Assessing the Impact of Risk-Warning eHMI Information Content on Pedestrian Mental Workload, Situation Awareness, and Gap Acceptance in Full and Partial eHMI Penetration Vehicle Platoons

External Human–Machine Interfaces (eHMIs) enhance pedestrian safety in interactions with autonomous vehicles (AVs) by signaling crossing risk based on time-to-arrival (TTA), categorized as low, medium, or high. This study compared five eHMI configurations (single-level low, medium, high; two-level low-medium, medium-high) against a three-level (low-medium-high) configuration to assess their impact on pedestrians’ crossing decisions, mental workload (MW), and situation awareness (SA) in vehicle platoon scenarios under full and partial eHMI penetration. In a video-based experiment with 24 participants, crossing decisions were evaluated via temporal gap selection, MW via P300 event-related potentials in an auditory oddball task, and SA via the Situation Awareness Rating Technique. The three-level configuration outperformed single-level medium, single-level high, two-level low-medium, and two-level medium-high in gap acceptance, promoting safer decisions by rejecting smaller gaps and accepting larger ones, and exhibited lower MW than the two-level medium-high configuration under partial penetration. No SA differences were observed. Although the three-level configuration was generally appreciated, future research should optimize presentation to mitigate issues from rapid signal changes. Notably, the single-level low configuration showed comparable performance, suggesting a simpler alternative for real-world eHMI deployment.

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Publication Details

Journal
Applied Sciences
Published
2025-07-24
DOI
https://doi.org/10.3390/app15158250
Citations
1
Primary Topic
Risk and Safety Analysis
Type
article
Field-Weighted Citation Impact
1.40

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article

Assessing the Impact of Risk-Warning eHMI Information Content on Pedestrian Mental Workload, Situation Awareness, and Gap Acceptance in Full and Partial eHMI Penetration Vehicle Platoons

Fang Yang, Xu Song Sun, Bingjian Liu, Jiming Bai et al.
1 citations
Applied Sciences
Risk and Safety Analysis
1.40
article

Assessing the Impact of Risk-Warning eHMI Information Content on Pedestrian Mental Workload, Situation Awareness, and Gap Acceptance in Full and Partial eHMI Penetration Vehicle Platoons

Fang Yang, Xu Song Sun, Bingjian Liu, Jiming Bai, Sheng Zhang, Luis Felipe Moreno Leyva
article en
1 citations

Abstract

External Human–Machine Interfaces (eHMIs) enhance pedestrian safety in interactions with autonomous vehicles (AVs) by signaling crossing risk based on time-to-arrival (TTA), categorized as low, medium, or high. This study compared five eHMI configurations (single-level low, medium, high; two-level low-medium, medium-high) against a three-level (low-medium-high) configuration to assess their impact on pedestrians’ crossing decisions, mental workload (MW), and situation awareness (SA) in vehicle platoon scenarios under full and partial eHMI penetration. In a video-based experiment with 24 participants, crossing decisions were evaluated via temporal gap selection, MW via P300 event-related potentials in an auditory oddball task, and SA via the Situation Awareness Rating Technique. The three-level configuration outperformed single-level medium, single-level high, two-level low-medium, and two-level medium-high in gap acceptance, promoting safer decisions by rejecting smaller gaps and accepting larger ones, and exhibited lower MW than the two-level medium-high configuration under partial penetration. No SA differences were observed. Although the three-level configuration was generally appreciated, future research should optimize presentation to mitigate issues from rapid signal changes. Notably, the single-level low configuration showed comparable performance, suggesting a simpler alternative for real-world eHMI deployment.

Applied SciencesVol. 15(15)
University of Nottingham Ningbo China (CN), Zhejiang University (CN)
Ningbo Municipal Bureau of Science and Technology, Qilu University of Technology, University of Nottingham Ningbo China
Openalex Percentile: Top 14%
Risk and Safety Analysis
1.40
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