Fetching the paper…
Reading the bibliography…
End-to-end autonomous driving is a fully differentiable machine learning system that takes raw sensor input data and other metadata as prior information and directly outputs the ego vehicle's control signals or planned trajectories.
Alvinn: An autonomous land vehicle in a neural network
Dean Pomerleau · 1989
Earlier work this paper cites.
Torcs, the open racing car simulator
Eric Espié, Christophe Guionneau, Bernhard Wymann, Christos Dimitrakakis, Rémi Coulom, and Andrew Sumner · 2005
Earlier work this paper cites.
Covariate shift adaptation by importance weighted cross validation
Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert Müller · 2007
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
Earlier work this paper cites.
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Earlier work this paper cites.
Simple online and realtime tracking
Alex Bewley, ZongYuan Ge, Lionel Ott, Fabio Ramos, and Ben Upcroft · 2016
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 reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
Earlier work this paper cites.
A general multi-context embedding model for mining human trajectory data
Ningnan Zhou, Wayne Xin Zhao, Xiao Zhang, Ji-Rong Wen, and Shan Wang · 2016
Earlier work this paper cites.
Explaining how a deep neural network trained with end-to-end learning steers a car
Mariusz Bojarski, Philip Yeres, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Lawrence Jackel, and Urs Muller · 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.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 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
Cited alongside, same era.
End-to-end learning of driving models with surround-view cameras and route planners
Simon Hecker, Dengxin Dai, and Luc Van Gool · 2018
Cited alongside, same era.
An algorithmic perspective on imitation learning
Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J Andrew Bagnell, Pieter Abbeel, Jan Peters, et al · 2018
Cited alongside, same era.
Variational end-to-end navigation and localization
Alexander Amini, Guy Rosman, Sertac Karaman, and Daniela Rus · 2019
Cited alongside, same era.
Deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety
Jianyu Chen, Bodi Yuan, and Masayoshi Tomizuka · 2019
Cited alongside, same era.
Q-learning algorithms: A comprehensive classification and applications
nuplan: A closed-loop ml-based planning benchmark for autonomous vehicles
Holger Caesar, Juraj Kabzan, Kok Seang Tan, Whye Kit Fong, Eric Wolff, Alex Lang, Luke Fletcher, Oscar Beijbom, and Sammy Omari · 2021
Later among the works it cites.
Hdmapgen: A hierarchical graph generative model of high definition maps
Lu Mi, Hang Zhao, Charlie Nash, Xiaohan Jin, Jiyang Gao, Chen Sun, Cordelia Schmid, Nir Shavit, Yuning Chai, and Dragomir Anguelov · 2021
Later among the works it cites.
Florence: A new foundation model for computer vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al · 2021
Later among the works it cites.
A survey of deep rl and il for autonomous driving policy learning
Zeyu Zhu and Huijing Zhao · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Beakcheol Jang, Myeonghwi Kim, Gaspard Harerimana, and Jong Wook Kim · 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.
Scalability in perception for autonomous driving: Waymo open dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov · 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.
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
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.
Badr Ben Elallid, Nabil Benamar, Abdelhakim Senhaji Hafid, Tajjeeddine Rachidi, and Nabil Mrani · 2022
Later among the works it cites.
A survey of localization methods for autonomous vehicles in highway scenarios
Johann Laconte, Abderrahim Kasmi, Romuald Aufrère, Maxime Vaidis, and Roland Chapuis · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
End-to-end autonomous driving: Challenges and frontiers
Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, and Hongyang Li · 2023
Closest in time.
Xiaosong Jia, Yulu Gao, Li Chen, Junchi Yan, Patrick Langechuan Liu, and Hongyang Li · 2023
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Closest in time.
Generative ai-empowered simulation for autonomous driving in vehicular mixed reality metaverses
Minrui Xu, Dusit Niyato, Junlong Chen, Hongliang Zhang, Jiawen Kang, Zehui Xiong, Shiwen Mao, and Zhu Han · 2023
Closest in time.
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
Closest in time.
Guided conditional diffusion for controllable traffic simulation
Ziyuan Zhong, Davis Rempe, Danfei Xu, Yuxiao Chen, Sushant Veer, Tong Che, Baishakhi Ray, and Marco Pavone · 2023
Closest in time.