Machine learning systems are becoming increasingly ubiquitous. These systems's adoption
has been expanding, accelerating the shift towards a more algorithmic society, meaning that …

Over the past decade, unmanned aerial vehicles (UAVs) have demonstrated increasing
attention and promise. They demonstrate great potential for application in both civilian and …

The ability to avoid collisions with moving obstacles, such as commercial aircraft is critical to
the safe operation of unmanned aerial vehicles (UAVs) and other air traffic. This paper …

Interpretability has become incredibly important as machine learning is increasingly used to
inform consequential decisions. We propose to construct global explanations of complex …

Applications of aerial robots are progressively expanding into complex urban and natural
environments. Despite remarkable advancements in the field, robotic rotorcraft is still …

The ability to interpret machine learning models has become increasingly important now that
machine learning is used to inform consequential decisions. We propose an approach …

In present road traffic system, drone-network based traffic monitoring using the Internet of
Vehicles (IoVs) is a promising solution. However, camera-based traffic monitoring does not …

Security of (semi)-autonomous vehicles is a growing concern, first, due to the increased
exposure of the functionality to potential attackers; second, due to the reliance of …

A path-planning algorithm to guide unmanned aerial vehicles (UAVs) for tracking multiple
ground targets based on the theory of partially observable Markov decision processes …

We propose to synthesize a control policy for a Markov decision process (MDP) such that the
resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a …