806 citations · 1.5k across the 12 of their papers we have counts for
12 papers
JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving
Wenjie Luo, Cheolho Park, Andre Cornman +2
We propose JFP, a Joint Future Prediction model that can learn to generate accurate and consistent multi-agent future trajectories. For this task, many different methods have been…
Joint Forecasting of Panoptic Segmentations with Difference Attention
Colin Graber, Cyril Jazra, Wenjie Luo +2
Forecasting of a representation is important for safe and effective autonomy. For this, panoptic segmentations have been studied as a compelling representation in recent work. Howe…
PhyAug: Physics-Directed Data Augmentation for Deep Sensing Model Transfer in Cyber-Physical Systems
Wenjie Luo, Zhenyu Yan, Qun Song +1
Run-time domain shifts from training-phase domains are common in sensing systems designed with deep learning. The shifts can be caused by sensor characteristic variations and/or di…
IntentNet: Learning to Predict Intention from Raw Sensor Data
Sergio Casas, Wenjie Luo, Raquel 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 behavi…
End-to-end Interpretable Neural Motion Planner
Wenyuan Zeng, Wenjie Luo, Simon Suo +4
In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interaction…
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
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 joint…
Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs
Sean Segal, Eric Kee, Wenjie Luo +3
In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling effici…
PIXOR: Real-time 3D Object Detection from Point Clouds
Bin Yang, Wenjie Luo, Raquel Urtasun
We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component…
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Wenjie Luo, Yujia Li, Raquel Urtasun +1
We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond t…
TorontoCity: Seeing the World with a Million Eyes
Shenlong Wang, Min Bai, Gellert Mattyus +7
In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712.5 of land, 8439 of road and around 400,000 buildings. Ou…
Efficient Summarization with Read-Again and Copy Mechanism
Wenyuan Zeng, Wenjie Luo, Sanja Fidler +1
Encoder-decoder models have been widely used to solve sequence to sequence prediction tasks. However current approaches suffer from two shortcomings. First, the encoders compute a…
Exploiting Semantic Information and Deep Matching for Optical Flow
Min Bai, Wenjie Luo, Kaustav Kundu +1
We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving. We build on the observation that the scene is typically composed of a…