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To safely navigate intricate real-world scenarios, autonomous vehicles must be able to adapt to diverse road conditions and anticipate future events.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson · 2016
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Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?
Nidhi Kalra and Susan M. Paddock · 2016
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A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
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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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Flow: Architecture and benchmarking for reinforcement learning in traffic control
Cathy Wu, Abdul Rahman Kreidieh, Kanaad Parvate, Eugene Vinitsky, and Alexandre Bayen · 2017
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David Ha and Jürgen Schmidhuber · 2018
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Towards sample efficient reinforcement learning
Yang Yu · 2018
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Microscopic traffic simulation using sumo
Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Flötteröd, Robert Hilbrich, Leonhard Lücken, Johannes Rummel, Peter Wagner, and Evamarie Wiessner · 2018
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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A survey of deep learning techniques for autonomous driving
Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias, and Gigel Macesanu · 2020
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Waymo public road safety performance data
Matthew Schwall, Tom Daniel, Trent Victor, Francesca Favaro, and Henning Hohnhold · 2020
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A survey of autonomous driving: Common practices and emerging technologies
Ekim Yurtsever, Jacob Lambert, Alexander Carballo, and Kazuya Takeda · 2020
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Mastering atari with discrete world models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2020
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Planning to explore via self-supervised world models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, and Deepak Pathak · 2020
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Learning by cheating
Dian Chen, Brady Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2020
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Motion planning for autonomous driving: The state of the art and future perspectives
Siyu Teng, Xuemin Hu, Peng Deng, Bai Li, Yuchen Li, Yunfeng Ai, Dongsheng Yang, Lingxi Li, Zhe Xuanyuan, Fenghua Zhu, et al · 2023
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End-to-end autonomous driving: Challenges and frontiers
Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, and Hongyang Li · 2023
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Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
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Gaia-1: A generative world model for autonomous driving
Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, and Gianluca Corrado · 2023
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Drivedreamer: Towards real-world-driven world models for autonomous driving
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Discovering and achieving goals via world models
Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, and Deepak Pathak · 2021
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A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2021
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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2021
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Model-based imitation learning for urban driving
Anthony Hu, Gianluca Corrado, Nicolas Griffiths, Zachary Murez, Corina Gurau, Hudson Yeo, Alex Kendall, Roberto Cipolla, and Jamie Shotton · 2022
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Iso-dream: Isolating and leveraging noncontrollable visual dynamics in world models
Minting Pan, Xiangming Zhu, Yunbo Wang, and Xiaokang Yang · 2022
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Zeyu Gao, Yao Mu, Ruoyan Shen, Chen Chen, Yangang Ren, Jianyu Chen, Shengbo Eben Li, Ping Luo, and Yanfeng Lu · 2022
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A survey of multi-agent reinforcement learning with communication
Changxi Zhu, Mehdi Dastani, and Shihan Wang · 2022
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Xiaofeng Wang, Zheng Zhu, Guan Huang, Xinze Chen, and Jiwen Lu · 2023
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Adriver-i: A general world model for autonomous driving
Fan Jia, Weixin Mao, Yingfei Liu, Yucheng Zhao, Yuqing Wen, Chi Zhang, Xiangyu Zhang, and Tiancai Wang · 2023
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Dense reinforcement learning for safety validation of autonomous vehicles
Shuo Feng, Haowei Sun, Xintao Yan, Haojie Zhu, Zhengxia Zou, Shengyin Shen, and Henry X Liu · 2023
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https://safety.fhwa.dot.gov/local_rural/training/fhwasa1109/app_c.cfm
Crash rate calculations by US department of transportation federal highway administration · 2024
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World models for autonomous driving: An initial survey
Yanchen Guan, Haicheng Liao, Zhenning Li, Guohui Zhang, and Chengzhong Xu · 2024
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Is sora a world simulator? a comprehensive survey on general world models and beyond
Zheng Zhu, Xiaofeng Wang, Wangbo Zhao, Chen Min, Nianchen Deng, Min Dou, Yuqi Wang, Botian Shi, Kai Wang, Chi Zhang, et al · 2024
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Drivedreamer-2: Llm-enhanced world models for diverse driving video generation
Guosheng Zhao, Xiaofeng Wang, Zheng Zhu, Xinze Chen, Guan Huang, Xiaoyi Bao, and Xingang Wang · 2024
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Qifeng Li, Xiaosong Jia, Shaobo Wang, and Junchi Yan · 2024
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