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Found 5 papers, 1 papers with code
UnO: Unsupervised Occupancy Fields for Perception and Forecasting
Perceiving the world and forecasting its future state is a critical task for self-driving.
QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving
Instead, we shift the paradigm to have the planner query occupancy at relevant spatio-temporal points, restricting the computation to those regions of interest.
Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving
A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants.
Multi-Agent Routing Value Iteration Network
In this paper we tackle the problem of routing multiple agents in a coordinated manner.
Learning of Coordination Policies for Robotic Swarms
Inspired by biological swarms, robotic swarms are envisioned to solve real-world problems that are difficult for individual agents.
