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Current end-to-end autonomous driving methods either run a controller based on a planned trajectory or perform control prediction directly, which have spanned two separately studied lines of research.
Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1988
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Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
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Multitask learning
Rich Caruana · 1997
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
Earlier work this paper cites.
Multi-task feature learning
Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Model predictive control
Eduardo F Camacho and Carlos Bordons Alba · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Learning to propose objects
Philipp Krähenbühl and Vladlen Koltun · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
CARLA: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
End-to-end learning of driving models from large-scale video datasets
Huazhe Xu, Yang Gao, Fisher Yu, and Trevor Darrell · 2017
Earlier work this paper cites.
CARLA: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Earlier work this paper cites.
End-to-end driving via conditional imitation learning
Felipe Codevilla, Matthias Müller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy · 2018
Earlier work this paper cites.
Zhihao Li, Toshiyuki Motoyoshi, Kazuma Sasaki, Tetsuya Ogata, and Shigeki Sugano · 2018
Earlier work this paper cites.
Cirl: Controllable imitative reinforcement learning for vision-based self-driving
Xiaodan Liang, Tairui Wang, Luona Yang, and Eric Xing · 2018
Earlier work this paper cites.
Out-of-distribution detection using an ensemble of self supervised leave-out classifiers
Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu, Dipankar Das, Bharat Kaul, and Theodore L Willke · 2018
Earlier work this paper cites.
End-to-end multi-modal multi-task vehicle control for self-driving cars with visual perceptions
Zhengyuan Yang, Yixuan Zhang, Jerry Yu, Junjie Cai, and Jiebo Luo · 2018
Earlier work this paper cites.
Multinet++: Multi-stream feature aggregation and geometric loss strategy for multi-task learning
Sumanth Chennupati, Ganesh Sistu, Senthil Yogamani, and Samir A Rawashdeh · 2019
Earlier work this paper cites.
Exploring the limitations of behavior cloning for autonomous driving
Felipe Codevilla, Eder Santana, Antonio M López, and Adrien Gaidon · 2019
Earlier work this paper cites.
Model predictive path following control for autonomous cars considering a measurable disturbance: Implementation, testing, and verification
Hongyan Guo, Dongpu Cao, Hong Chen, Zhenping Sun, and Yunfeng Hu · 2019
Earlier work this paper cites.
Learning to steer by mimicking features from heterogeneous auxiliary networks
Yuenan Hou, Zheng Ma, Chunxiao Liu, and Chen Change Loy · 2019
Cited alongside, same era.
Learning to drive in a day
Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, and Amar Shah · 2019
Cited alongside, same era.
Multi-task multi-sensor fusion for 3d object detection
Ming Liang, Bin Yang, Yun Chen, Rui Hu, and Raquel Urtasun · 2019
Cited alongside, same era.
Deep local trajectory replanning and control for robot navigation
Ashwini Pokle, Roberto Martín-Martín, Patrick Goebel, Vincent Chow, Hans M Ewald, Junwei Yang, Zhenkai Wang, Amir Sadeghian, Dorsa Sadigh, Silvio Savarese, et al · 2019
Cited alongside, same era.
Multi-task mutual learning for vehicle re-identification
Georgia Rajamanoharan, Aytaç Kanacı, Minxian Li, Shaogang Gong, et al · 2019
Cited alongside, same era.
Deep imitative models for flexible inference, planning, and control
Expert drivers for autonomous driving
Bernhard Jaeger · 2021
Later among the works it cites.
Omnidet: Surround view cameras based multi-task visual perception network for autonomous driving
Varun Ravi Kumar, Senthil Yogamani, Hazem Rashed, Ganesh Sitsu, Christian Witt, Isabelle Leang, Stefan Milz, and Patrick Mäder · 2021
Later among the works it cites.
Multi-modal fusion transformer for end-to-end autonomous driving
Aditya Prakash, Kashyap Chitta, and Andreas Geiger · 2021
Later among the works it cites.
Yolop: You only look once for panoptic driving perception
Dong Wu, Manwen Liao, Weitian Zhang, and Xinggang Wang · 2021
Later among the works it cites.
Explainability of vision-based autonomous driving systems: Review and challenges
Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, and Matthieu Cord · 2021
Later among the works it cites.
