Autonomous driving paper index
Data Management and Privacy in AI for Aerospace and Defense
One-line summary
The aerospace and defense (A&D) sector, a global industry valued in the trillions of dollars, is demonstrably at the forefront of the transformation driven by artificial intelligence (AI).
Engineering notes
The lessons learned in securing data for these critical terrestrial systems are directly applicable to the ultimate benchmark: architecting trustworthy AI to operate safely in the unforgiving environment of space.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
The aerospace and defense (A&D) sector, a global industry valued in the trillions of dollars, is demonstrably at the forefront of the transformation driven by artificial intelligence (AI). AI is no longer a futuristic concept but a present-day enabler, driving advancements from enhanced situational awareness through intelligent sensor fusion and optimized logistics powered by predictive analytics, to the deployment of autonomous operational systems and sophisticated predictive maintenance schedules that increase platform availability. The global AI market in A&D is projected for significant growth, with some estimates projecting it to more than double in the next decade, underscoring its escalating importance. At the heart of this revolution lies data—vast, varied, and vital. Data serves as the critical fuel for AI algorithms, training them to recognize patterns, make predictions, and execute complex tasks. However, this profound reliance on data introduces a dual challenge: while data propels innovation and operational advantage, it simultaneously presents significant and multifaceted risks to security, privacy, and ethical integrity, particularly within the high-stakes A&D environment, which has zero tolerance for failure. This chapter uses the aerospace and defense sector as its primary lens because it represents the most demanding proving ground for the principles of data and AI assurance. The lessons learned in securing data for these critical terrestrial systems are directly applicable to the ultimate benchmark: architecting trustworthy AI to operate safely in the unforgiving environment of space.
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