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Found 5 papers, 2 papers with code
What is the Best Grid-Map for Self-Driving Cars Localization? An Evaluation under Diverse Types of Illumination, Traffic, and Environment
In this work, we provide data for such analysis by comparing the accuracy of a particle filter localization when using occupancy, reflectivity, color, or semantic grid maps.
Training Agents using Upside-Down Reinforcement Learning
Many of its general principles are outlined in a companion report; the goal of this paper is to develop a practical learning algorithm and show that this conceptually simple perspective on agent training can produce a range of rewarding behaviors for multiple episodic environments.
Self-Driving Cars: A Survey
In this survey, we present the typical architecture of the autonomy system of self-driving cars.
Robotics
Memory-like Map Decay for Autonomous Vehicles based on Grid Maps
The objective of map decay is to correct invalid occupancy probabilities of map cells that are unobservable by sensors.
Hindsight policy gradients
A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy.
