4 accepted papers
With the increasing use of neural networks in critical systems, runtime monitoring becomes essential to reject unsafe predictions during inference. Various techniques have emerged to establish rejection scores that maximize the separability between the distributions of safe and unsafe predictions. T…
The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee their correct behavior. Runtime monitors are components aiming to identify unsafe predictions and discard them before they can lead to catastrophic consequences. Several recent works on runtime monito…
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 n…
Robotic systems evolving in hazardous and harsh environment are prone to mission failure or system loss in presence of faults. This paper presents a fault tolerant methodology, implemented into a control architecture of an underwater robot that executes biological monitoring missions. High level con…