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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

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)

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)

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