CAREER: Towards Safety-Critical Real-Time Systems with Learning Components

在人工智能（AI）及其广泛应用的快速发展中，当今的安全关键系统，从自动驾驶汽车到手术机器人，越来越依赖于支持学习的模块。确保这些人工智能驱动系统的时间安全至关重要，特别是在高风险和时间关键的环境中。除了提高功能、准确性和效率外，这些系统还必须在最坏情况和极端事件下安全地满足实时约束。然而，具有学习组件的安全关键系统与传统系统有很大的不同，在各个方面表现出更大的动态性和复杂性。设计高性能的实时调度策略，并进行严格但适当的悲观分析，以证明时间的安全性变得更具挑战性。该项目旨在通过创建一个框架来解决这一挑战，该框架将实时安全验证和保证无缝集成到人工智能驱动的安全关键系统的性能优化过程中。它将有助于现代自主系统的关键技术的进步，这些系统具有学习能力，需要对高度动态的内部和外部环境做出响应。此外，该项目非常重视将研究融入教育和推广活动，以促进STEM教育的多样性，并扩大对计算和工程的参与。该项目旨在提供实时安全保障，同时优化具有学习组件的安全关键系统的平均性能和效率。为了实现这一目标，该项目将设计创新的实时调度策略，专门用于处理这些系统的复杂和动态计算工作量，以实现最小性能损失的时间安全。理论分析将得出约束最坏情况下的时序行为，同时最大限度地提高平均性能和效率。此外，它将开创安全强化学习机制，旨在有效地学习和优化系统性能，同时确保部署时的时间安全性。该项目为应对为人工智能驱动的安全关键系统提供临时安全保证的巨大挑战奠定了基础，同时最大限度地提高其整体性能，包括增强实现任务级目标的能力，优化节能，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，更广泛的影响审查标准。

In the rapidly advancing world of artificial intelligence (AI) and its widespread applications, today's safety-critical systems, ranging from self-driving cars to surgical robots, increasingly rely on learning-enabled modules. Ensuring the temporal safety of these AI-driven systems is crucial, especially in high-stakes and time-critical settings. Alongside improving functionality, accuracy, and efficiency, these systems must be safe in meeting real-time constraints despite worst-case scenarios and extreme events. However, safety-critical systems with learning components differ significantly from traditional ones, exhibiting greater dynamism and complexity in various aspects. Designing performant real-time scheduling strategies and conducting rigorous yet appropriately pessimistic analyses for proving temporal safety becomes much more challenging. This project aims to address this challenge by creating a framework that seamlessly integrates real-time safety verification and assurance into the performance optimization process of AI-driven safety-critical systems. It will contribute to the advancement of critical technologies of modern autonomous systems with learning capabilities that need to respond to highly dynamic internal and external environments. Moreover, the project places a strong emphasis on integrating research into educational and outreach activities to promote diversity in STEM education and broaden participation in computing and engineering.This project aims to provide real-time safety guarantees while optimizing the average performance and efficiency of safety-critical systems with learning components. To achieve this, the project will devise innovative real-time scheduling strategies specifically tailored to handle the intricate and dynamic computational workloads of these systems for achieving temporal safety with minimum performance loss. Theoretical analyses will be derived to bound the worst-case timing behavior while simultaneously maximizing average performance and efficiency. Additionally, it will pioneer safe reinforcement learning mechanisms designed to efficiently learn and optimize system performance while ascertaining temporal safety upon deployment. The project lays the foundation for addressing the grand challenge of providing temporal safety guarantees for AI-driven safety-critical systems while simultaneously maximizing their overall performance, including enhancing the capability of achieving mission-level goals, optimizing energy-saving, and improving system efficiency.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.