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Autonomous vehicle (AV) stacks have traditionally relied on decomposed approaches, with separate modules handling perception, prediction, and planning.
ALVINN: an autonomous land vehicle in a neural network
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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End to end learning for self-driving cars, 2016
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zeiba · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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End-to-end driving via conditional imitation learning
Felipe Codevilla, Matthias Müller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy · 2018
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3d semantic segmentation with submanifold sparse convolutional networks
Benjamin Graham, Martin Engelcke, and Laurens Van Der Maaten · 2018
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Lanelet2: A high-definition map framework for the future of automated driving
Fabian Poggenhans, Jan-Hendrik Pauls, Johannes Janosovits, Stefan Orf, Maximilian Naumann, Florian Kuhnt, and Matthias Mayr · 2018
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Yin Zhou and Oncel Tuzel · 2018
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Argoverse: 3d tracking and forecasting with rich maps
Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, and James Hays · 2019
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A constructive prediction of the generalization error across scales
Jonathan S Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit · 2019
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Waymo Open Dataset: An autonomous driving dataset
Waymo · 2019
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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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VectorNet: Encoding HD maps and agent dynamics from vectorized representation
Jiyang Gao, Chen Sun, Hang Zhao, Yi Shen, Dragomir Anguelov, Congcong Li, and Cordelia Schmid · 2020
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Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al · 2020
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One thousand and one hours: Self-driving motion prediction dataset
J. Houston, G. Zuidhof, L. Bergamini, Y. Ye, A. Jain, S. Omari, V. Iglovikov, and P. Ondruska · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Learning lane graph representations for motion forecasting
Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, and Raquel Urtasun · 2020
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Language models are few-shot learners
Ben Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, S Agarwal, et al · 2020
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CoverNet: Multimodal behavior prediction using trajectory sets
T. Phan-Minh, E. C. Grigore, F. A. Boulton, O. Beijbom, and E. M. Wolff · 2020
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Lift, Splat, Shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3D
Jonah Philion and Sanja Fidler · 2020
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Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data
Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, and Marco Pavone · 2020
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TNT: Target-driveN Trajectory Prediction
H. Zhao, J. Gao, T. Lan, C. Sun, B. Sapp, B. Varadarajan, Y. Shen, Y. Shen, Y. Chai, C. Schmid, C. Li, and D. Anguelov · 2020
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A theoretical-empirical approach to estimating sample complexity of dnns
Devansh Bisla, Apoorva Nandini Saridena, and Anna Choromanska · 2021
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Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset
Scott Ettinger, Shuyang Cheng, Benjamin Caine, Chenxi Liu, Hang Zhao, Sabeek Pradhan, Yuning Chai, Ben Sapp, Charles Qi, Yin Zhou, Zoey Yang, Aurélien Chouard, Pei Sun, Jiquan Ngiam, Vijay Vasudevan, Alexander McCauley, Jonathon Shlens, and Dragomir Anguelov · 2021
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HOME: Heatmap output for future motion estimation
T. Gilles, S. Sabatini, D. Tsishkou, B. Stanciulescu, and F. Moutarde · 2021
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Data and parameter scaling laws for neural machine translation
Mitchell A Gordon, Kevin Duh, and Jared Kaplan · 2021
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DenseTNT: End-to-end trajectory prediction from dense goal sets
J. Gu, C. Sun, and H. Zhao · 2021
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A probabilistic method to predict classifier accuracy on larger datasets given small pilot data
Ethan Harvey, Wansu Chen, David M Kent, and Michael C Hughes · 2023
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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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Tri-perspective view for vision-based 3d semantic occupancy prediction
Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang, Jie Zhou, and Jiwen Lu · 2023
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A meta-learning approach to predicting performance and data requirements
Achin Jain, Gurumurthy Swaminathan, Paolo Favaro, Hao Yang, Avinash Ravichandran, Hrayr Harutyunyan, Alessandro Achille, Onkar Dabeer, Bernt Schiele, Ashwin Swaminathan, et al · 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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Nuplan: A closed-loop ml-based planning benchmark for autonomous vehicles
K. Tan et al. H. Caesar, J. Kabzan · 2021
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Voxel transformer for 3d object detection
Jiageng Mao, Yujing Xue, Minzhe Niu, Haoyue Bai, Jiashi Feng, Xiaodan Liang, Hang Xu, and Chunjing Xu · 2021
Cited alongside, same era.
