Forward Collision Warning Effects Across Driver Attention State Clusters Under Comparable-Risk Naturalistic Driving Conditions

Using Naturalistic Driving Study data, this study examined how the association between Forward Collision Warning (FCW) activation and perception-reaction time (PRT) varied across driver attention states during car-following. Head-orientation, gaze-direction, and upper-body posture features were used in a Hidden Markov Model to estimate unstable-attention probability over time, and clustering identified three attention-state profiles: stable, fluctuating, and persistently unstable. The stable profile reflected consistently low unstable-state probability, the fluctuating profile reflected frequent shifts in unstable-state probability, and the persistently unstable profile reflected sustained high unstable-state probability. A Gamma generalized estimating equation model was applied to comparable-risk events. The association between FCW activation and PRT showed evidence of variation across attention states, with the largest estimated warning-related reduction observed in the persistently unstable attention state. These findings suggest that considering pre-event attention state may enable a more informative assessment of the support FCW can provide in situations with a greater risk of delayed driver response.

Authors

Institutions

Publication Details

Journal
International Journal of Human-Computer Interaction
Published
2026-10-09
DOI
https://doi.org/10.1080/10447318.2026.2740895
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Forward Collision Warning Effects Across Driver Attention State Clusters Under Comparable-Risk Naturalistic Driving Conditions

Mohamed A. Abdel-Aty, Yang-Jun Joo, Uibeom Chun, Rodrigo Vena Garcia
International Journal of Human-Computer Interaction
Human-Automation Interaction and Safety
article

Forward Collision Warning Effects Across Driver Attention State Clusters Under Comparable-Risk Naturalistic Driving Conditions

Mohamed A. Abdel-Aty, Yang-Jun Joo, Uibeom Chun, Rodrigo Vena Garcia
article en

Abstract

Using Naturalistic Driving Study data, this study examined how the association between Forward Collision Warning (FCW) activation and perception-reaction time (PRT) varied across driver attention states during car-following. Head-orientation, gaze-direction, and upper-body posture features were used in a Hidden Markov Model to estimate unstable-attention probability over time, and clustering identified three attention-state profiles: stable, fluctuating, and persistently unstable. The stable profile reflected consistently low unstable-state probability, the fluctuating profile reflected frequent shifts in unstable-state probability, and the persistently unstable profile reflected sustained high unstable-state probability. A Gamma generalized estimating equation model was applied to comparable-risk events. The association between FCW activation and PRT showed evidence of variation across attention states, with the largest estimated warning-related reduction observed in the persistently unstable attention state. These findings suggest that considering pre-event attention state may enable a more informative assessment of the support FCW can provide in situations with a greater risk of delayed driver response.

International Journal of Human-Computer Interaction
University of Central Florida (US), College of Central Florida (US)
Openalex Percentile: Top 8%
Human-Automation Interaction and Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.