Growing air traffic and an increasing demand for Unmanned Aircraft System (UAS) services elicit the need for new systems assisting air traffic controllers and UAS operators in performing their tasks in order to manage more aircraft without compromising operational safety. A key challenge is to support One-To-Many (OTM) operations where a single human operator is responsible for command and control of multiple assets. This study focusses on facial expression analysis for cognitive state estimation in a representative OTM scenario. The aim is to support the design of OTM systems and associated Human-Machine Interfaces and Interaction (HMI2), which can dynamically adapt the automation support level as a function of the operator’s cognitive states.
