1 citations · 1 across the 7 of their papers we have counts for
7 papers
Guided Model-based LiDAR Super-Resolution for Resource-Efficient Automotive scene Segmentation
Alexandros Gkillas, Nikos Piperigkos, Aris S. Lalos
High-resolution LiDAR data plays a critical role in 3D semantic segmentation for autonomous driving, but the high cost of advanced sensors limits large-scale deployment. In contras…
Integrated Simulation Framework for Adversarial Attacks on Autonomous Vehicles
Christos Anagnostopoulos, Ioulia Kapsali, Alexandros Gkillas +2
Autonomous vehicles (AVs) rely on complex perception and communication systems, making them vulnerable to adversarial attacks that can compromise safety. While simulation offers a…
Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing
Maria Damanaki, Nikos Piperigkos, Alexandros Gkillas +1
Multi-Object Tracking (MOT) plays a crucial role in autonomous driving systems, as it lays the foundations for advanced perception and precise path planning modules. Nonetheless, s…
Personalized Federated Learning for Cross-view Geo-localization
Christos Anagnostopoulos, Alexandros Gkillas, Nikos Piperigkos +1
In this paper we propose a methodology combining Federated Learning (FL) with Cross-view Image Geo-localization (CVGL) techniques. We address the challenges of data privacy and het…
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis
Alexandros Gkillas, Aris Lalos
Anomaly and missing data constitute a thorny problem in industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, howev…
Federated Data-Driven Kalman Filtering for State Estimation
Nikos Piperigkos, Alexandros Gkillas, Christos Anagnostopoulos +1
This paper proposes a novel localization framework based on collaborative training or federated learning paradigm, for highly accurate localization of autonomous vehicles. More spe…