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The ability to reliably perceive the environmental states, particularly the existence of objects and their motion behavior, is crucial for autonomous driving.
A new approach to linear filtering and prediction problems
Rudolph Emil Kalman · 1960
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Determining optical flow
Berthold KP Horn and Brian G Schunck · 1981
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An iterative image registration technique with an application to stereo vision
Bruce D Lucas, Takeo Kanade, et al · 1981
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Using occupancy grids for mobile robot perception and navigation
Alberto Elfes · 1989
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Method for registration of 3-d shapes
Paul J Besl and Neil D McKay · 1992
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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A survey on motion prediction and risk assessment for intelligent vehicles
Stéphanie Lefèvre, Dizan Vasquez, and Christian Laugier · 2014
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
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Social lstm: Human trajectory prediction in crowded spaces
Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese · 2016
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R-fcn: Object detection via region-based fully convolutional networks
Jifeng Dai, Yi Li, Kaiming He, and Jian Sun · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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Multi-view 3d object detection network for autonomous driving
Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
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Desire: Distant future prediction in dynamic scenes with interacting agents
Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B Choy, Philip HS Torr, and Manmohan Chandraker · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Forecasting interactive dynamics of pedestrians with fictitious play
Wei-Chiu Ma, De-An Huang, Namhoon Lee, and Kris M Kitani · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Learning spatio-temporal representation with pseudo-3d residual networks
Zhaofan Qiu, Ting Yao, and Tao Mei · 2017
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Yolo9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Intentnet: Learning to predict intention from raw sensor data
Sergio Casas, Wenjie Luo, and Raquel Urtasun · 2018
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Convolutional social pooling for vehicle trajectory prediction
Nachiket Deo and Mohan M Trivedi · 2018
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Social gan: Socially acceptable trajectories with generative adversarial networks
Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi · 2018
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Dynamic occupancy grid prediction for urban autonomous driving: A deep learning approach with fully automatic labeling
Stefan Hoermann, Martin Bach, and Klaus Dietmayer · 2018
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
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Hplflownet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds
Xiuye Gu, Yijie Wang, Chongruo Wu, Yong Jae Lee, and Panqu Wang · 2019
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Pointpillars: Fast encoders for object detection from point clouds
Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom · 2019
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Multi-task multi-sensor fusion for 3d object detection
Ming Liang, Bin Yang, Yun Chen, Rui Hu, and Raquel Urtasun · 2019
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Tsm: Temporal shift module for efficient video understanding
Ji Lin, Chuang Gan, and Song Han · 2019
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Cited alongside, same era.
Joint 3d proposal generation and object detection from view aggregation
Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, and Steven L Waslander · 2018
Cited alongside, same era.
Cornernet: Detecting objects as paired keypoints
Hei Law and Jia Deng · 2018
Cited alongside, same era.
Deep continuous fusion for multi-sensor 3d object detection
Ming Liang, Bin Yang, Shenlong Wang, and Raquel Urtasun · 2018
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Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net
Wenjie Luo, Bin Yang, and Raquel Urtasun · 2018
Cited alongside, same era.
Frustum pointnets for 3d object detection from rgb-d data
Charles R Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J Guibas · 2018
Cited alongside, same era.
R2p2: A reparameterized pushforward policy for diverse, precise generative path forecasting
Nicholas Rhinehart, Kris M Kitani, and Paul Vernaza · 2018
Cited alongside, same era.
Flownet3d: Learning scene flow in 3d point clouds
Xingyu Liu, Charles R Qi, and Leonidas J Guibas · 2019
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Meteornet: Deep learning on dynamic 3d point cloud sequences
Xingyu Liu, Mengyuan Yan, and Jeannette Bohg · 2019
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Trafficpredict: Trajectory prediction for heterogeneous traffic-agents
Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, and Dinesh Manocha · 2019
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Multi-step prediction of occupancy grid maps with recurrent neural networks
Nima Mohajerin and Mohsen Rohani · 2019
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Deep hough voting for 3d object detection in point clouds
Charles R Qi, Or Litany, Kaiming He, and Leonidas J Guibas · 2019
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Sophie: An attentive gan for predicting paths compliant to social and physical constraints
Amir Sadeghian, Vineet Kosaraju, Ali Sadeghian, Noriaki Hirose, Hamid Rezatofighi, and Silvio Savarese · 2019
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Long-term occupancy grid prediction using recurrent neural networks
Marcel Schreiber, Stefan Hoermann, and Klaus Dietmayer · 2019
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Pointrcnn: 3d object proposal generation and detection from point cloud
Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li · 2019
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Video classification with channel-separated convolutional networks
Du Tran, Heng Wang, Lorenzo Torresani, and Matt Feiszli · 2019
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
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Point cloud processing via recurrent set encoding
Pengxiang Wu, Chao Chen, Jingru Yi, and Dimitris Metaxas · 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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Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction
Pu Zhang, Wanli Ouyang, Pengfei Zhang, Jianru Xue, and Nanning Zheng · 2019
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Multi-agent tensor fusion for contextual trajectory prediction
Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris Baker, Yibiao Zhao, Yizhou Wang, and Ying Nian Wu · 2019
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Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl · 2019
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3d point cloud processing and learning for autonomous driving
Siheng Chen, Baoan Liu, Chen Feng, Carlos Vallespi-Gonzalez, and Carl Wellington · 2020
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