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.

自主的网络物理系统（A-CP），包括自动驾驶汽车，环境监测无人机和搜索和救援机器人，预计无需人力援助即可在挑战性的物理景观中导航。为了确保任务成功，重要的是要了解什么内部或外部条件可能导致A-CPS设备失败。最先进的是要验证A-CP在部署前可以在不利条件下运行，然后进行连续监测后部署。但是，很难预见A-CPS设备一旦在现实世界中就遇到的所有困难，并且如果在设备运行时观察到故障条件，则通常为时已晚。该项目的重点是提前预测A-CP的失败，并注入动作以避免失败。特别是，该项目分析了在商业嵌入式系统上执行的最新对象检测应用程序，使用机器学习技术生成数据驱动的预测模型，并实现软件管理器以配置系统以避免预测故障。该项目的成功将为物理环境与A-CPS行为之间的关系提供新的见解，并使开发人员能够以更高的信心创建更可靠和弹性的算法。自治的网络物理系统可以在没有人类干预的情况下导航物理世界，可以显着改善生活质量和安全的质量。例如，仅加利福尼亚就拥有超过3,100万英亩的野生地，其火灾严重性区域高或很高，从北部到南部边界。自主野火检测无人机可以帮助挽救潜在的野火受害者的居民的生命，以及负责监测高风险地区的公务员。为了取得成功，此类系统必须能够处理困难和不可预测的环境条件。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。