Neuroergonomic evaluation of risk-warning eHMI penetration rates in vehicle platoons: effects on pedestrians’ mental workload, situation awareness, and gap acceptance

The deployment of Level 4-5 autonomous vehicles (AVs) in real traffic environments presents significant challenges for pedestrian interaction, primarily due to the absence of human driver cues. To address this limitation, risk-warning external human-machine interfaces (eHMIs), which communicate dynamic crossing risks based on the AV's time-to-arrival (TTA), have received growing attention. However, given the visual complexity of real-world traffic and the potential transition from partial to full eHMI adoption within vehicle platoons, the cognitive impacts of such systems on pedestrians, particularly in terms of mental workload (MW) and situation awareness (SA), require further investigation. This study investigates the impact of three eHMI penetration rates-zero (all vehicles without eHMI), partial (half with and half without), and full (all vehicles with eHMI)-within vehicle platoons on pedestrians' MW, SA, and gap acceptance. To simulate realistic traffic conditions, vehicle speeds (30 km/h and 36 km/h) and temporal gap sizes (ranging from 2 to 6 s in 0.5-s increments) were randomized. A video-based experiment was conducted with 24 participants who simulated street-crossing decisions by selecting appropriate temporal gaps when encountering vehicle platoons. SA was assessed using the Situation Awareness Rating Technique (SART), and MW was evaluated through a concurrent oddball task, with P300 event-related potentials (ERPs) serving as indicators of residual attentional capacity. Results showed that full eHMI penetration resulted in significantly higher SA and lower MW compared to partial penetration condition. When compared to zero penetration, full eHMI penetration enhanced pedestrians' sensitivity to gap size, indicating that as the gap size increased, the probability of pedestrians crossing increased more sharply. Notably, this effect occurred without significantly altering MW or SA. In contrast, partial penetration condition led to pedestrians crossing less frequently when encountering vehicles equipped with eHMI and was associated with significantly higher MW and lower SA. These findings suggest that risk-warning eHMIs enhance pedestrian-AV interactions effectively under full penetration conditions. However, partial penetration may impair pedestrians' decision-making and cognitive performance, highlighting the importance of strategic planning during the transitional deployment phase of eHMIs.

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Publication Details

Journal
Scientific Reports
Published
2026-03-15
DOI
https://doi.org/10.1038/s41598-026-42814-3
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00

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article

Neuroergonomic evaluation of risk-warning eHMI penetration rates in vehicle platoons: effects on pedestrians’ mental workload, situation awareness, and gap acceptance

Puhong Li, Jing Ma, Sheng Wen, Bingjian Liu et al.
Scientific Reports
Human-Automation Interaction and Safety
article

Neuroergonomic evaluation of risk-warning eHMI penetration rates in vehicle platoons: effects on pedestrians’ mental workload, situation awareness, and gap acceptance

Puhong Li, Jing Ma, Sheng Wen, Bingjian Liu, Xu Sun, Wei Lyu, Fang Yang, Siyuan Zhou
article en

Abstract

The deployment of Level 4-5 autonomous vehicles (AVs) in real traffic environments presents significant challenges for pedestrian interaction, primarily due to the absence of human driver cues. To address this limitation, risk-warning external human-machine interfaces (eHMIs), which communicate dynamic crossing risks based on the AV's time-to-arrival (TTA), have received growing attention. However, given the visual complexity of real-world traffic and the potential transition from partial to full eHMI adoption within vehicle platoons, the cognitive impacts of such systems on pedestrians, particularly in terms of mental workload (MW) and situation awareness (SA), require further investigation. This study investigates the impact of three eHMI penetration rates-zero (all vehicles without eHMI), partial (half with and half without), and full (all vehicles with eHMI)-within vehicle platoons on pedestrians' MW, SA, and gap acceptance. To simulate realistic traffic conditions, vehicle speeds (30 km/h and 36 km/h) and temporal gap sizes (ranging from 2 to 6 s in 0.5-s increments) were randomized. A video-based experiment was conducted with 24 participants who simulated street-crossing decisions by selecting appropriate temporal gaps when encountering vehicle platoons. SA was assessed using the Situation Awareness Rating Technique (SART), and MW was evaluated through a concurrent oddball task, with P300 event-related potentials (ERPs) serving as indicators of residual attentional capacity. Results showed that full eHMI penetration resulted in significantly higher SA and lower MW compared to partial penetration condition. When compared to zero penetration, full eHMI penetration enhanced pedestrians' sensitivity to gap size, indicating that as the gap size increased, the probability of pedestrians crossing increased more sharply. Notably, this effect occurred without significantly altering MW or SA. In contrast, partial penetration condition led to pedestrians crossing less frequently when encountering vehicles equipped with eHMI and was associated with significantly higher MW and lower SA. These findings suggest that risk-warning eHMIs enhance pedestrian-AV interactions effectively under full penetration conditions. However, partial penetration may impair pedestrians' decision-making and cognitive performance, highlighting the importance of strategic planning during the transitional deployment phase of eHMIs.

Scientific Reports
University of Nottingham Ningbo China (CN), Qilu University of Technology (CN), Ningbo City College of Vocational Technology (CN), China Automotive Engineering Research Institute (CN), Shandong Academy of Sciences (CN), Anhui Polytechnic University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 29%
Human-Automation Interaction and Safety
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