Cognitive evaluation of HUD interface layout for intelligent automotive based on Bayesian BWM and Gray-TOPSIS

Abstract

To reduce drivers’ cognitive load during the driving process, The present study concentrates on the cognitive evaluation and analysis of the Head-Up Display (HUD) interface layout, aiming to enhance human cognitive efficiency. Initially, a combination of eye-tracking technology and cognitive load theory is used to investigate users’ attention allocation and changes in eye movement indicators, followed by the conversion of these indicators. A comprehensive HUD interface layout evaluation system is established, considering structural layout esthetics, task efficiency, and cognitive load. To achieve this, an intelligent cognitive evaluation method for the automotive HUD interface layout is proposed, based on the Bayesian BWM and Gray-TOPSIS. Bayesian BWM is employed to determine the weights of evaluation indicators, followed by Gray-TOPSIS to assess and rank the layout candidate solutions. Experimental results indicate that in the optimal layout design, users exhibit fewer eye movements, shorter gaze durations, esthetically pleasing interface structures, and lower cognitive loads. Furthermore, comparative experiments validate the effectiveness and stability of the Bayesian BWM and Gray-TOPSIS methods. These findings offer guidance and reference for further optimizing the layout of intelligent automotive HUD interfaces.