Collaborative Research: SLES: Foundations of Qualitative and Quantitative Safety Assessment of Learning-enabled Systems

合作研究：SLES：学习型系统定性和定量安全评估的基础

• 批准号: 2331938
• 负责人: Weiming Xiang
• 金额: $ 27.09万
• 依托单位: AUGUSTA UNIVERSITY RESEARCH INSTITUTE, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331938&HistoricalAwards=false
• 关键词: Collaborative; Research; SLES; Foundations; Qualitative

Learning-enabled autonomous systems operating in unfamiliar or unprecedented environments pose new foundational challenges for their safety assessment and subsequent risk management. In this context, the system-level safety means the complicated behaviors created by the interactions between multiple learning components and the physical world satisfy the safety requirements, protecting the system from accidental failures to avoid hazards such as collisions to other vehicles, bicycles and pedestrians. The qualitative and quantitative methodologies envisioned to complement each other by providing both 'yes' or 'no' binary decisions and numerical measures of safety, which allow for a thorough understanding of safety concerns and enable effective safety verification in uncertain environments. This project targets the foundational challenges of developing qualitative and quantitative safety assessment methods capable of capturing uncertainties from environments and providing timely, comprehensive, and accurate safety evaluations at the system level. The outcomes are expected to boost the trustworthiness and adaptability of learning-enabled systems to the unknown world and facilitate their safe integration into various domains, such as autonomous vehicles, robotics, or industrial automation. Educational and outreach activities are well-integrated into the research, including curriculum development, K-12 STEM outreach, and industrial engagement activities. The designed activities are uniquely positioned to promote diversity throughout this project by giving priority consideration, mentoring, and working with students in underrepresented minority groups. The proposed research efforts will be directed toward building the foundations of end-to-end qualitative and quantitative safety assessment of learning-enabled autonomous systems. This project will develop the probabilistic star temporal logic specification language. The new specification language offers a formalism for expressive modeling of learning process uncertainty and complex temporal behaviors, and supports both qualitative and quantitative reasoning. Efficient computation methods and tools will be developed to verify probabilistic star temporal logic specifications for learning-enabled deep neural network components. The verification methods and tools are centered on enhancing their scalability and resource efficiency. This project will develop system-level qualitative and quantitative safety assessment methods and tools that can handle the interplay of various learning-enabled components in a system under different availability of environment information. Learning-enabled F1Tenth testbed, a small-scale system of real autonomous vehicles and its simulator, will be used to create multiple real-world autonomous driving scenarios to validate and evaluate the applicability, scalability and reliability of the proposed methods and tools.This research is supported by a partnership between the National Science Foundation and Open Philanthropy.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.

在不熟悉或前所未有的环境中运行的学习支持的自主系统对其安全评估和随后的风险管理提出了新的基础挑战。在这种情况下，系统级的安全是指由多个学习组成部分与物理世界之间的相互作用所产生的复杂行为满足安全要求，从而保护系统免于意外失败，以避免诸如对其他车辆，自行车和行人的碰撞之类的危害。通过提供“是”或“否”的二进制决策和安全性衡量标准的定性和定量方法，可以互相补充，这可以彻底了解安全问题并在不确定的环境中启用有效的安全验证。该项目针对开发能够捕获环境不确定性并在系统级别及时，全面和准确的安全评估的定性和定量安全评估方法的基本挑战。预计结果将提高支持学习系统的可信赖性和适应性，并促进其安全整合到各种领域，例如自动驾驶汽车，机器人技术或工业自动化。教育和外展活动已融入研究，包括课程开发，K-12 STEM外展和工业参与活动。通过优先考虑，指导和与代表性不足的少数群体的学生合作，设计的活动是唯一的定位，可以在整个项目中促进多样性。拟议的研究工作将用于建立对支持学习的自主系统的端到端定性和定量安全评估的基础。该项目将开发概率之星时间逻辑规范语言。新的规范语言为学习过程不确定性和复杂的时间行为的表达性建模提供了形式主义，并支持定性和定量推理。将开发有效的计算方法和工具，以验证为启用了学习的深度神经网络组件的概率之星时间逻辑规范。验证方法和工具以增强其可扩展性和资源效率为中心。该项目将开发系统级的定性和定量安全评估方法和工具，这些方法和工具可以在不同的环境信息中处理系统中各种启用学习的组件的相互作用。基于学习的第13个测试床是一个真正的自动驾驶汽车及其模拟器的小规模系统，将用于创建多个现实世界自主驾驶的场景，以验证和评估所提出的方法和工具的适用性，可伸缩性和可靠性。这项研究由国家科学基金会和开放式慈善机构之间的合作伙伴支持。使用基金会的智力优点和更广泛的影响评估标准进行评估。

项目成果

Computationally efficient neural hybrid automaton framework for learning complex dynamics

• DOI: 10.1016/j.neucom.2023.126879
• 发表时间: 2023-10
• 期刊: Neurocomputing
• 影响因子: 6
• 作者: Tao Wang;Yejiang Yang;Weiming Xiang

Maximum output discrepancy computation for convolutional neural network compression

• DOI: 10.1016/j.ins.2024.120367
• 发表时间: 2024-04
• 期刊: Inf. Sci.
• 作者: Zihao Mo;Weiming Xiang