TY - JOUR
T1 - Machine Learning/Artificial Intelligence for Sensor Data Fusion-Opportunities and Challenges
AU - Blasch, Erik
AU - Pham, Tien
AU - Chong, Chee Yee
AU - Koch, Wolfgang
AU - Leung, Henry
AU - Braines, Dave
AU - Abdelzaher, Tarek
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2021/7/1
Y1 - 2021/7/1
N2 - 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.
AB - 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.
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U2 - 10.1109/MAES.2020.3049030
DO - 10.1109/MAES.2020.3049030
M3 - Article
AN - SCOPUS:85112138326
SN - 0885-8985
VL - 36
SP - 80
EP - 93
JO - IEEE Aerospace and Electronic Systems Magazine
JF - IEEE Aerospace and Electronic Systems Magazine
IS - 7
M1 - 9475913
ER -