Research On Workload Of Remote Tower Controller

Remote tower technology is developing rapidly all over the world,and as the tower control environment and command method change,the workload of the controller also changes,but in order to ensure safety and safety,in China there is relatively little research on remote towers.efficiency of remote tower operation.It is very important to effectively monitor the workload of the remote tower controller in real time.In recent years,emerging technologies have effectively supported the detection of eye movements and head postures on remote tower controllers.In particular,computer vision technology is better suited for real remote tower control environments because it can quickly process large amounts of video image data without the need for a controller equipped with physiological equipment or eye trackers.The purpose of this study is to study and build a workload evaluation model for remote tower controllers based on eye characteristics and head posture estimates in terms of comprehensiveness,real-time,and accuracy.First,based on a literature review,we will explain the workload definition,analyze the process of remote tower control,and analyze the influencing factors from four aspects:people,machinery,environment,and management.Based on this,the key metrics that support workload assessment are selected(blink frequency,blink time,PERCLOS value,head-up time percentage,talk time,NASA-TLX score).Then build using a directional gradient histogram,a constrained local neural domain,and the EPn P algorithm_a simulates how to detect the actual operating environment of a remote tower.control experiments,data analysis,and results show:blink frequency,blink time,PERCLOS value,head-up time percentage,talk time,NASA-TLX score are significantly related to remote tower control load;finally use eye movements.between indicators.colinearity builds an eye movement coefficient and combines it with the call time and NASA-TLX score to establish a multiple linear regression model.The obtained model is validated using the first model.-Xinjiang remote tower line data.The results show that the regression model load measurements are equal to the NASA-TLX subjective load score,this can effectively reflect changes in remote tower control workloads.