Assessing driver reactions to emergency navigation prompts in head-up displays
This study examines the effectiveness of warning messages via Head-up displays (HUDs) that provided navigation-based evasion instructions in enhancing driver performance and safety during critical traffic events. A dual-method approach was employed, combining an online survey with a driving simulator study. The online survey evaluated drivers' initial reactions to different visual HUD designs, such as directional arrows, lane markings, and textual cues, within a simulated emergency scenario. The insights gained from the survey were used to identify the most effective warning design, which was subsequently implemented in a driving simulator experiment. There, the real-time responses of drivers to the preferred HUD design were examined under controlled conditions. The results show that the instructions improve drivers' understanding of the required actions and significantly reduce reaction time to approaching emergency vehicles, as reflected by behavioural and perceptual indicators associated with improved situational awareness. Participants who received visual instructions in the HUD reported reduced cognitive workload, greater confidence in their actions and a more accurate understanding of lane navigation requirements. Data from eye movement analyses and lane deviation analyses confirmed faster and more precise reactions in critical situations, such as forming an emergency lane, while physiological measurements, including heart rate, showed no significant increase in stress levels. These results highlight the potential of head-up displays as effective tools for improving traffic safety and optimising rescue chain operations during critical incidents.
Authors
- Marie David (ORCID: https://orcid.org/0000-0002-4511-3266)
- Дирк Кемпер (ORCID: https://orcid.org/0000-0002-3099-6836)
- Álvaro García (ORCID: https://orcid.org/0000-0001-8730-6632)
- Martin Baumann
Institutions
- Universität Ulm (DE)
- Technische Hochschule Ulm (DE)
- RWTH Aachen University (DE)
Publication Details
- Journal
- Accident Analysis & Prevention
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1016/j.aap.2025.108199
- Primary Topic
- Human-Automation Interaction and Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.00