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  4. Machine Learning/Artificial Intelligence for Sensor Data Fusion - Opportunities and Challenges
 
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2021
Journal Article
Title

Machine Learning/Artificial Intelligence for Sensor Data Fusion - Opportunities and Challenges

Abstract
During Fusion 2019 Conference (https://www.fusion2019.org/program.html), leading experts presented ideas on the historical, contemporary, and future coordination of artificial intelligence/machine learning (AI/ML) with sensor data fusion (SDF). While AI/ML and SDF concepts have had a rich history since the early 1900s-emerging from philosophy and psychology-it was not until the dawn of computers that both AI/ML and SDF researchers initiated discussions on how mathematical techniques could be implemented for real-time analysis. ML, and in particular deep learning, has demonstrated tremendous success in computer vision, natural language understanding, and data analytics. As a result, ML has been proposed as the solution to many problems that inherently include multi-modal data. For example, success in autonomous vehicles has validated the promise of ML with SDF, but additional research is needed to explain, understand, and coordinate heterogeneous data analytics for situation awareness. The panel identified opportunities for merging AI/ML and SDF such as computational efficiency, improved decision making, expanding knowledge, and providing security; while highlighting challenges for multi-domain operations, human-machine teaming, and ethical deployment strategies.
Author(s)
Blasch, Erik
Pham, Tien
Chong, Chee-Yee
Koch, Wolfgang  
Leung, Henry
Braines, Dave
Abdelzaher, Tarek
Journal
IEEE aerospace and electronic systems magazine  
DOI
10.1109/MAES.2020.3049030
Language
English
Fraunhofer-Institut für Kommunikation, Informationsverarbeitung und Ergonomie FKIE  
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