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

包括自动驾驶车辆、环境监测无人机和搜救机器人在内的自主网络物理系统(A-CP)预计将在没有人类帮助的情况下导航具有挑战性的物理景观。为了确保任务成功，重要的是要了解哪些内部或外部条件可能导致A-CPS设备发生故障。最先进的做法是在部署前验证A-CPS可以在恶劣条件下运行，然后在部署后进行持续监测。然而，很难预见A-CPS设备一旦部署在现实世界中可能遇到的所有困难，并且如果在设备运行时观察到故障条件，则通常为时已晚。本项目的重点是提前预测A-CP的故障，并注入避免故障的行动。特别是，该项目分析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，并实现软件管理器来配置系统以避免预测的故障。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创建更可靠、更具弹性和更高置信度的算法。无需人工干预即可在物理世界中导航的自主网络物理系统可以显著提高生活质量和安全性。例如，仅加利福尼亚州就有超过3100万英亩的荒地，从北部边界到南部边界都有高度或非常严重的火灾危险区域。自主野火探测无人机可以帮助拯救可能成为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。为了成功，这样的系统必须能够处理困难和不可预测的环境条件。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.