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Deep learning-based models are widely deployed in autonomous driving areas, especially the increasingly noticed end-to-end solutions.
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.
Interpretable learning for self-driving cars by visualizing causal attention
Jinkyu Kim and John Canny · 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.
Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
Mayank Bansal, Alex Krizhevsky, and Abhijit Ogale · 2018
Earlier work this paper cites.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Earlier work this paper cites.
Causal confusion in imitation learning
Pim De Haan, Dinesh Jayaraman, and Sergey Levine · 2019
Earlier work this paper cites.
Learning by cheating
Dian Chen, Brady Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2020
Earlier work this paper cites.
Who make drivers stop? towards driver-centric risk assessment: Risk object identification via causal inference
Chengxi Li, Stanley H Chan, and Yi-Ting Chen · 2020
Earlier work this paper cites.
Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d
Jonah Philion and Sanja Fidler · 2020
Earlier work this paper cites.
Neat: Neural attention fields for end-to-end autonomous driving
Kashyap Chitta, Aditya Prakash, and Andreas Geiger · 2021
Earlier work this paper cites.
Toward explainable and advisable model for self-driving cars
Jinkyu Kim, Anna Rohrbach, Zeynep Akata, Suhong Moon, Teruhisa Misu, Yi-Ting Chen, Trevor Darrell, and John Canny · 2021
Cited alongside, same era.
Learning interpretable end-to-end vision-based motion planning for autonomous driving with optical flow distillation
Hengli Wang, Peide Cai, Yuxiang Sun, Lujia Wang, and Ming Liu · 2021
Cited alongside, same era.
Learning from all vehicles
Dian Chen and Philipp Krähenbühl · 2022
Cited alongside, same era.
Transfuser: Imitation with transformer-based sensor fusion for autonomous driving
Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, Zehao Yu, Katrin Renz, and Andreas Geiger · 2022
Cited alongside, same era.
Resolving copycat problems in visual imitation learning via residual action prediction
Chia-Chi Chuang, Donglin Yang, Chuan Wen, and Yang Gao · 2022
Cited alongside, same era.
Steex: steering counterfactual explanations with semantics
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
Later among the works it cites.
End-to-end planning of autonomous driving in industry and academia: 2022-2023
Gongjin Lan and Qi Hao · 2023
Later among the works it cites.
Safety-enhanced autonomous driving using interpretable sensor fusion transformer
Hao Shao, Letian Wang, Ruobing Chen, Hongsheng Li, and Yu Liu · 2023
Later among the works it cites.
Drivelm: Driving with graph visual question answering
Chonghao Sima, Katrin Renz, Kashyap Chitta, Li Chen, Hanxue Zhang, Chengen Xie, Ping Luo, Andreas Geiger, and Hongyang Li · 2023
Later among the works it cites.
Drivegpt4: Interpretable end-to-end autonomous driving via large language model
Zhenhua Xu, Yujia Zhang, Enze Xie, Zhen Zhao, Yong Guo, Kenneth KY Wong, Zhenguo Li, and Hengshuang Zhao · 2023
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Paul Jacob, Éloi Zablocki, Hedi Ben-Younes, Mickaël Chen, Patrick Pérez, and Matthieu Cord · 2022
Cited alongside, same era.
Explaining autonomous driving actions with visual question answering
Shahin Atakishiyev, Mohammad Salameh, Housam Babiker, and Randy Goebel · 2023
Cited alongside, same era.
End-to-end autonomous driving: Challenges and frontiers
Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, and Hongyang Li · 2023
Cited alongside, same era.
Rethinking imitation-based planner for autonomous driving
Jie Cheng, Yingbing Chen, Xiaodong Mei, Bowen Yang, Bo Li, and Ming Liu · 2023
Cited alongside, same era.
Hidden biases of end-to-end driving models
Bernhard Jaeger, Kashyap Chitta, and Andreas Geiger · 2023
Cited alongside, same era.
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
Cited in the paper.
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
Cited in the paper.
Later among the works it cites.
Octet: Object-aware counterfactual explanations
Mehdi Zemni, Mickaël Chen, Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, and Matthieu Cord · 2023
Later among the works it cites.
Leveraging driver attention for an end-to-end explainable decision-making from frontal images
Javier Araluce, Luis M Bergasa, Manuel Ocaña, Ángel Llamazares, and Elena López-Guillén · 2024
Closest in time.
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
Closest in time.
Polarpoint-bev: Bird-eye-view perception in polar points for explainable end-to-end autonomous driving
Yuchao Feng and Yuxiang Sun · 2024
Closest in time.
A survey for foundation models in autonomous driving
Haoxiang Gao, Yaqian Li, Kaiwen Long, Ming Yang, and Yiqing Shen · 2024
Closest in time.