CRII: CNS: Supporting Resilient Perception in Autonomous Cyber-physical Systems

CRII：CNS：支持自主网络物理系统中的弹性感知

• 批准号: 2348349
• 负责人: Bryan Donyanavard
• 金额: $ 17.5万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2348349&HistoricalAwards=false
• 关键词: CRII; CNS; Supporting; Resilient; Perception

Autonomous cyber-physical systems (A-CPS), including self-driving vehicles, environmental monitoring drones, and search-and-rescue robots, are expected to navigate challenging physical landscapes without human assistance. To ensure mission success, it is important to understand what internal or external conditions may cause A-CPS devices to fail. The state-of-the-art is to verify that A-CPS can operate in adverse conditions before deployment, followed by continuous monitoring post-deployment. However, it is difficult to anticipate all of the difficulties an A-CPS device may encounter once deployed in the real world, and if failure conditions are observed while the device is operating, it is typically too late to react. This project focuses on predicting failures for A-CPS ahead of time and injecting actions to avoid the failures. In particular, the project analyzes a state-of-the-art object detection application executing on a commercial-of-the-shelf embedded system, generates data-driven predictive models of failure using machine learning techniques, and implements a software manager to configure the system to avoid predicted failures. The success of the project will provide new insights into the relationship between physical environments and A-CPS behaviors and will enable developers to create more reliable and resilient algorithms with higher confidence.Autonomous cyber-physical systems that can navigate the physical world without human intervention could significantly improve quality of life and safety. For instance, California alone has over 31 million acres of wildlands, with high or very high fire hazard severity zones, spanning from the northern to the southern borders. Autonomous wildfire detection drones could help save lives of residents that are potential wildfire victims, as well as public servants responsible for monitoring high risk regions. To be successful, such systems must be able to handle difficult and unpredictable environmental conditions.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.

自主网络物理系统（A-CPS），包括自动驾驶汽车、环境监测无人机和搜救机器人，预计将在没有人类帮助的情况下，在具有挑战性的物理环境中导航。为确保使命成功，了解可能导致A-CPS设备故障的内部或外部条件非常重要。最先进的技术是在部署前验证A-CPS可以在不利条件下运行，然后在部署后进行持续监测。然而，一旦在真实的世界中部署A-CPS设备，就难以预期A-CPS设备可能遇到的所有困难，并且如果在设备操作时观察到故障状况，则通常反应太晚。该项目的重点是提前预测A-CPS的故障，并注入措施，以避免故障。特别是，该项目分析了在商用嵌入式系统上执行的最先进的对象检测应用程序，使用机器学习技术生成数据驱动的故障预测模型，并实现了软件管理器来配置系统以避免预测故障。该项目的成功将为物理环境和A-CPS行为之间的关系提供新的见解，并将使开发人员能够以更高的信心创建更可靠和更有弹性的算法。可以在没有人为干预的情况下导航物理世界的自主网络物理系统可以显着提高生活质量和安全性。例如，仅加州就有超过3100万英亩的荒地，从北方到南部边界都有高或非常高的火灾危险严重性区域。自动野火探测无人机可以帮助拯救潜在野火受害者的居民以及负责监测高风险地区的公务员的生命。为了取得成功，这些系统必须能够处理困难和不可预测的环境条件。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。