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Mapping

Recent ATG R&D publications

DAGMapper: Learning to Map by Discovering Lane Topology

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Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

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Convolutional Recurrent Network for Road Boundary Extraction

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Learning to Localize Through Compressed Binary Maps

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Deep Multi-Sensor Lane Detection

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

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End-to-End Deep Structured Models for Drawing Crosswalks

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Matching Adversarial Networks

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Hierarchical Recurrent Attention Networks for Structured Online Maps

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DeepRoadMapper: Extracting Road Topology From Aerial Images

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R&D Homepage

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