250 citations · 354 across the 13 of their papers we have counts for
16 papers
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
Sean Segal, Nishanth Kumar, Sergio Casas +4
Self-driving vehicles must perceive and predict the future positions of nearby actors in order to avoid collisions and drive safely. A learned deep learning module is often respons…
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…
MP3: A Unified Model to Map, Perceive, Predict and Plan
Sergio Casas, Abbas Sadat, Raquel Urtasun
High-definition maps (HD maps) are a key component of most modern self-driving systems due to their valuable semantic and geometric information. Unfortunately, building HD maps has…
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…
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
Simon Suo, Sebastian Regalado, Sergio Casas +1
Simulation has the potential to massively scale evaluation of self-driving systems enabling rapid development as well as safe deployment. To close the gap between simulation and th…
Diverse Complexity Measures for Dataset Curation in Self-driving
Abbas Sadat, Sean Segal, Sergio Casas +4
Modern self-driving autonomy systems heavily rely on deep learning. As a consequence, their performance is influenced significantly by the quality and richness of the training data…