36 citations · 88 across the 5 of their papers we have counts for
6 papers
What is the Best Grid-Map for Self-Driving Cars Localization? An Evaluation under Diverse Types of Illumination, Traffic, and Environment
Filipe Mutz, Thiago Oliveira-Santos, Avelino Forechi +4
The localization of self-driving cars is needed for several tasks such as keeping maps updated, tracking objects, and planning. Localization algorithms often take advantage of maps…
Training Agents using Upside-Down Reinforcement Learning
Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz +2
We develop Upside-Down Reinforcement Learning (UDRL), a method for learning to act using only supervised learning techniques. Unlike traditional algorithms, UDRL does not use rewar…
Self-Driving Cars: A Survey
Claudine Badue, Rânik Guidolini, Raphael Vivacqua Carneiro +10
We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system tha…
Memory-like Map Decay for Autonomous Vehicles based on Grid Maps
Thomas Teixeira, Filipe Mutz, Karin Satie Komati +5
In this work, we present a novel strategy for correcting imperfections in occupancy grid maps called map decay. The objective of map decay is to correct invalid occupancy probabili…
Hindsight policy gradients
Paulo Rauber, Avinash Ummadisingu, Filipe Mutz +1
A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy. In addition to their potential to generalize desirable behav…
A Model-Predictive Motion Planner for the IARA Autonomous Car
Vinicius Cardoso, Josias Oliveira, Thomas Teixeira +5
We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a…