Fetching the paper…
Reading the bibliography…
Autonomous driving is of great interest to industry and academia alike.
Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
Earlier work this paper cites.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, Geoffrey E Hinton, et al · 1991
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Natural object recognition
Thomas M Strat · 1992
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Introduction to reinforcement learning
Richard S Sutton, Andrew G Barto, et al · 1998
Earlier work this paper cites.
Data assimilation using an ensemble kalman filter technique
Peter L Houtekamer and Herschel L Mitchell · 1998
Earlier work this paper cites.
Torcs, the open racing car simulator
Bernhard Wymann, Eric Espié, Christophe Guionneau, Christos Dimitrakakis, Rémi Coulom, and Andrew Sumner · 2000
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour · 2000
Earlier work this paper cites.
Aging, driving and vision
Joanne M Wood · 2002
Earlier work this paper cites.
Autonomous Robots: From Biological Inspiration to Implementation and Control (Intelligent Robotics and Autonomous Agents)
George A. Bekey · 2005
Earlier work this paper cites.
High speed obstacle avoidance using monocular vision and reinforcement learning
Jeff Michels, Ashutosh Saxena, and Andrew Y Ng · 2005
Earlier work this paper cites.
The impact of visual field loss on driving performance: evidence from on-road driving assessments
Lyne Racette and Evanne J Casson · 2005
Earlier work this paper cites.
Off-road obstacle avoidance through end-to-end learning
Urs Muller, Jan Ben, Eric Cosatto, Beat Flepp, and Yann L Cun · 2006
Earlier work this paper cites.
Stanley: The robot that won the darpa grand challenge: Research articles
Sebastian Thrun, Mike Montemerlo, Hendrik Dahlkamp, David Stavens, Andrei Aron, James Diebel, Philip Fong, John Gale, Morgan Halpenny, Gabriel Hoffmann, Kenny Lau, Celia Oakley, Mark Palatucci, Vaughan Pratt, Pascal Stang, Sven Strohband, Cedric Dupont, Lars-Erik Jendrossek, Christian Koelen, Charles Markey, Carlo Rummel, Joe van Niekerk, Eric Jensen, Philippe Alessandrini, Gary Bradski, Bob Davies, Scott Ettinger, Adrian Kaehler, Ara Nefian, and Pamela Mahoney · 2006
Earlier work this paper cites.
Learning to drive a real car in 20 minutes
Martin Riedmiller, Mike Montemerlo, and Hendrik Dahlkamp · 2007
Earlier work this paper cites.
Predictive active steering control for autonomous vehicle systems
P. Falcone, F. Borrelli, J. Asgari, H. E. Tseng, and D. Hrovat · 2007
Earlier work this paper cites.
Junior: The stanford entry in the urban challenge
Michael Montemerlo, Jan Becker, Suhrid Bhat, Hendrik Dahlkamp, Dmitri Dolgov, Scott Ettinger, Dirk Haehnel, Tim Hilden, Gabe Hoffmann, Burkhard Huhnke, Doug Johnston, Stefan Klumpp, Dirk Langer, Anthony Levandowski, Jesse Levinson, Julien Marcil, David Orenstein, Johannes Paefgen, Isaac Penny, Anna Petrovskaya, Mike Pflueger, Ganymed Stanek, David Stavens, Antone Vogt, and Sebastian Thrun · 2008
Earlier work this paper cites.
A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
Earlier work this paper cites.
The DARPA urban challenge: autonomous vehicles in city traffic
Martin Buehler, Karl Iagnemma, and Sanjiv Singh · 2009
Earlier work this paper cites.
Data mining for imbalanced datasets: An overview
Nitesh V Chawla · 2009
Earlier work this paper cites.
A new learning paradigm: Learning using privileged information
Vladimir Vapnik and Akshay Vashist · 2009
Earlier work this paper cites.
High level software architecture for autonomous mobile robot
Krejsa J., Vechet S., Hrbacek J., and Schreiber P · 2010
Earlier work this paper cites.
Bezier curve based path planning for autonomous vehicle in urban environment
Long Han, Hironari Yashiro, Hossein Tehrani Nik Nejad, Quoc Huy Do, and Seiichi Mita · 2010
Earlier work this paper cites.
Towards fully autonomous driving: Systems and algorithms
Jesse Levinson, Jake Askeland, Jan Becker, Jennifer Dolson, David Held, Soeren Kammel, J Zico Kolter, Dirk Langer, Oliver Pink, Vaughan Pratt, et al · 2011
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
Earlier work this paper cites.
Unbiased look at dataset bias
Antonio Torralba, Alexei A Efros, et al · 2011
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Evolving deep unsupervised convolutional networks for vision-based reinforcement learning
Jan Koutník, Jürgen Schmidhuber, and Faustino Gomez · 2014
Earlier work this paper cites.
