Quantitative model of the driver's reaction time during daytime fog – application to a head up display-based advanced driver assistance system

Road accidents because of fog are relatively rare but their severity is greater and the risk of pile-up is higher. However, processing the images grabbed by cameras embedded in the vehicles can restore some visibility. Tarel et al. (2012) proposed to implement head up displays (HUD) to help drivers...

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Bibliographic Details
Published in:IET intelligent transport systems 2015-05, Vol.9 (4), p.375-381
Main Authors: Halmaoui, Houssam, Joulan, Karine, Hautière, Nicolas, Cord, Aurélien, Brémond, Roland
Format: Article
Language:English
Subjects:
Cameras
collision avoidance
Computation
Computer Science
contextual cues
contrast polarity
daytime fog
dehazed image display
driver information systems
driver reaction time
driver RT
Embedded Systems
Fog
head up display‐based advanced driving assistance system
head‐up displays
HUD‐based ADAS
image processing
Mathematical models
modified Piéron’s law
onboard camera images
population image
predictive model
psychology
quantitative model
Reaction time
road accidents
road safety
road safety improvement
road scene
Roads
target visibility
Visibility
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Summary:Road accidents because of fog are relatively rare but their severity is greater and the risk of pile-up is higher. However, processing the images grabbed by cameras embedded in the vehicles can restore some visibility. Tarel et al. (2012) proposed to implement head up displays (HUD) to help drivers anticipate potential collisions by displaying dehazed images of the road scene. In the present study, three experiments have been designed to quantify the expected gain of such a system in terms of the driver's reaction time (RT). The first experiment compares the RT with and without dehazing, giving quantitative evidence that such an advanced driving assistance system (ADAS) may improve road safety. Then, based on a modified Piéron's law, a quantitative model is proposed, linking the RT to the target visibility (Vt), which can be computed from onboard camera images. Two additional experiments have been conducted, giving evidence that the proposed RT model, computed from Vt, is robust with respect to contextual cues, to contrast polarity and to population sample. The authors finally propose to use this predictive model to switch on/off the proposed HUD-based ADAS.
ISSN:1751-956X
1751-9578
1751-9578
DOI:10.1049/iet-its.2014.0101