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Nicholas Rhinehart, Rowan McAllister, and Sergey Levine · 2019
Cited alongside, same era.
End-to-end interpretable neural motion planner
Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, and Raquel Urtasun · 2019
Cited alongside, same era.
The nvidia pilotnet experiments
Mariusz Bojarski, Chenyi Chen, Joyjit Daw, Alperen Değirmenci, Joya Deri, Bernhard Firner, Beat Flepp, Sachin Gogri, Jesse Hong, Lawrence Jackel, et al · 2020
Cited alongside, same era.
Learning by cheating
Dian Chen, Brady Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2020
Cited alongside, same era.
Urban driving with conditional imitation learning
Jeffrey Hawke, Richard Shen, Corina Gurau, Siddharth Sharma, Daniele Reda, Nikolay Nikolov, Przemysław Mazur, Sean Micklethwaite, Nicolas Griffiths, Amar Shah, et al · 2020
Cited alongside, same era.
Multi-task learning with future states for vision-based autonomous driving
Inhan Kim, Hyemin Lee, Joonyeong Lee, Eunseop Lee, and Daijin Kim · 2020
Cited alongside, same era.
Learning situational driving
Eshed Ohn-Bar, Aditya Prakash, Aseem Behl, Kashyap Chitta, and Andreas Geiger · 2020
Cited alongside, same era.
Learning by watching
Jimuyang Zhang and Eshed Ohn-Bar · 2021
Later among the works it cites.
End-to-end urban driving by imitating a reinforcement learning coach
Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu, and Luc Van Gool · 2021
Later among the works it cites.
Sam: Squeeze-and-mimic networks for conditional visual driving policy learning
Albert Zhao, Tong He, Yitao Liang, Haibin Huang, Guy Van den Broeck, and Stefano Soatto · 2021
Later among the works it cites.
Gri: General reinforced imitation and its application to vision-based autonomous driving
Raphael Chekroun, Marin Toromanoff, Sascha Hornauer, and Fabien Moutarde · 2021
Later among the works it cites.
Learning to drive from a world on rails
Dian Chen, Vladlen Koltun, and Philipp Krähenbühl · 2021
Later among the works it cites.
Neat: Neural attention fields for end-to-end autonomous driving
Kashyap Chitta, Aditya Prakash, and Andreas Geiger · 2021
Later among the works it cites.
Expert drivers for autonomous driving
Bernhard Jaeger · 2021
Later among the works it cites.
Multi-modal fusion transformer for end-to-end autonomous driving
Aditya Prakash, Kashyap Chitta, and Andreas Geiger · 2021
Later among the works it cites.
Learning by watching
Jimuyang Zhang and Eshed Ohn-Bar · 2021
Later among the works it cites.
End-to-end urban driving by imitating a reinforcement learning coach
Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu, and Luc Van Gool · 2021
Later among the works it cites.
https://leaderboard.carla.org/ , 2022
CARLA autonomous driving leaderboard · 2022
Closest in time.
Learning from all vehicles
Dian Chen and Philipp Krähenbühl · 2022
Closest in time.
Persformer: 3d lane detection via perspective transformer and the openlane benchmark
Li Chen, Chonghao Sima, Yang Li, Zehan Zheng, Jiajie Xu, Xiangwei Geng, Hongyang Li, Conghui He, Jianping Shi, Yu Qiao, et al · 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 mixture of domain-specific experts via disentangled factors for autonomous driving
Inhan Kim, Joonyeong Lee, and Daijin Kim · 2022
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Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection
Yingwei Li, Adams Wei Yu, Tianjian Meng, Ben Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Bo Wu, Yifeng Lu, Denny Zhou, et al · 2022
Closest in time.
Cadre: A cascade deep reinforcement learning framework for vision-based autonomous urban driving
Yinuo Zhao, Kun Wu, Zhiyuan Xu, Zhengping Che, Qi Lu, Jian Tang, and Chi Harold Liu · 2022
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Multi-task conditional imitation learning for autonomous navigation at crowded intersections
Zeyu Zhu and Huijing Zhao · 2022
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https://leaderboard.carla.org/ , 2022
CARLA autonomous driving leaderboard · 2022
Closest in time.
Learning from all vehicles
Dian Chen and Philipp Krähenbühl · 2022
Closest in time.