# ATC Human Factors Involved in RPAS Contingency Management in Non-Segregated Airspace
## 2 Method
### Human Factor Concerns in RPAS Operations  
Working with any kind of autonomous system would face various human-factor related challenges. Comparing manned aircrafts with autonomous unmanned systems, the RPAS exhibits unconventional flight characteristics: they fly at lower cruise speeds and have limited climb and descent performance, but, at the same time, are capable of flying at higher altitudes and for more extended periods. Furthermore, unmanned aircrafts are primarily employed on surveillance missions, such as security monitoring, search and rescue, etc., generally involving mission-oriented flight plans, including extensive scanning and loitering, rather than the typical point-to-point trajectories employed in commercial aviation. This specific type of trajectory requires new considerations for wake vortex, separation, and visual/radar detection, as well as an improvement in the prediction of trajectories (e.g., flight-intent technology proposed in [8]). Due to such flight characteristics, RPAS management and operation may also induce an increased workload for their remote pilots and air traffic controllers. The extensive duration of RPA operations causes it to be difficult for pilots to maintain vigilance due to the inactivity associated with the low task load and lack of interaction with the system for long periods of the mission. On the other hand, when the workload increases, operators are required to allocate their attention among multiple tasks effectively. Generally, automation tends to shift operators from autonomous controllers of work activities to the passive monitors of technologies. Such tasks may elicit passive fatigue in operators, implying a risk of task disengagement that may be exacerbated by fatigue. This is relevant not only for the RPA piloting but also for its interaction with air traffic controllers. Figure 1 depicts the Yerkes-Dodson Law, where performance varies with arousal. Performance quality is expected to decline when arousal is too high or too low and tunnel vision is symptomatic of attention narrowing. Conversely, moderate workload levels over prolonged periods can prompt operators to learn to adapt and adjust. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Rating Scale** & **Kind of Evaluation** & **Evaluation Area** \\ \hline NASA Task Load Index [10] & Subjective assessments & Workload \\ \hline Instantaneous Self-assessment [11] & Subjective assessments & Workload \\ \hline Cognitive Architecture for Safety Critical Task Simulation (CASCaS) [12] & Analytical techniques & Advanced flight interfaces \\ \hline Air-Man-Machine Integration Design and Analysis System (Air-MIDAS) [13] & Analytical techniques & ATC handoff tasks and ATM free flight concept \\ \hline Modified Bedford [14] & Subjective assessments & Flight handling qualities \\ \hline Modified Cooper-Harper [15] & Subjective assessments & Flight handling qualities \\ \hline Subjective Workload Assessment Technique (SWAT) [16] & Subjective assessments & Workload \\ \hline Situational Awareness Rating Technique (SART) [17] & Subjective assessments & Situational awareness \\ \hline Situational Awareness Subjective Workload Dominance (SA-SWORD) [17] & Subjective assessments & Situational awareness \\ \hline Situational Awareness Supervisory Rating Form [17] & Subjective assessments & Situational awareness \\ \hline Adaptive Control of Thought-Rational (ACT-R) [18] & Cognitive architectures & ATC human–machine interactions \\ \hline Situation Awareness Global Assessment Technique (SAGAT) [19] & Task-based metrics & Situational awareness of pilots and ATM operators \\ \hline Situation Present Assessment Method (SPAM) [19] & Task-based metrics & Situation awareness in air traffic control tasks \\ \hline Capacity analysis methodology (CAPAN) [20] & Task-based metrics & ATC workload and sector capacity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Human factor rating scales in the aviation domain.