针对长期运行的智能人机系统，提出个性化建模方法提升安全与可靠性。
Personalized Model-Based Design of Human Centric AI enabled CPS for Long term usage
- 基于个性化模型，动态适应长期使用中的未知场景。
- 解决传统测试方法在长周期应用中覆盖不足的问题。
- 适合医疗、自动驾驶等高安全性要求的持续运行系统。
人本关键系统越来越多地引入人工智能，以从传感器数据中提取知识。典型应用包括医疗监测与控制系统、基于手势的人机交互系统以及自动驾驶汽车。这些系统设计用于长期甚至终身运行，如1型糖尿病闭环血糖控制、自动驾驶汽车及中风诊断与康复监测系统。然而，长期运行可能暴露于未充分测试的边缘情况，导致系统性能不确定，进而违反安全、可持续性和安全性要求。本文分析了现有针对人工智能赋能的人本控制系统在安全、可持续性与安全性方面的分析技术，并讨论其在实际长期使用中测试能力的局限性。随后，提出个性化建模解决方案，以潜在消除上述局限。
原文摘要 · Abstract (English)
Human centric critical systems are increasingly involving artificial intelligence to enable knowledge extraction from sensor collected data. Examples include medical monitoring and control systems, gesture based human computer interaction systems, and autonomous cars. Such systems are intended to operate for a long term potentially for a lifetime in many scenarios such as closed loop blood glucose control for Type 1 diabetics, self-driving cars, and monitoting systems for stroke diagnosis, and rehabilitation. Long term operation of such AI enabled human centric applications can expose them to corner cases for which their operation is may be uncertain. This can be due to many reasons such as inherent flaws in the design, limited resources for testing, inherent computational limitations of the testing methodology, or unknown use cases resulting from human interaction with the system. Such untested corner cases or cases for which the system performance is uncertain can lead to violations in the safety, sustainability, and security requirements of the system. In this paper, we analyze the existing techniques for safety, sustainability, and security analysis of an AI enabled human centric control system and discuss their limitations for testing the system for long term use in practice. We then propose personalized model based solutions for potentially eliminating such limitations.
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