AHFE Open AccessEuropean University–Industry Collaboration for Civil Counter-Drone Protection: A Human-Centered, AI-Game-Based Socio-Technical Systems Approach
Abstract
This paper presents a comprehensive framework for civilian counter-drone (C-UAS) systems, combining AI-based detection, data fusion, and decision-support tools with human-in-the-loop operational concepts. The research emphasizes early-warning and socially acceptable mitigation strategies to protect critical infrastructure and public spaces while adhering to European legal and data protection standards. The project further incorporates gamified, simulation-based training and evaluation approaches to support iterative testing, decision-making under uncertainty, and competence development. In addition, it integrates stakeholder engagement, interdisciplinary student involvement, and European cooperation to ensure both practical applicability and long-term workforce development in the field of civil drone defense.
Keywords: Civil Drone Defense, Critical Infrastructure Protection (CIP), AI-based Drone Detection, Student-driven Applied Research
DOI: 10.54941/ahfe1007684
Cite this paper
More from this volume
- Workshop: Orchestrating Synthesized Human and AI-Agentic Workflows: AI Agency Benefits, Disruptions and Management
- The Risks, Challenges, and Potential Opportunities with GenAI
- Agentic LLMs for Scalable, Verifiable System Health Digital Twins
- When Workflows Stay Stable but Meaning Moves in Agentic Analyst Pipelines
- Effects of Swarm Size Variability on Operator Workload
- A Computer-Vision Approach to Accessible Robot Control: Hand Gesture Recognition for Users With Limited Mobility or Speech
- Emotive Design Heuristics: A Methodology for Creating and Validating Empathetic Design Heuristics for Human-Robot Interaction
- User Perception and Sentiment Analysis of Knee exoskeletons for Hiking Based on Social Media Comments: A Preliminary Study
- Effects of Robot Non-Verbal Behaviors on Human Emotion Recognition in Human–Robot Communication
- Rule-Based Interpretable AI for Concurrent Collision Detection in Industrial Robot Manipulators
- Human Factors in the Design of Human–Machine Interfaces for Counter-Drone Systems
- Human-Friendly Control of Drones and Drone Swarms Using Natural Language and AI-Based Task Decomposition