SAGE Journals

Are Warnings Suitable for Presentation in Head-Up Display? A Meta-Analysis for the Effect of Head-Up Display Warning on Driving Performance

Posted on 2023-09-17 - 12:08

Head-up display (HUD) warnings are gaining increasing attention with the growing importance of advanced warning systems in vehicles for improving driving safety. However, the effectiveness of HUD warnings is still controversial considering the various driving performance indexes, warning modalities, and driving scenarios used in previous studies. This study conducts meta-analyses of the relevant literature to examine whether HUD warnings (both unimodal HUD warnings and multimodal warnings with HUD) have advantages compared with other warnings (head-down display warnings, auditory warnings, tactile warnings, and multimodal warnings without HUD) for driver reaction times and driving quality. Overall, 33 articles were included in four meta-analyses. Additionally, the effects of the related moderator variables of scenario, mapping of warnings, and non-driving-related tasks (NDRTs) on the relationship between using HUD warnings and using non-HUD warnings was analyzed. The results show that using unimodal HUD warnings leads to faster reaction times compared with head-down display warnings but has no significant advantage on driving performance over auditory warnings in general and results in slower reaction times than using tactile warnings. However, results of the moderating effects suggest that using HUD warnings in non-critical scenarios and using high mapping HUD icons (i.e., HUD warnings with specific hazard information) are conducive to improving driving performance in comparison with other warning types. These results highlight the potential advantages of HUD in transmitting warning information. The future application and design of HUD warnings may need to focus on specific situations considering iconic mapping to an event.


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Transportation Research Record: Journal of the Transportation Research Board


Liuqingcheng Niu
Sike Gao
Jinlei Shi
Changxu Wu
Yuwei Wang
Shu Ma
Duming Wang
Zhen Yang
Hongting Li
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