodong Yang · 2022
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Parting with misconceptions about learning-based vehicle motion planning
Daniel Dauner, Marcel Hallgarten, Andreas Geiger, and Kashyap Chitta · 2023
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Vad: Vectorized scene representation for efficient autonomous driving
Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, and Xinggang Wang · 2023
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Rethinking the open-loop evaluation of end-to-end autonomous driving in nuscenes
Jiang-Tian Zhai, Ze Feng, Jihao Du, Yongqiang Mao, Jiang-Jiang Liu, Zichang Tan, Yifu Zhang, Xiaoqing Ye, and Jingdong Wang · 2023
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Is ego status all you need for open-loop end-to-end autonomous driving?
Zhiqi Li, Zhiding Yu, Shiyi Lan, Jiahan Li, Jan Kautz, Tong Lu, and José M. Álvarez · 2023
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Driveadapter: Breaking the coupling barrier of perception and planning in end-to-end autonomous driving
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Plant: explainable planning transformers via object-level representations
Katrin Renz, Kashyap Chitta, Otniel-Bogdan Mercea, A. Sophia Koepke, Zeynep Akata, and Andreas Geiger · 2022
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St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning
Shengchao Hu, Li Chen, Penghao Wu, Hongyang Li, Junchi Yan, and Dacheng Tao · 2022
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Learning from all vehicles
Dian Chen and Philipp Krähenbühl · 2022
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Safety-enhanced autonomous driving using interpretable sensor fusion transformer
Hao Shao, Letian Wang, RuoBing Chen, Hongsheng Li, and Yu Liu · 2022
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Model-based imitation learning for urban driving
Anthony Hu, Gianluca Corrado, Nicolas Griffiths, Zak Murez, Corina Gurau, Hudson Yeo, Alex Kendall, Roberto Cipolla, and Jamie Shotton · 2022
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Mmfn: multi-modal-fusion-net for end-to-end driving
Qingwen Zhang, Mingkai Tang, Ruoyu Geng, Feiyi Chen, Ren Xin, and Lujia Wang · 2022
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Curriculum learning: A survey, 2022
Petru Soviany, Radu Tudor Ionescu, Paolo Rota, and Nicu Sebe · 2022
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Xiaosong Jia, Yulu Gao, Li Chen, Junchi Yan, Patrick Langechuan Liu, and Hongyang Li · 2023
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Think twice before driving: Towards scalable decoders for end-to-end autonomous driving
Xiaosong Jia, Penghao Wu, Li Chen, Jiangwei Xie, Conghui He, Junchi Yan, and Hongyang Li · 2023
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Picor: Multi-task deep reinforcement learning with policy correction
Fengshuo Bai, Hongming Zhang, Tianyang Tao, Zhiheng Wu, Yanna Wang, and Bo Xu · 2023
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Hidden biases of end-to-end driving models
Bernhard Jaeger, Kashyap Chitta, and Andreas Geiger · 2023
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Policy pre-training for autonomous driving via self-supervised geometric modeling
Penghao Wu, Li Chen, Hongyang Li, Xiaosong Jia, Junchi Yan, and Yu Qiao · 2023
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Coaching a teachable student
Jimuyang Zhang, Zanming Huang, and Eshed Ohn-Bar · 2023
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Flatfusion: Delving into details of sparse transformer-based camera-lidar fusion for autonomous driving, 2024
Yutao Zhu, Xiaosong Jia, Xinyu Yang, and Junchi Yan · 2024
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Amp: Autoregressive motion prediction revisited with next token prediction for autonomous driving
Xiaosong Jia, Shaoshuai Shi, Zijun Chen, Li Jiang, Wenlong Liao, Tao He, and Junchi Yan · 2024
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Lightzero: A unified benchmark for monte carlo tree search in general sequential decision scenarios
Yazhe Niu, Yuan Pu, Zhenjie Yang, Xueyan Li, Tong Zhou, Jiyuan Ren, Shuai Hu, Hongsheng Li, and Yu Liu · 2024
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Qifeng Li, Xiaosong Jia, Shaobo Wang, and Junchi Yan · 2024
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Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research
Cole Gulino, Justin Fu, Wenjie Luo, George Tucker, Eli Bronstein, Yiren Lu, Jean Harb, Xinlei Pan, Yan Wang, Xiangyu Chen, et al · 2024
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Towards learning-based planning: The nuplan benchmark for real-world autonomous driving
Napat Karnchanachari, Dimitris Geromichalos, Kok Seang Tan, Nanxiang Li, Christopher Eriksen, Shakiba Yaghoubi, Noushin Mehdipour, Gianmarco Bernasconi, Whye Kit Fong, Yiluan Guo, et al · 2024
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Efficient preference-based reinforcement learning via aligned experience estimation
Fengshuo Bai, Rui Zhao, Hongming Zhang, Sijia Cui, Ying Wen, Yaodong Yang, Bo Xu, and Lei Han · 2024
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Activead: Planning-oriented active learning for end-to-end autonomous driving, 2024
Han Lu, Xiaosong Jia, Yichen Xie, Wenlong Liao, Xiaokang Yang, and Junchi Yan · 2024
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PDM-Lite: A rule-based planner for carla leaderboard 2.0
Jens Beißwenger · 2024
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Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking
Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, and Kashyap Chitta · 2024
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Generalized predictive model for autonomous driving
Jiazhi Yang, Shenyuan Gao, Yihang Qiu, Li Chen, Tianyu Li, Bo Dai, Kashyap Chitta, Penghao Wu, Jia Zeng, Ping Luo, Jun Zhang, Andreas Geiger, Yu Qiao, and Hongyang Li · 2024
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Vista: A generalizable driving world model with high fidelity and versatile controllability
Shenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta, Yihang Qiu, Andreas Geiger, Jun Zhang, and Hongyang Li · 2024
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Simgen: Simulator-conditioned driving scene generation
Yunsong Zhou, Michael Simon, Zhenghao Peng, Sicheng Mo, Hongzi Zhu, Minyi Guo, and Bolei Zhou · 2024
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