Cnn features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
P Kingma Diederik, Max Welling, et al · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Combined longitudinal and lateral control for automated vehicle guidance
Rachid Attia, Rodolfo Orjuela, and Michel Basset · 2014
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Earlier work this paper cites.
Learning driving styles for autonomous vehicles from demonstration
Markus Kuderer, Shilpa Gulati, and Wolfram Burgard · 2015
Earlier work this paper cites.
Deepdriving: Learning affordance for direct perception in autonomous driving
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao · 2015
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
Earlier work this paper cites.
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
Earlier work this paper cites.
Car that knows before you do: Anticipating maneuvers via learning temporal driving models
Ashesh Jain, Hema S Koppula, Bharad Raghavan, Shane Soh, and Ashutosh Saxena · 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.
A survey of motion planning and control techniques for self-driving urban vehicles
Brian Paden, Michal Čáp, Sze Zheng Yong, Dmitry Yershov, and Emilio Frazzoli · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
Query-efficient imitation learning for end-to-end autonomous driving
Jiakai Zhang and Kyunghyun Cho · 2016
Cited alongside, same era.
Safe, multi-agent, reinforcement learning for autonomous driving
Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua · 2016
Cited alongside, same era.
Learning from maps: Visual common sense for autonomous driving
Ari Seff and Jianxiong Xiao · 2016
Lidar-video driving dataset: Learning driving policies effectively
Yiping Chen, Jingkang Wang, Jonathan Li, Cewu Lu, Zhipeng Luo, Han Xue, and Cheng Wang · 2018
Later among the works it cites.
Complex-yolo: An euler-region-proposal for real-time 3d object detection on point clouds
Martin Simon, Stefan Milz, Karl Amende, and Horst-Michael Gross · 2018
Later among the works it cites.
Pixor: Real-time 3d object detection from point clouds
Bin Yang, Wenjie Luo, and Raquel Urtasun · 2018
Later among the works it cites.
Sgpn: Similarity group proposal network for 3d point cloud instance segmentation
Weiyue Wang, Ronald Yu, Qiangui Huang, and Ulrich Neumann · 2018
Later among the works it cites.
On offline evaluation of vision-based driving models
Felipe Codevilla, Antonio M López, Vladlen Koltun, and Alexey Dosovitskiy · 2018
Later among the works it cites.
Visualbackprop: Efficient visualization of cnns for autonomous driving
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?
Nidhi Kalra and Susan M Paddock · 2016
Cited alongside, same era.
Challenges in autonomous vehicle testing and validation
Philip Koopman and Michael Wagner · 2016
Cited alongside, same era.
Eder Santana and George Hotz · 2016
Cited alongside, same era.
Towards a robust software architecture for autonomous robot software
Shuo Yang, Xinjun Mao, Sen Yang, Zhe Liu, Guochun Chen, Shuo Wang, Jiangtao Xue, and Zixi Xu · 2017
Cited alongside, same era.
CARLA: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
Cited alongside, same era.
Deep reinforcement learning framework for autonomous driving
Ahmad EL Sallab, Mohammed Abdou, Etienne Perot, and Senthil Yogamani · 2017
Cited alongside, same era.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Cited alongside, same era.
Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Larry J Ackel, Urs Muller, Phil Yeres, and Karol Zieba · 2018
Later among the works it cites.
Robust physical-world attacks on deep learning visual classification
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
Later among the works it cites.
Scalable end-to-end autonomous vehicle testing via rare-event simulation
Matthew O’Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, and John C Duchi · 2018
Later among the works it cites.
Failure prediction for autonomous driving
Simon Hecker, Dengxin Dai, and Luc Van Gool · 2018
Later among the works it cites.
Deeptest: Automated testing of deep-neural-network-driven autonomous cars
Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray · 2018
Later among the works it cites.
A commute in data: The comma2k19 dataset
Harald Schafer, Eder Santana, Andrew Haden, and Riccardo Biasini · 2018
Later among the works it cites.
Bdd100k: A diverse driving video database with scalable annotation tooling
Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell · 2018
Later among the works it cites.
Textual explanations for self-driving vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John Canny, and Zeynep Akata · 2018
Later among the works it cites.
Toward driving scene understanding: A dataset for learning driver behavior and causal reasoning
Vasili Ramanishka, Yi-Ting Chen, Teruhisa Misu, and Kate Saenko · 2018
Later among the works it cites.
A survey of autonomous driving: Common practices and emerging technologies
Ekim Yurtsever, Jacob Lambert, Alexander Carballo, and Kazuya Takeda · 2019
Later among the works it cites.
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
Later among the works it cites.
End-to-end interpretable neural motion planner
Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, and Raquel Urtasun · 2019
Later among the works it cites.
Learning by cheating
Dian Chen, Brady Zhou, and Vladlen Koltun · 2019
Later among the works it cites.
