4 citations · 6 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…