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ATG R&D Publications

Importance of Prior Knowledge in Precise Multimodal Prediction

Multi-Agent Routing Value Iteration Network

Hierarchical Verification for Adversarial Robustness

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

DSDNet: Deep Structured self-Driving Network

Dense RepPoints: Representing Visual Objects with Dense Point Sets

End-to-end Contextual Perception and Prediction with Interaction Transformer

Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction

Conditional Entropy Coding for Efficient Video Compression

Learning Lane Graph Representations for Motion Forecasting

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

Weakly-supervised 3D Shape Completion in the Wild

Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations

LevelSet R-CNN: A Deep Variational Method for Instance Segmentation

LidarSIM: Realistic LiDAR Simulation by Leveraging the Real World

Physically Realizable Adversarial Examples for LiDAR Detection

PolyTransform: Deep Polygon Transformer for Instance Segmentation

OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression

PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

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

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