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ATG R&D Publications

Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles

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Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

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Identifying Unknown Instances for Autonomous Driving

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DSIC: Deep Stereo Image Compression

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Learning Joint 2D-3D Representations for Depth Completion

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DAGMapper: Learning to Map by Discovering Lane Topology

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DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch

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End-to-End Interpretable Neural Motion Planner

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UPSNet: A Unified Panoptic Segmentation Network

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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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Multi-Task Multi-Sensor Fusion for 3D Object Detection

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Deep Rigid Instance Scene Flow

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DeepSignals: Predicting Intent of Drivers Through Visual Signals

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Graph HyperNetworks for Neural Architecture Search

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Deep Multi-Sensor Lane Detection

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HDNET: Exploiting HD Maps for 3D Object Detection

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IntentNet: Learning to Predict Intention from Raw Sensor Data

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Learning to Localize Using a LiDAR Intensity Map

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

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Deep Continuous Fusion for Multi-Sensor 3D Object Detection

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End-to-End Deep Structured Models for Drawing Crosswalks

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Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net

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SBNet: Sparse Blocks Network for Fast Inference

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Deep Parametric Continuous Convolutional Neural Networks

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Hierarchical Recurrent Attention Networks for Structured Online Maps

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PIXOR: Real-time 3D Object Detection from Point Clouds

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Matching Adversarial Networks

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Learning to Reweight Examples for Robust Deep Learning

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End-to-End Learning of Multi-Sensor 3D Tracking by Detection

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DeepRoadMapper: Extracting Road Topology From Aerial Images

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