论文标题
评估无人机紧急着陆的运行时监控
Evaluation of Runtime Monitoring for UAV Emergency Landing
论文作者
论文摘要
要在人口稠密地区认证无人机运营，必须制定诸如紧急降落（EL）之类的风险缓解策略，以解决潜在的失败。 EL的目的是通过使用车载传感器找到安全的着陆区来降低地面风险。本文的第一个贡献是提出一种新的EL方法，该方法符合最近研究中提出的安全要求。特别是，提出的EL管道包括在执行过程中监视基于学习的组件的机制。这样，另一个贡献是研究机器学习运行时监控（MLRM）方法在现实世界中关键系统的背景下的行为。引入了一种新的评估方法，并应用于评估三种MLRM机制的实际安全益处。将所提出的方法与默认缓解策略进行比较（检测到失败时打开降落伞），并且似乎更安全。
To certify UAV operations in populated areas, risk mitigation strategies -- such as Emergency Landing (EL) -- must be in place to account for potential failures. EL aims at reducing ground risk by finding safe landing areas using on-board sensors. The first contribution of this paper is to present a new EL approach, in line with safety requirements introduced in recent research. In particular, the proposed EL pipeline includes mechanisms to monitor learning based components during execution. This way, another contribution is to study the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within the context of a real-world critical system. A new evaluation methodology is introduced, and applied to assess the practical safety benefits of three MLRM mechanisms. The proposed approach is compared to a default mitigation strategy (open a parachute when a failure is detected), and appears to be much safer.