Multimodal end-to-end autonomous driving
Yi Xiao, Felipe Codevilla, Akhil Gurram, Onay Urfalioglu, and Antonio M López · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Later among the works it cites.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2019
Later among the works it cites.
Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
Later among the works it cites.
Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
Later among the works it cites.
Exploring the limitations of behavior cloning for autonomous driving
Felipe Codevilla, Eder Santana, Antonio M López, and Adrien Gaidon · 2019
Later among the works it cites.
A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
Later among the works it cites.
Does computer vision matter for action?
Brady Zhou, Philipp Krähenbühl, and Vladlen Koltun · 2019
Later among the works it cites.
Multinet: Multi-modal multi-task learning for autonomous driving
Sauhaarda Chowdhuri, Tushar Pankaj, and Karl Zipser · 2019
Later among the works it cites.
Causal confusion in imitation learning
Pim de Haan, Dinesh Jayaraman, and Sergey Levine · 2019
Later among the works it cites.
Urban driving with conditional imitation learning
Jeffrey Hawke, Richard Shen, Corina Gurau, Siddharth Sharma, Daniele Reda, Nikolay Nikolov, Przemyslaw Mazur, Sean Micklethwaite, Nicolas Griffiths, Amar Shah, et al · 2019
Later among the works it cites.
Simulation-based reinforcement learning for real-world autonomous driving
Błażej Osiński, Adam Jakubowski, Piotr Miłoś, Paweł Ziecina, Christopher Galias, and Henryk Michalewski · 2019
Later among the works it cites.
Vr-goggles for robots: Real-to-sim domain adaptation for visual control
Jingwei Zhang, Lei Tai, Peng Yun, Yufeng Xiong, Ming Liu, Joschka Boedecker, and Wolfram Burgard · 2019
Later among the works it cites.
Learning to drive from simulation without real world labels
Alex Bewley, Jessica Rigley, Yuxuan Liu, Jeffrey Hawke, Richard Shen, Vinh-Dieu Lam, and Alex Kendall · 2019
Later among the works it cites.
Learning accurate, comfortable and human-like driving
Simon Hecker, Dengxin Dai, and Luc Van Gool · 2019
Later among the works it cites.
Grounding human-to-vehicle advice for self-driving vehicles
Jinkyu Kim, Teruhisa Misu, Yi-Ting Chen, Ashish Tawari, and John Canny · 2019
Later among the works it cites.
Pointpillars: Fast encoders for object detection from point clouds
Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom · 2019
Later among the works it cites.
Pointrcnn: 3d object proposal generation and detection from point cloud
Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li · 2019
Later among the works it cites.
Di Feng, Christian Haase-Schuetz, Lars Rosenbaum, Heinz Hertlein, Fabian Duffhauss, Claudius Glaeser, Werner Wiesbeck, and Klaus Dietmayer · 2019
Later among the works it cites.
Variational end-to-end navigation and localization
Alexander Amini, Guy Rosman, Sertac Karaman, and Daniela Rus · 2019
Later among the works it cites.
Efficient black-box assessment of autonomous vehicle safety
Justin Norden, Matthew O’Kelly, and Aman Sinha · 2019
Later among the works it cites.
Early failure detection of deep end-to-end control policy by reinforcement learning
Keuntaek Lee, Kamil Saigol, and Evangelos A Theodorou · 2019
Later among the works it cites.
A2d2: Aev autonomous driving dataset
Jakob Geyer, Yohannes Kassahun, Mentar Mahmudi, Xavier Ricou, Rupesh Durgesh, Andrew S Chung, Lorenz Hauswald, Viet Hoang Pham, Maximilian Mhlegg, Sebastian Dorn, et al · 2019
Later among the works it cites.
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 · 2019
Later among the works it cites.
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, et al · 2019
Later among the works it cites.
The h3d dataset for full-surround 3d multi-object detection and tracking in crowded urban scenes
Abhishek Patil, Srikanth Malla, Haiming Gang, and Yi-Ting Chen · 2019
Later among the works it cites.
Instance segmentation of lidar point clouds
Feihu Zhang, Chenye Guan, Jin Fang, Song Bai, Ruigang Yang, Philip Torr, and Victor Prisacariu · 2020
Closest in time.
2019 autonomous vehicle disengagement reports
Department of Motor Vehicles State of California · 2020
Closest in time.
Phantom of the adas: Phantom attacks on driver-assistance systems
Ben Nassi, Dudi Nassi, Raz Ben-Netanel, Yisroel Mirsky, Oleg Drokin, and Yuval Elovici · 2020
Closest in time.
Can autonomous vehicles identify, recover from, and adapt to distribution shifts?
Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, and Yarin Gal · 2020
Closest in time.
New safety gizmos are making car insurance more expensive
Alex Davies · 2020
Closest in time.
http://www.dbehavior.net/index.html , Accessed: 2020-02-25
Large-scale driving behavior dataset · 2020
Closest in time.