Wenjie Luo
Wenjie is a senior research scientist, founding member of the Uber ATG R&D team. His research interests include computer vision and machine learning, and his work spans the full autonomy stack including perception, prediction and planning. Previously, he did master in TTI-Chicago and continued to the PhD program in University of Toronto, both under Prof. Raquel Urtasun. He also spent some time at Apple SPG prior to join Uber.
Research Papers
End-to-end Interpretable Neural Motion Planner
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, R. Urtasun
In this paper, we propose a neural motion planner for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and an HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
In this paper, we propose a neural motion planner for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and an HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
IntentNet: Learning to Predict Intention from Raw Sensor Data
S. Casas, W. Luo, R. Urtasun
In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants. We define intent as a combination of discrete high level behaviors as well as continuous trajectories describing future motion. In this paper we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. [...] [PDF]
Conference on Robot Learning (CORL), 2018
In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants. We define intent as a combination of discrete high level behaviors as well as continuous trajectories describing future motion. In this paper we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. [...] [PDF]
Conference on Robot Learning (CORL), 2018
Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds
C. Zhang, W. Luo, R. Urtasun
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. [...] [PDF]
International Conference on 3D Vision (3DV), 2018
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. [...] [PDF]
International Conference on 3D Vision (3DV), 2018
Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
W. Luo, B. Yang, R. Urtasun
In this paper we propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. By jointly reasoning about these tasks, our holistic approach is more robust to occlusion as well as sparse data at range. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018
In this paper we propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. By jointly reasoning about these tasks, our holistic approach is more robust to occlusion as well as sparse data at range. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018
PIXOR: Real-time 3D Object Detection from Point Clouds
B. Yang, W. Luo, R. Urtasun
We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018
We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018
TorontoCity: Seeing the World With a Million Eyes
S. Wang; M. Bai; G. Mattyus; H. Chu; W. Luo; B. Yang; J. Liang; J. Cheverie; R. Urtasun; D. Lin.
Despite the substantial progress in recent years, the image captioning techniques are still far from being perfect. Sentences produced by existing methods, e.g. those based on RNNs, are often overly rigid and lacking in variability. [...] [PDF]
International Conference on Computer Vision (ICCV), 2017
Despite the substantial progress in recent years, the image captioning techniques are still far from being perfect. Sentences produced by existing methods, e.g. those based on RNNs, are often overly rigid and lacking in variability. [...] [PDF]
International Conference on Computer Vision (ICCV), 2017
