Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Convoying or platooning with a fleet of autonomous vehicles, which is denoted as
autonomous convoying in this paper, has attracted increasing attention from the research …

Among the various options to estimate uncertainty in deep neural networks, Monte-Carlo
dropout is widely popular for its simplicity and effectiveness. However the quality of the …

In autonomous navigation, sensors suffer from massive occlusion in cluttered environments,
leaving a significant amount of space unknown. In practice, treating the unknown space in …

Deep learning has recently gained high interest in ophthalmology due to its ability to detect
clinically significant features for diagnosis and prognosis. Despite these significant …

This roadmap summarises a six-year multidisciplinary research project called Robots and
the Future of Welfare Services (ROSE), funded by the Strategic Research Council (SRC) …

Modern intelligent and autonomous robotic applications often require robots to have more
information about their environment than that provided by traditional occupancy grid maps …

To perform high speed tasks, sensors of autonomous cars have to provide as much
information in as few time steps as possible. However, radars, one of the sensor modalities …

An environment representation (ER) is a substantial part of every autonomous system. It
introduces a common interface between perception and other system components, such as …

In this paper, we develop and evaluate a Convolutional Neural Network (CNN)-based Light
Detection and Ranging (LiDAR) localization algorithm that includes uncertainty …