Analysing temporal performance profiles of UAV operators using time series clustering

- A methodology for analysing UAV operators based on their performance evolution.
- Time series clustering to extract the most representative operator temporal profiles.
- Quantitative evaluation using human judgement-based ground truth data.
- Experimentation carried out in a lightweight multi-UAV simulation environment.
- The resulting profiles contain distinct and informative behavioural patterns.

The continuing growth in the use of Unmanned Aerial Vehicles (UAVs) is causing an important social step forward in the performance of many sensitive tasks, reducing both human and economical risks. The work of UAV operators is a key aspect to guarantee the success of this kind of tasks, and thus UAV operations are studied in many research fields, ranging from human factors to data analysis and machine learning. The present work aims to describe the behaviour of operators over time using a profile-based model where the evolution of the operator performance during a mission is the main unit of measure. In order to compare how different operators act throughout a mission, we describe a methodology based of multivariate-time series clustering to define and analyse a set of representative temporal performance profiles. The proposed methodology is applied in a multi-UAV simulation environment with inexperienced operators, obtaining a fair description of the temporal behavioural patterns followed during the course of the simulation.