3 citations · 5 across the 5 of their papers we have counts for
5 papers
Deep Occupancy-Predictive Representations for Autonomous Driving
Eivind Meyer, Lars Frederik Peiss, Matthias Althoff
Manually specifying features that capture the diversity in traffic environments is impractical. Consequently, learning-based agents cannot realize their full potential as neural mo…
Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric
Eivind Meyer, Maurice Brenner, Bowen Zhang +3
Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants an…
Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning
Thomas Nakken Larsen, Amalie Heiberg, Eivind Meyer +3
Autonomous systems are becoming ubiquitous and gaining momentum within the marine sector. Since the electrification of transport is happening simultaneously, autonomous marine vess…
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning
Eivind Meyer, Amalie Heiberg, Adil Rasheed +1
Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they h…
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
Eivind Meyer, Haakon Robinson, Adil Rasheed +1
In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the…