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Machine Learning/Artificial Intelligence for Sensor Data Fusion–Opportunities and Challenges
IEEE Aerospace and Electronic Systems Magazine ( IF 3.6 ) Pub Date : 2021-07-07 , DOI: 10.1109/maes.2020.3049030
Erik Blasch 1 , Tien Pham 2 , Chee-Yee Chong 3 , Wolfgang Koch 4 , Henry Leung 5 , Dave Braines 6 , Tarek Abdelzaher 7
Affiliation  

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.

中文翻译:

用于传感器数据融合的机器学习/人工智能——机遇与挑战

在 Fusion 2019 会议 (https://www.fusion2019.org/program.html) 期间,领先专家就人工智能/机器学习 (AI/ML) 与传感器数据融合 (SDF) 的历史、当代和未来协调提出了想法)。虽然 AI/ML 和 SDF 概念自 1900 年代初期以来有着悠久的历史——它们来自哲学和心理学——但直到计算机出现曙光时,AI/ML 和 SDF 研究人员才开始讨论如何将数学技术应用于实际——时间分析。ML,尤其是深度学习,在计算机视觉、自然语言理解和数据分析方面取得了巨大成功。因此,ML 已被提议作为许多固有包含多模态数据的问题的解决方案。例如,自动驾驶汽车的成功验证了机器学习与 SDF 的前景,但还需要更多的研究来解释、理解和协调异构数据分析以实现态势感知。该小组确定了合并 AI/ML 和 SDF 的机会,例如计算效率、改进决策、扩展知识和提供安全性;同时强调多域操作、人机协作和道德部署策略的挑战。
更新日期:2021-07-09
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