Sergio Casas
Sergio Casas
PhD Student at University of Toronto, Research Scientist at Uber ATG
在 cs.toronto.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Intentnet: Learning to Predict Intention from Raw Sensor Data
S Casas, W Luo, R Urtasun
CoRL 18, 2018
1342018
End-to-End Interpretable Neural Motion Planner
W Zeng, W Luo, S Suo, A Sadat, B Yang, S Casas, R Urtasun
CVPR 19, 2019
912019
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
322020
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
302020
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
S Casas, C Gulino, S Suo, K Luo, R Liao, R Urtasun
ECCV 20, 2020
172020
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
132020
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects
B Yang, R Guo, M Liang, S Casas, R Urtasun
ECCV 20, 2020
112020
The Importance of Prior Knowledge in Precise Multimodal Prediction
S Casas, C Gulino, S Suo, R Urtasun
IROS 20, 2020
92020
StrObe: Streaming Object Detection from LiDAR Packets
D Frossard, S Suo, S Casas, J Tu, R Hu, R Urtasun
CoRL 20, 2020
42020
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
32019
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
12021
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
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