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.

As intelligent cockpit systems increasingly rely on touchscreen interaction, concerns have emerged regarding driver distraction and interaction safety. Existing studies have primarily examined physical interface design, cognitive processing, multimodal interaction, or adaptive systems separately, while a unified framework explaining their combined influence on safety perception and user experience remains limited. Drawing on affordance theory, this study proposes a multidimensional interaction design framework consisting of physical affordance, cognitive affordance, interactive affordance, and dynamic affordance. A structural equation modeling approach was employed using survey data collected from 390 drivers. The results indicate that interactive affordance shows the largest model-estimated path coefficient for safety perception (β = 0.312, p < 0.001) and the largest direct coefficient for user experience (β = 0.246, p < 0.001), closely followed by cognitive affordance (β = 0.212, p < 0.001); because the affordance dimensions are empirically strongly overlapping (HTMT > 0.95), these dimension-level comparisons are descriptive rather than definitive. Safety perception significantly mediates the relationships between affordance dimensions and user experience. In addition, dynamic affordance positively moderates the relationship between physical affordance and safety perception (β = 0.161, p < 0.001), suggesting that context-adaptive interaction can strengthen the safety benefits of physical interface design. This study extends the application of affordance theory to intelligent cockpit touchscreen interaction and highlights the importance of integrating multimodal feedback, cognitive simplicity, and context-adaptive interaction into intelligent cockpit design. The findings provide practical implications for improving perceived safety and user experience in intelligent vehicles.