Perception and prediction

ATG’s work perceives and understands the dynamic world around a self-driving car through a multi-sensor setup and uses this information to predict possible future states of the world.

Recent ATG R&D publications

DSDNet: Deep Structured self-Driving Network

Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun (ECCV 2020)

Importance of Prior Knowledge in Precise Multimodal Prediction

Sergio Casas*, Cole Gulino*, Simon Suo*, and Raquel Urtasun (IROS 2020, oral)

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

Sergio Casas, Cole Gulino, Simon Suo, Katie Luo, Renjie Liao, Raquel Urtasun (ECCV 2020)

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

Lingyun Luke Li, Bin Yang, Ming Liang, Wenyuan Zeng, Mengye Ren, Sean Segal, Raquel Urtasun (ECCV 2020, oral)

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

Kelvin Wong, Qiang Zhang, Ming Liang, Bin Yang, Renjie Liao, Abbas Sadat, Raquel Urtasun (ECCV 2020)

Learning Lane Graph Representations for Motion Forecasting

Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun (ECCV 2020, oral)

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

Bin Yang*, Runsheng Guo*, Ming Liang, Sergio Casas, Raquel Urtasun (ECCV 2020)

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

Abbas Sadat*, Sergio Casas*, Mengye Ren, Xinyu Wu, Pranaab Dhawan, Raquel Urtasun (ECCV 2020)

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

Namdar Homayounfar, Yuwen Xiong, Justin Liang, Wei-Chiu Ma, Raquel Urtasun (ECCV 2020)

Physically Realizable Adversarial Examples for LiDAR Detection

James Tu, Mengye Ren, Siva Manivasagam, Bin Yang, Ming Liang, Richard Du, Frank Cheng, Raquel Urtasun (CVPR 2020)

PolyTransform: Deep Polygon Transformer for Instance Segmentation

Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Yuwen Xiong, Rui Hu, Raquel Urtasun (CVPR 2020)

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

Ming Liang*, Bin Yang*, Wenyuan Zeng, Yun Chen, Rui Hu, Sergio Casas, Raquel Urtasun (CVPR 2020)

阅读详细信息

Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction

Ajay Jain*, Sergio Casas*, Renjie Liao*, Yuwen Xiong*, Song Feng, Sean Segal, Raquel Urtasun (CoRL 2019)

Identifying Unknown Instances for Autonomous Driving

Kelvin Wong, Shenlong Wang, Mengye Ren, Ming Liang, Raquel Urtasun (CoRL 2019)

DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch

Shivam Duggal, Shenlong Wang, Wei-Chiu Ma, Rui Hu, Raquel Urtasun (ICCV 2019)

Learning Joint 2D-3D Representations for Depth Completion

Yun Chen*, Bin Yang*, Ming Liang, Raquel Urtasun (ICCV 2019)

End-to-End Interpretable Neural Motion Planner

Wenyuan Zeng*, Wenjie Luo*, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, Raquel Urtasun (CVPR 2019, oral)

UPSNet: A Unified Panoptic Segmentation Network

Yuwen Xiong*, Renjie Liao*, Hengshuang Zhao*, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun (CVPR 2019, oral)

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

Ming Liang*, Bin Yang*, Yun Chen, Rui Hu, Raquel Urtasun (CVPR 2019)

Deep Rigid Instance Scene Flow

Wei-Chiu Ma, Shenlong Wang, Rui Hu, Yuwen Xiong, Raquel Urtasun (CVPR 2019)

DeepSignals: Predicting Intent of Drivers Through Visual Signals

Davi Frossard, Eric Kee, 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)

HDNET: Exploiting HD Maps for 3D Object Detection

Bin Yang, Ming Liang, Raquel Urtasun (CORL 2018, spotlight)

IntentNet: Learning to Predict Intention from Raw Sensor Data

Sergio Casas, Wenjie Luo, Raquel Urtasun (CORL 2018, spotlight)

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

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

Deep Continuous Fusion for Multi-Sensor 3D Object Detection

Ming Liang, Bin Yang, Shenlong Wang, Raquel Urtasun (ECCV 2018)

SBNet: Sparse Blocks Network for Fast Inference

Mengye Ren*, Andrei Pokrovsky*, Bin Yang*, Raquel Urtasun (CVPR 2018, spotlight)

Deep Parametric Continuous Convolutional Neural Networks

Shenlong Wang*, Simon Suo*, Wei-Chiu Ma, Andrei Pokrovsky, Raquel Urtasun (CVPR 2018, spotlight)

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

Bin Yang, Wenjie Luo, Raquel Urtasun (CVPR 2018)

Matching Adversarial Networks

Gellért Máttyus, Raquel Urtasun (CVPR 2018)

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

Wenjie Luo, Bin Yang, Raquel Urtasun (CVPR 2018, oral)

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

Davi Frossard, Raquel Urtasun (ICRA 2018)

R&D Homepage

Back to Homepage

选择首选语言

English 简体中文 Español (Internacional)

Perception and prediction

Recent ATG R&D publications

DSDNet: Deep Structured self-Driving Network

Importance of Prior Knowledge in Precise Multimodal Prediction

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

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

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

Learning Lane Graph Representations for Motion Forecasting

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

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

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

Physically Realizable Adversarial Examples for LiDAR Detection

PolyTransform: Deep Polygon Transformer for Instance Segmentation

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

Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction

Identifying Unknown Instances for Autonomous Driving

DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch

Learning Joint 2D-3D Representations for Depth Completion

End-to-End Interpretable Neural Motion Planner

UPSNet: A Unified Panoptic Segmentation Network

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

Deep Rigid Instance Scene Flow

DeepSignals: Predicting Intent of Drivers Through Visual Signals

Deep Multi-Sensor Lane Detection

HDNET: Exploiting HD Maps for 3D Object Detection

IntentNet: Learning to Predict Intention from Raw Sensor Data

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

Deep Continuous Fusion for Multi-Sensor 3D Object Detection

SBNet: Sparse Blocks Network for Fast Inference

Deep Parametric Continuous Convolutional Neural Networks

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

Matching Adversarial Networks

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

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

R&D Homepage

公司

注册以开车接单/提供派送服务

创建乘客账号

Uber Eats 优食派送服务

登录以开车接单/提供派送服务

登录以乘车

登录以通过 Uber Eats 优食预约派送服务