Skip to main content
Uber
Uber

ATG R&D Publications

Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles

Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

Identifying Unknown Instances for Autonomous Driving

DSIC: Deep Stereo Image Compression

Learning Joint 2D-3D Representations for Depth Completion

DAGMapper: Learning to Map by Discovering Lane Topology

DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch

End-to-End Interpretable Neural Motion Planner

UPSNet: A Unified Panoptic Segmentation Network

Convolutional Recurrent Network for Road Boundary Extraction

Learning to Localize Through Compressed Binary Maps

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

Deep Rigid Instance Scene Flow

DeepSignals: Predicting Intent of Drivers Through Visual Signals

Graph HyperNetworks for Neural Architecture Search

Deep Multi-Sensor Lane Detection

HDNET: Exploiting HD Maps for 3D Object Detection

IntentNet: Learning to Predict Intention from Raw Sensor Data

Learning to Localize Using a LiDAR Intensity Map

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

Deep Continuous Fusion for Multi-Sensor 3D Object Detection

End-to-End Deep Structured Models for Drawing Crosswalks

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

SBNet: Sparse Blocks Network for Fast Inference

Deep Parametric Continuous Convolutional Neural Networks

Hierarchical Recurrent Attention Networks for Structured Online Maps

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

Matching Adversarial Networks

Learning to Reweight Examples for Robust Deep Learning

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

DeepRoadMapper: Extracting Road Topology From Aerial Images

R&D Homepage