116 citations · 131 across the 10 of their papers we have counts for
11 papers
QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving
Sourav Biswas, Sergio Casas, Quinlan Sykora +3
A self-driving vehicle must understand its environment to determine the appropriate action. Traditional autonomy systems rely on object detection to find the agents in the scene. H…
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…
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…
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
John Phillips, Julieta Martinez, Ioan Andrei Bârsan +3
Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving, including perception, motion forecasting, and motion planning. However, these…
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Jingkang Wang, Ava Pun, James Tu +5
As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes…
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
Alexander Cui, Sergio Casas, Abbas Sadat +2
In this paper, we present LookOut, a novel autonomy system that perceives the environment, predicts a diverse set of futures of how the scene might unroll and estimates the traject…
Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs
Sean Segal, Eric Kee, Wenjie Luo +3
In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling effici…
Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
Kelvin Wong, Qiang Zhang, Ming Liang +4
We present a novel method for testing the safety of self-driving vehicles in simulation. We propose an alternative to sensor simulation, as sensor simulation is expensive and has l…
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
Abbas Sadat, Sergio Casas, Mengye Ren +3
In this paper we propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles and produces interpretable in…
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
Abbas Sadat, Mengye Ren, Andrei Pokrovsky +3
The motion planners used in self-driving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules. This is usually achieved by two modules: beh…