Sergio Casas

Sergio Casas

Staff Researcher @ Waabi

Ph.D. Candidate @ UofT

About me

I am a Staff Researcher and TLM at Waabi, where I lead our Perception and Behavior Reasoning team. I am also a PhD candidate at the University of Toronto supervised by Professor Raquel Urtasun. My research lies at the intersection of computer vision, machine learning, and robotics.

Interests

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision
  • Robotics - Autonomous Driving
  • Generative Models
  • Imitation Learning

Education

  • PhD in Computer Science, 2020 - Present

    University of Toronto

  • MSc in Computer Science, 2018 - 2020

    University of Toronto

  • BSc in Computer Science, 2013 - 2017

    Universitat Politècnica de Catalunya

  • BSc in Industrial Tech. Engineering, 2012 - 2017

    Universitat Politècnica de Catalunya

Selected Publications

For a complete and up-to-date list of publications visit my Google Scholar

(* denotes equal contribution)

DeTra: A Unified Model for Object Detection and Trajectory Forecasting

ECCV 2024
Unified object detection and trajectory prediction as trajectory refinement.
Sergio Casas*, Ben Agro*, Jiageng Mao*, Thomas Gilles, Alexander Cui, Thomas Li, Raquel Urtasun
PDF Project
DeTra: A Unified Model for Object Detection and Trajectory Forecasting

UnO: Unsupervised Occupancy Fields for Perception and Forecasting

CVPR 2024 (Oral)
Occupancy Foundation Model (Unsupervised)
Ben Agro*, Quinlan Sykora*, Sergio Casas*, Thomas Gilles, Raquel Urtasun
PDF Project
UnO: Unsupervised Occupancy Fields for Perception and Forecasting

QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving

ICRA 2024
End-to-end autonomy leveraging implicit occupancy.
Sourav Biswas*, Sergio Casas*, Quinlan Sykora, Ben Agro, Abbas Sadat, Raquel Urtasun
PDF Project
QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving

Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion

ICLR 2024
LiDAR World Model.
Lunjun Zhang, Yuwen Xiong, Ze Yang, Sergio Casas, Rui Hu, Raquel Urtasun
PDF Project
Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion

ImplicitO: Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving

CVPR 2023 (Highlight)
Efficient implicit occupancy perception and forecasting model.
Ben Agro*, Quinlan Sykora*, Sergio Casas*, Raquel Urtasun
PDF Project
ImplicitO: Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving

MP3: A Unified Model to Map, Perceive, Predict and Plan

CVPR 2021 (Best Paper Candidate, Oral)
Interpretable end-to-end neural motion planning without high-definition maps
Sergio Casas*, Abbas Sadat*, Raquel Urtasun
PDF Poster Video
MP3: A Unified Model to Map, Perceive, Predict and Plan

LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving

ICCV 2021
Contingency planning from diverse joint trajectory samples for all actors in the scene
Alexander Cui*, Abbas Sadat*, Sergio Casas*, Renjie Liao, Raquel Urtasun
PDF Video
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving

TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors

CVPR 2021
Realistic long-term vehicle behavior simulation learned from imitation and common sense
Simon Suo, Sebastian Regalado, Sergio Casas, Raquel Urtasun
PDF Video
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

ECCV 2020
ILVM characterizes the joint distribution over multiple actors’ future trajectories
Sergio Casas*, Cole Gulino*, Simon Suo*, Katie Luo, Renjie Liao, Raquel Urtasun
PDF Video
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

End-to-end Interpretable Neural Motion Planner

CVPR 2019 (Oral)
Neural motion planner from LiDAR and HD maps
Wenyuan Zeng*, Wenjie Luo*, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, Raquel Urtasun
PDF Video
End-to-end Interpretable Neural Motion Planner

Intentnet: Learning to Predict Intention from Raw Sensor Data

CoRL 2018 (Spotlight)
Joint perception and prediction from LiDAR point clouds and HD maps
Sergio Casas, Wenjie Luo, Raquel Urtasun
PDF Video
Intentnet: Learning to Predict Intention from Raw Sensor Data

Experience

 
 
 
 
 

Staff Researcher

Waabi

Mar 2021 – Present Toronto, Canada
We are building the next generation of self-driving technology. Stay tuned!
 
 
 
 
 

Research Scientist

Uber Advanced Technologies Group (acquired by Aurora)

Oct 2017 – Feb 2021 Toronto, Canada
Research in Autonomous Driving: Perception, Prediction and Motion Planning systems.
 
 
 
 
 

Research Assistant

University of Toronto

Feb 2017 – Jul 2017 Toronto, Canada
Research in spatio-temporal reasoning for sports analytics. Worked on automatizing the NBA Play-by-Play reports. Supervised by Prof. Urtasun.
 
 
 
 
 

Data Analytics Consultant

Arcvi Big Data Agency

Jun 2016 – Jan 2017 Barcelona, Spain
Creation of strategy solutions using simple Machine Learning techniques. Advised multiple retail, insurance and credit companies.
 
 
 
 
 

Software Engineering Intern

Psycle Interactive Ltd.

Jun 2015 – Aug 2015 Whitchurch, United Kingdom
Mobile application development and UI/UX design. Research project on document topic classification and information retrieval.