7 citations · 17 across the 16 of their papers we have counts for
16 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…
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…
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…
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…
Improving classification of road surface conditions via road area extraction and contrastive learning
Linh Trinh, Ali Anwar, Siegfried Mercelis
Maintaining roads is crucial to economic growth and citizen well-being because roads are a vital means of transportation. In various countries, the inspection of road surfaces is s…
Multiple data sources and domain generalization learning method for road surface defect classification
Linh Trinh, Ali Anwar, Siegfried Mercelis
Roads are an essential mode of transportation, and maintaining them is critical to economic growth and citizen well-being. With the continued advancement of AI, road surface inspec…
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…
Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary Model
Siemen Herremans, Ali Anwar, Siegfried Mercelis
Reinforcement learning has demonstrated impressive performance in various challenging problems such as robotics, board games, and classical arcade games. However, its real-world ap…
SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles
Linh Trinh, Ali Anwar, Siegfried Mercelis
Recently, there has been an upsurge in the research on maritime vision, where a lot of works are influenced by the application of computer vision for Unmanned Surface Vehicles (USV…
Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning
Siemen Herremans, Ali Anwar, Arne Troch +4
Autonomous shipping has recently gained much interest in the research community. However, little research focuses on inland - and port navigation, even though this is identified by…
Safety Aware Autonomous Path Planning Using Model Predictive Reinforcement Learning for Inland Waterways
Astrid Vanneste, Simon Vanneste, Olivier Vasseur +7
In recent years, interest in autonomous shipping in urban waterways has increased significantly due to the trend of keeping cars and trucks out of city centers. Classical approache…
Attention Based Feature Fusion For Multi-Agent Collaborative Perception
Ahmed N. Ahmed, Siegfried Mercelis, Ali Anwar
In the domain of intelligent transportation systems (ITS), collaborative perception has emerged as a promising approach to overcome the limitations of individual perception by enab…
The Second Monocular Depth Estimation Challenge
Jaime Spencer, C. Stella Qian, Michaela Trescakova +40
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, includin…
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient
Sixing Yu, Phuong Nguyen, Ali Anwar +1
Federated Learning (FL) has emerged as a new paradigm for training machine learning models distributively without sacrificing data security and privacy. Learning models on edge dev…
Learning to Communicate Using Counterfactual Reasoning
Simon Vanneste, Astrid Vanneste, Kevin Mets +5
Learning to communicate in order to share state information is an active problem in the area of multi-agent reinforcement learning (MARL). The credit assignment problem, the non-st…