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In autonomous driving, end-to-end planners directly utilize raw sensor data, enabling them to extract richer scene features and reduce information loss compared to traditional planners.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Earlier work this paper cites.
Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
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Exploring the limitations of behavior cloning for autonomous driving
Felipe Codevilla, Eder Santana, Antonio M López, and Adrien Gaidon · 2019
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Video representation learning by dense predictive coding
Tengda Han, Weidi Xie, and Andrew Zisserman · 2019
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End-to-end interpretable neural motion planner
Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, and Raquel Urtasun · 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
Earlier work this paper cites.
Memory-augmented dense predictive coding for video representation learning
Tengda Han, Weidi Xie, and Andrew Zisserman · 2020
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Perceive, predict, and plan: Safe motion planning through interpretable semantic representations
Abbas Sadat, Sergio Casas, Mengye Ren, Xinyu Wu, Pranaab Dhawan, and Raquel Urtasun · 2020
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End-to-end model-free reinforcement learning for urban driving using implicit affordances
Marin Toromanoff, Emilie Wirbel, and Fabien Moutarde · 2020
Earlier work this paper cites.
Tracking objects as points
Xingyi Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2020
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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
Earlier work this paper cites.
Mp3: A unified model to map, perceive, predict and plan
Sergio Casas, Abbas Sadat, and Raquel Urtasun · 2021
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Safe local motion planning with self-supervised freespace forecasting
Peiyun Hu, Aaron Huang, John Dolan, David Held, and Deva Ramanan · 2021
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Bevdet: High-performance multi-camera 3d object detection in bird-eye-view
Junjie Huang, Guan Huang, Zheng Zhu, and Dalong Du · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Multi-modal fusion transformer for end-to-end autonomous driving
Aditya Prakash, Kashyap Chitta, and Andreas Geiger · 2021
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Immortal tracker: Tracklet never dies
Qitai Wang, Yuntao Chen, Ziqi Pang, Naiyan Wang, and Zhaoxiang Zhang · 2021
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Openscene: The largest up-to-date 3d occupancy prediction benchmark in autonomous driving, 2023
OpenScene Contributors · 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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Hidden biases of end-to-end driving models
Bernhard Jaeger, Kashyap Chitta, and Andreas Geiger · 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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ReasonNet: End-to-End Driving with Temporal and Global Reasoning
Hao Shao, Letian Wang, Ruobing Chen, Steven L Waslander, Hongsheng Li, and Yu Liu · 2023
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Policy pre-training for autonomous driving via self-supervised geometric modeling
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End-to-end urban driving by imitating a reinforcement learning coach
Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu, and Luc Van Gool · 2021
Cited alongside, same era.
Learning from all vehicles
Dian Chen and Philipp Krähenbühl · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Differentiable raycasting for self-supervised occupancy forecasting
Tarasha Khurana, Peiyun Hu, Achal Dave, Jason Ziglar, David Held, and Deva Ramanan · 2022
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Petr: Position embedding transformation for multi-view 3d object detection
Yingfei Liu, Tiancai Wang, Xiangyu Zhang, and Jian Sun · 2022
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Time will tell: New outlooks and a baseline for temporal multi-view 3d object detection
Jinhyung Park, Chenfeng Xu, Shijia Yang, Kurt Keutzer, Kris Kitani, Masayoshi Tomizuka, and Wei Zhan · 2022
Cited alongside, same era.
Plant: Explainable planning transformers via object-level representations
Katrin Renz, Kashyap Chitta, Otniel-Bogdan Mercea, A Koepke, Zeynep Akata, and Andreas Geiger · 2022
Cited alongside, same era.
Penghao Wu, Li Chen, Hongyang Li, Xiaosong Jia, Junchi Yan, and Yu Qiao · 2023
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Learning unsupervised world models for autonomous driving via discrete diffusion
Lunjun Zhang, Yuwen Xiong, Ze Yang, Sergio Casas, Rui Hu, and Raquel Urtasun · 2023
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Occworld: Learning a 3d occupancy world model for autonomous driving
Wenzhao Zheng, Weiliang Chen, Yuanhui Huang, Borui Zhang, Yueqi Duan, and Jiwen Lu · 2023
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Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking
Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, et al · 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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Driveworld: 4d pre-trained scene understanding via world models for autonomous driving
Chen Min, Dawei Zhao, Liang Xiao, Jian Zhao, Xinli Xu, Zheng Zhu, Lei Jin, Jianshu Li, Yulan Guo, Junliang Xing, et al · 2024
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Vlp: Vision language planning for autonomous driving
Chenbin Pan, Burhaneddin Yaman, Tommaso Nesti, Abhirup Mallik, Alessandro G Allievi, Senem Velipasalar, and Liu Ren · 2024
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Drivevlm: The convergence of autonomous driving and large vision-language models
Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Yang Wang, Zhiyong Zhao, Kun Zhan, Peng Jia, Xianpeng Lang, 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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