Effects of Driving Task Demands and Information Load on AR-HUD Cognitive Efficiency: The Moderating Role of Working...

The driving scenario and information load jointly influence the cognitive efficiency of augmented reality head-up display (AR-HUD) interfaces. However, the moderating role of drivers' working memory capacity (WMC) remains unclear. To investigate this mechanism, a simulated driving experiment with a mixed design was conducted in a low-immersivity desktop virtual reality (VR) environment. First, 40 volunteers were screened using an automated operation span task, yielding 16 high- and low-WMC participants. They then drove under three scenarios (urban intersection, expressway, construction zone) and six levels of AR-HUD visual information load. Generalized linear models were applied to the reaction time, fixation duration, and pupil diameter. The results revealed a significant three-way interaction among WMC, scenario, and information load. High-WMC drivers maintained faster responses and lower subjective loads up to Levels 4-6, adopting a deep processing strategy; low-WMC drivers already showed cognitive overload at Level 4 and above, requiring an optimal load range of Level 2-3. The construction zone induced the steepest increase in cognitive load, whereas the expressway markedly reduced sensitivity to additional visual information. Therefore, the optimal AR-HUD information load must be adapted to drivers' WMC: high-WMC drivers can safely handle Levels 4-6 in low- or medium-demand scenarios, whereas low-WMC drivers require a minimalist presentation of Levels 2-3 in high-demand situations. This study provides quantitative, empirically grounded guidelines for designing cognitively adaptive AR-HUD interfaces.