| Intentnet: Learning to Predict Intention from Raw Sensor Data S Casas, W Luo, R Urtasun CoRL 18, 2018 | 134 | 2018 |
| End-to-End Interpretable Neural Motion Planner W Zeng, W Luo, S Suo, A Sadat, B Yang, S Casas, R Urtasun CVPR 19, 2019 | 91 | 2019 |
| SpAGNN: Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data S Casas, C Gulino, R Liao, R Urtasun ICRA 20, 2020 | 43* | 2020 |
| PnPNet: End-to-End Perception and Prediction with Tracking in the Loop M Liang, B Yang, W Zeng, Y Chen, R Hu, S Casas, R Urtasun CVPR 20, 2020 | 32 | 2020 |
| Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction A Jain, S Casas, R Liao, Y Xiong, S Feng, S Segal, R Urtasun CoRL 19, 2020 | 30 | 2020 |
| Implicit Latent Variable Model for Scene-Consistent Motion Forecasting S Casas, C Gulino, S Suo, K Luo, R Liao, R Urtasun ECCV 20, 2020 | 17 | 2020 |
| Perceive, Predict, and Plan: Safe Motion Planning through Interpretable Semantic Representations A Sadat, S Casas, M Ren, X Wu, P Dhawan, R Urtasun ECCV 20, 2020 | 13 | 2020 |
| RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects B Yang, R Guo, M Liang, S Casas, R Urtasun ECCV 20, 2020 | 11 | 2020 |
| The Importance of Prior Knowledge in Precise Multimodal Prediction S Casas, C Gulino, S Suo, R Urtasun IROS 20, 2020 | 9 | 2020 |
| StrObe: Streaming Object Detection from LiDAR Packets D Frossard, S Suo, S Casas, J Tu, R Hu, R Urtasun CoRL 20, 2020 | 4 | 2020 |
| Multi-Task Machine-Learned Models for Object Intention Determination in Autonomous Driving S Casas, W Luo, R Urtasun US Patent App. 16/420,686, 2019 | 3 | 2019 |
| Diverse Complexity Measures for Dataset Curation in Self-driving A Sadat, S Segal, S Casas, J Tu, B Yang, R Urtasun, E Yumer arXiv preprint arXiv:2101.06554, 2021 | 1 | 2021 |
| Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes S Segal, N Kumar, S Casas, W Zeng, M Ren, J Wang, R Urtasun arXiv preprint arXiv:2104.03956, 2021 | | 2021 |
| MP3: A Unified Model to Map, Perceive, Predict and Plan S Casas, A Sadat, R Urtasun arXiv preprint arXiv:2101.06806, 2021 | | 2021 |
| TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors S Suo, S Regalado, S Casas, R Urtasun arXiv preprint arXiv:2101.06557, 2021 | | 2021 |
| Deep Multi-Task Learning for Joint Localization, Perception, and Prediction J Phillips, J Martinez, IA Bârsan, S Casas, A Sadat, R Urtasun arXiv preprint arXiv:2101.06720, 2021 | | 2021 |
| AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles J Wang, A Pun, J Tu, S Manivasagam, A Sadat, S Casas, M Ren, ... arXiv preprint arXiv:2101.06549, 2021 | | 2021 |
| LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving A Cui, A Sadat, S Casas, R Liao, R Urtasun arXiv preprint arXiv:2101.06547, 2021 | | 2021 |
| Systems and Methods for Generating Motion Forecast Data for Actors with Respect to an Autonomous Vehicle and Training a Machine Learned Model for the Same R Urtasun, R Liao, S Casas, CC Gulino US Patent App. 16/816,671, 2021 | | 2021 |
| Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting K Luo, S Casas, R Liao, X Yan, Y Xiong, W Zeng, R Urtasun arXiv preprint arXiv:2101.02385, 2021 | | 2021 |
Raquel UrtasunProfessor, University of Toronto. Chief Scientist, Uber ATG在 cs.toronto.edu 的电子邮件经过验证
