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Mapping

Our AI-based methods automate and assist human-in-the-loop HD map production processes through machine learning and computer vision applied to offline map generation pipelines.

Recent ATG R&D publications

DAGMapper: Learning to Map by Discovering Lane Topology

Namdar Homayounfar, Wei-Chiu Ma*, Justin Liang*, Xinyu Wu, Jack Fan, Raquel Urtasun (ICCV 2019)

Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

Wei-Chiu Ma*, Ignacio Tartavull*, Ioan Andrei Barsan*, Shenlong Wang*, Min Bai, Gellert Mattyus, Namdar Homayounfar, Shrinidhi Kowshika Lakshmikanth, Andrei Pokrovsky, Raquel Urtasun (IROS 2019)

Convolutional Recurrent Network for Road Boundary Extraction

Justin Liang*, Namdar Homayounfar*, Wei-Chiu Ma, Shenlong Wang, Raquel Urtasun (CVPR 2019)

Learning to Localize Through Compressed Binary Maps

Xinkai Wei*, Ioan Andrei Bârsan*, Shenlong Wang*, Julieta Martinez, Raquel Urtasun (CVPR 2019)

Deep Multi-Sensor Lane Detection

Min Bai*, Gellért Máttyus*, Namdar Homayounfar, Shenlong Wang, Shrinidhi Kowshika Lakshmikanth, Raquel Urtasun (IROS 2018, oral)

Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds

Chris Zhang, Wenjie Luo, Raquel Urtasun (3DV 2018, spotlight)

End-to-End Deep Structured Models for Drawing Crosswalks

Justin Liang, Raquel Urtasun (ECCV 2018)

Matching Adversarial Networks

Gellért Máttyus, Raquel Urtasun (CVPR 2018)

Hierarchical Recurrent Attention Networks for Structured Online Maps

Namdar Homayounfar, Wei-Chiu Ma, Shrinidhi Kowshika Lakshmikanth, Raquel Urtasun (CVPR 2018)

DeepRoadMapper: Extracting Road Topology From Aerial Images

Gellért Máttyus, Wenjie Luo, Raquel Urtasun (ICCV 2017)

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