This study aims to optimize the visual interaction design of AR-HUD and reduce cognitive load in complex driving situations. An immersive driving simulation incorporating eye-tracking technology was utilized to analyze objective physiological indices and measure subjective cognitive load using the NASA-TLX. Additionally, a visual cognitive load index was integrated into a BP-GA neural network model for load prediction, enabling the derivation of an optimal solution for AR-HUD design. The optimized AR-HUD interface demonstrated a significant reduction in cognitive load compared to the previous prototype. The experimental group achieved a mean total score of 25.63 on the WP scale, whereas the control group scored 43.53, indicating a remarkable improvement of 41.4%. This study presents an innovative approach to optimizing AR-HUD design, effectively reducing cognitive load in complex driving situations. The findings demonstrate the potential of the proposed algorithm to enhance user experience and performance.

This study aims to explore AR-HUD(Augmented Reality-Head up Display) visual interaction cognitive load’s prediction algorithm model and obtain the best adaptation mode of AR-HUD interface visual Interaction Design. Through immersive driving simulation experiments, a driver assistance test system was established to analyze drivers’ eye movement behavior and visual resource allocation characteristics. The driver’s attention will be less focused on the driving task and correspondingly less on elements of the driving environment, negatively affecting the recovery of cognitive resources. The focus of this study is to establish a visual cognitive load index by combining the visual intensity model and the user’s subjective cognitive load evaluation of the interface. The AR-HUD visual Interaction Design coding and visual cognitive load index are used as the input and output layers to establish a visual cognitive load prediction neural network model. The neural network model was introduced into the genetic algorithm’s fitness function. The genetic algorithm was used to obtain the optimal AR-HUD Visual Interaction Design solution in the finite solution space. Then the optimal AR-HUD visual Interaction Design was obtained. The CH Scale scale was used to assess the validation of the algorithm’s soundness.

MotivationAugmented reality head-up display (AR-HUD) interface design takes on critical significance in enhancing driving safety and user experience among professional drivers. However, optimizing the above-mentioned interfaces poses challenges, innovative methods are urgently required to enhance performance and reduce cognitive load.DescriptionA novel method was proposed, combining the IVPM method with a GA to optimize AR-HUD interfaces. Leveraging machine learning, the IVPM-GA method was adopted to predict cognitive load and iteratively optimize the interface design.ResultsExperimental results confirmed the superiority of IVPM-GA over the conventional BP-GA method. Optimized AR-HUD interfaces using IVPM-GA significantly enhanced the driving performance, and user experience was enhanced since 80% of participants rated the IVPM-GA interface as visually comfortable and less distracting.ConclusionIn this study, an innovative method was presented to optimize AR-HUD interfaces by integrating IVPM with a GA. IVPM-GA effectively reduced cognitive load, enhanced driving performance, and improved user experience for professional drivers. The above-described findings stress the significance of using machine learning and optimization techniques in AR-HUD interface design, with the aim of enhancing driver safety and occupational health. The study confirmed the practical implications of machine learning optimization algorithms for designing AR-HUD interfaces with reduced cognitive load and improved occupational safety and health (OSH) for professional drivers.