6 papers
Post-Hoc Robustness for Model-Based Reinforcement Learning
Siemen Herremans, Ali Anwar, Siegfried Mercelis
To improve the real-world applicability of reinforcement learning (RL), the field of adversarially robust RL studies how to train agents under adversarial environment perturbations…
ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research
Bavo Lesy, Siemen Herremans, Robin Kerstens +4
The transport industry has recently shown significant interest in unmanned surface vehicles (USVs), specifically for port and inland waterway transport. These systems can improve o…
LiDAR-BIND-T: Improved and Temporally Consistent Sensor Modality Translation and Fusion for Robotic Applications
Niels Balemans, Ali Anwar, Jan Steckel +1
This paper extends LiDAR-BIND, a modular multi-modal fusion framework that binds heterogeneous sensors (radar, sonar) to a LiDAR-defined latent space, with mechanisms that explicit…
A comprehensive review of datasets and deep learning techniques for vision in Unmanned Surface Vehicles
Linh Trinh, Siegfried Mercelis, Ali Anwar
Unmanned Surface Vehicles (USVs) have emerged as a major platform in maritime operations, capable of supporting a wide range of applications. USVs can help reduce labor costs, incr…
Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping
Bavo Lesy, Ali Anwar, Siegfried Mercelis
Recently, there has been growing interest in autonomous shipping due to its potential to improve maritime efficiency and safety. The use of advanced technologies, such as artificia…
Data selection method for assessment of autonomous vehicles
Linh Trinh, Ali Anwar, Siegfried Mercelis
As the popularity of autonomous vehicles has grown, many standards and regulators, such as ISO, NHTSA, and Euro NCAP, require safety validation to ensure a sufficient level of safe…