Using Brainwaves and Eye Tracking to Determine Attention Levels for Auto-Lighting Systems
Author(s) -
Junzo Watada,
Yung-chin Hsiao,
Hanayuki Kitagawa
Publication year - 2015
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2015.p0611
Subject(s) - incandescent light bulb , computer science , visibility , light emitting diode , smart lighting , led lamp , automotive engineering , automotive industry , eye tracking , computer vision , sight , human eye , tracking (education) , artificial intelligence , simulation , engineering , architectural engineering , electrical engineering , psychology , optics , pedagogy , physics , aerospace engineering
To prevent car accidents, it should be possible for pedestrians and other drivers to detect oncoming vehicles. Many car accidents are caused because persons are not aware of approaching traffic, and this applies especially to visual awareness. The daytime running light (DRL) and the third braking light (TBL) were developed to significantly increase the visibility of vehicles, and their effectiveness has been verified through numerous studies. Usage of light-emitting diode (LED) lighting technology has also become popular in auto-lighting systems because of its advantages of energy efficiency, long life, and stylish appearance. However, LED lighting technology is very different from conventional incandescent or high-intensity discharge (HID) lighting technology. In this paper, we determine the effectiveness of LEDs as DRLs and TBLs. We measure human attention levels by observing brainwaves and performing eye-tracking experiments that shows the relationship between the theory of attention, brainwaves, and eye tracking. The results obtained show that it is feasible to evaluate automotive exterior lighting using the attention levels of subjects.
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