Raquel Urtasun
Latest
MAD: Memory-Augmented Detection of 3D Objects
DIO: Decomposable Implicit 4D Occupancy-Flow World Model
DeTra: A Unified Model for Object Detection and Trajectory Forecasting
UnO: Unsupervised Occupancy Fields for Perception and Forecasting
QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving
Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
ImplicitO: Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving
MP3: A Unified Model to Map, Perceive, Predict and Plan
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
Diverse Complexity Measures for Dataset Curation in Self-driving
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
Strobe: Streaming Object Detection from LiDAR Packets
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects
The Importance of Prior Knowledge in Precise Multimodal Prediction
PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
End-to-end Interpretable Neural Motion Planner
Intentnet: Learning to Predict Intention from Raw Sensor Data
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