Formulation of tunnel vision risk prediction model using situational awareness measures

Abstract

Safety is paramount in aviation, and air traffic control (ATC) plays a critical role in ensuring safety of traffic in the skies. With aviation recovering from the effects of the pandemic and expected to grow beyond pre-pandemic levels, air traffic will increase, thereby increasing the load on air traffic controllers. Both dispersed and concentrated attention are required in ATC, but there is a limit on humans’ attention. With increased congestion in the skies, concentrated areas of aircraft may occur, potentially leading to attention tunneled in an area. Overall dispersed attention may be compromised, and situational awareness (SA) over the entire area of control is lost as a result. This project aims to formulate a model to predict the risk of attentional tunnelling in ATC scenarios. A model was first proposed under Methodology, and experiments were conducted on the NARSIM ATC Simulator along with the use of SA measures, namely eye-tracking and Situation Awareness Global Assessment Technique (SAGAT). The data collected in the experiments were used to validate the proposed model.