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Benchmarking vision-based driving policies is challenging.
Robustness results in linear-quadratic gaussian based multivariable control designs
Norman Lehtomaki, Nils Sandell, and Michael Athans · 1981
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ALVINN: an autonomous land vehicle in a neural network
Dean Pomerleau · 1988
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Congested traffic states in empirical observations and microscopic simulations
Martin Treiber, Ansgar Hennecke, and Dirk Helbing · 2000
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Stanley: The robot that won the DARPA grand challenge
Sebastian Thrun, Michael Montemerlo, Hendrik Dahlkamp, David Stavens, Andrei Aron, James Diebel, Philip Fong, John Gale, Morgan Halpenny, Gabriel Hoffmann, Kenny Lau, Celia M. Oakley, Mark Palatucci, Vaughan R. Pratt, Pascal Stang, Sven Strohband, Cedric Dupont, Lars-Erik Jendrossek, Christian Koelen, Charles Markey, Carlo Rummel, Joe van Niekerk, Eric Jensen, Philippe Alessandrini, Gary R. Bradski, Bob Davies, Scott Ettinger, Adrian Kaehler, Ara V. Nefian, and Pamela Mahoney · 2006
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Optimal trajectory generation for dynamic street scenarios in a frenet frame
Moritz Werling, Julius Ziegler, Sören Kammel, and Sebastian Thrun · 2010
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Vehicle dynamics and control
Rajesh Rajamani · 2011
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End to end learning for self-driving cars
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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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On offline evaluation of vision-based driving models
Felipe Codevilla, Antonio M. Lopez, Vladlen Koltun, and Alexey Dosovitskiy · 2018
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Baidu apollo EM motion planner
Haoyang Fan, Fan Zhu, Changchun Liu, Liangliang Zhang, Li Zhuang, Dong Li, Weicheng Zhu, Jiangtao Hu, Hongye Li, and Qi Kong · 2018
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Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John Mark Allen, Vinh Dieu Lam, Alex Bewley, and Amar Shah · 2018
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Conditional affordance learning for driving in urban environments
Axel Sauer, Nikolay Savinov, and Andreas Geiger · 2018
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Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
Mayank Bansal, Alex Krizhevsky, and Abhijit S. Ogale · 2019
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Learning by cheating
Dian Chen, Brady Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2019
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Jointly learnable behavior and trajectory planning for self-driving vehicles
Abbas Sadat, Mengye Ren, Andrei Pokrovsky, Yen-Chen Lin, Ersin Yumer, and Raquel Urtasun · 2019
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End-to-end interpretable neural motion planner
Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, and Raquel Urtasun · 2019
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Learning robust control policies for end-to-end autonomous driving from data-driven simulation
Alexander Amini, Igor Gilitschenski, Jacob Phillips, Julia Moseyko, Rohan Banerjee, Sertac Karaman, and Daniela Rus · 2020
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art , volume 12
Joel Janai, Fatma Güney, Aseem Behl, and Andreas Geiger · 2020
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Perceive, predict, and plan: Safe motion planning through interpretable semantic representations
Abbas Sadat, Sergio Casas, Mengye Ren, Xinyu Wu, Pranaab Dhawan, and Raquel Urtasun · 2020
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Mp3: A unified model to map, perceive, predict and plan
Sergio Casas, Abbas Sadat, and Raquel Urtasun · 2021
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Learning to drive from a world on rails
Dian Chen, Vladlen Koltun, and Philipp Krähenbühl · 2021
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Neat: Neural attention fields for end-to-end autonomous driving
Kashyap Chitta, Aditya Prakash, and Andreas Geiger · 2021
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Multi-modal fusion transformer for end-to-end autonomous driving
Planning-oriented autonomous driving
Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, and Hongyang Li · 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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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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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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Dolphins: Multimodal language model for driving
Yingzi Ma, Yulong Cao, Jiachen Sun, Marco Pavone, and Chaowei Xiao · 2023
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Aditya Prakash, Kashyap Chitta, and Andreas Geiger · 2021
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Urban driver: Learning to drive from real-world demonstrations using policy gradients
Oliver Scheel, Luca Bergamini, Maciej Wolczyk, Błażej Osiński, and Peter Ondruska · 2021
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Vista 2.0: An open, data-driven simulator for multimodal sensing and policy learning for autonomous vehicles
Alexander Amini, Tsun-Hsuan Wang, Igor Gilitschenski, Wilko Schwarting, Zhijian Liu, Song Han, Sertac Karaman, and Daniela Rus · 2022
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ST-P3: End-to-end vision-based autonomous driving via spatial-temporal feature learning
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Plant: Explainable planning transformers via object-level representations
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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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Learning interactive driving policies via data-driven simulation
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Sivabalan Manivasagam, Ioan Andrei Bârsan, Jingkang Wang, Ze Yang, and Raquel Urtasun · 2023
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Reasonnet: End-to-end driving with temporal and global reasoning
Hao Shao, Letian Wang, Ruobing Chen, Steven L. Waslander, Hongsheng Li, and Yu Liu · 2023
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DriveLM: Driving with graph visual question answering
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LLM4Drive: A survey of large language models for autonomous driving
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Fusionad: Multi-modality fusion for prediction and planning tasks of autonomous driving
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Rethinking the open-loop evaluation of end-to-end autonomous driving in nuscenes
Jiang-Tian Zhai, Ze Feng, Jinhao Du, Yongqiang Mao, Jiang-Jiang Liu, Zichang Tan, Yifu Zhang, Xiaoqing Ye, and Jingdong Wang · 2023
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Coaching a teachable student
Jimuyang Zhang, Zanming Huang, and Eshed Ohn-Bar · 2023
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VADv2: End-to-end vectorized autonomous driving via probabilistic planning
Shaoyu Chen, Bo Jiang, Hao Gao, Bencheng Liao, Qing Xu, Qian Zhang, Chang Huang, Wenyu Liu, and Xinggang Wang · 2024
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Sledge: Synthesizing driving environments with generative models and rule-based traffic
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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, and Holger Caesar · 2024
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NeuRAD: Neural rendering for autonomous driving
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Para-drive: Parallelized architecture for real-time autonomous driving
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Embodied understanding of driving scenarios
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