Categorical depth distribution network for monocular 3d object detection
Cody Reading, Ali Harakeh, Julia Chae, and Steven L Waslander · 2021
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Argoverse 2: Next generation datasets for self-driving perception and forecasting
Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, and James Hays · 2021
Cited alongside, same era.
AgentFormer: Agent-aware transformers for socio-temporal multi-agent forecasting
Ye Yuan, Xinshuo Weng, Yanglan Ou, and Kris M. Kitani · 2021
Cited alongside, same era.
Revisiting neural scaling laws in language and vision
Ibrahim M Alabdulmohsin, Behnam Neyshabur, and Xiaohua Zhai · 2022
Cited alongside, same era.
Data scaling laws in nmt: The effect of noise and architecture
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, and Orhan Firat · 2022
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Bevdepth: Acquisition of reliable depth for multi-view 3d object detection
Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li · 2023
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VectorMapNet: End-to-end vectorized HD map learning
Yicheng Liu, Yuan Yuantian, Yue Wang, Yilun Wang, and Hang Zhao · 2023
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Lanelet2 for nuscenes: Enabling spatial semantic relationships and diverse map-based anchor paths
Alexander Naumann, Felix Hertlein, Daniel Grimm, Maximilian Zipfl, Steffen Thoma, Achim Rettinger, Lavdim Halilaj, Juergen Luettin, Stefan Schmid, and Holger Caesar · 2023
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Large trajectory models are scalable motion predictors and planners
Qiao Sun, Shiduo Zhang, Danjiao Ma, Jingzhe Shi, Derun Li, Simian Luo, Yu Wang, Ningyi Xu, Guangzhi Cao, and Hang Zhao · 2023
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Surroundocc: Multi-camera 3d occupancy prediction for autonomous driving
Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, and Jiwen Lu · 2023
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Query-centric trajectory prediction
Zikang Zhou, Jianping Wang, Yung-Hui Li, and Yu-Kai Huang · 2023
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Explaining neural scaling laws
Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, and Utkarsh Sharma · 2024
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Training from zero: Forecasting of radio frequency machine learning data quantity
William H Clark IV and Alan J Michaels · 2024
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Scaling laws and compute-optimal training beyond fixed training durations
Alexander Hägele, Elie Bakouch, Atli Kosson, Loubna Ben Allal, Leandro Von Werra, and Martin Jaggi · 2024
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Emma: End-to-end multimodal model for autonomous driving
Jyh-Jing Hwang, Runsheng Xu, Hubert Lin, Wei-Chih Hung, Jingwei Ji, Kristy Choi, Di Huang, Tong He, Paul Covington, Benjamin Sapp, et al · 2024
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Is ego status all you need for open-loop end-to-end autonomous driving?
Zhiqi Li, Zhiding Yu, Shiyi Lan, Jiahan Li, Jan Kautz, Tong Lu, and Jose M Alvarez · 2024
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Mgmap: Mask-guided learning for online vectorized hd map construction
Xiaolu Liu, Song Wang, Wentong Li, Ruizi Yang, Junbo Chen, and Jianke Zhu · 2024
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Alexander Popov, Alperen Degirmenci, David Wehr, Shashank Hegde, Ryan Oldja, Alexey Kamenev, Bertrand Douillard, David Nistér, Urs Muller, Ruchi Bhargava, Stan Birchfield, and Nikolai Smolyanskiy · 2024
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Key factors determining the required number of training images in person re-identification
Taku Sasaki, Adam S Walmsley, Kazuki Adachi, Shohei Enomoto, and Shin’ya Yamaguchi · 2024
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Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving
Xiaoyu Tian, Tao Jiang, Longfei Yun, Yucheng Mao, Huitong Yang, Yue Wang, Yilun Wang, and Hang Zhao · 2024
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Para-drive: Parallelized architecture for real-time autonomous driving
Xinshuo Weng, Boris Ivanovic, Yan Wang, Yue Wang, and Marco Pavone · 2024
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StreamMapNet: Streaming mapping network for vectorized online HD map construction
Tianyuan Yuan, Yicheng Liu, Yue Wang, Yilun Wang, and Hang Zhao · 2024
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Solving motion planning tasks with a scalable generative model
Yihan Hu, Siqi Chai, Zhening Yang, Jingyu Qian, Kun Li, Wenxin Shao, Haichao Zhang, Wei Xu, and Qiang Liu · 2025
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