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Investigating the Cognitive Response of Brake Lights in Initiating Braking Action Using EEG

Palaniappan, Ramaswamy, Mouli, Surej, Bowman, Howard, McLoughlin, Ian Vince (2021) Investigating the Cognitive Response of Brake Lights in Initiating Braking Action Using EEG. IEEE Transactions on Intelligent Transportation Systems, . pp. 1-6. ISSN 1524-9050. (doi:10.1109/TITS.2021.3091291) (KAR id:88998)

Abstract

Half of all road accidents result from either lack of driver attention or from maintaining insufficient separation between vehicles. Collision from the rear, in particular, has been identified as the most common class of accident in the UK, and its influencing factors have been widely studied for many years. Rear-mounted stop lamps, illuminated when braking, are the primary mechanism to alert following drivers to the need to reduce speed or brake. This paper develops a novel brain response approach to measuring subject reaction to different brake light designs. A variety of off-the-shelf brake light assemblies are tested in a physical simulated driving environment to assess the cognitive reaction times of 22 subjects. Eight pairs of LED-based and two pairs of incandescent bulb-based brake light assemblies are used and electroencephalogram (EEG) data recorded. Channel Pz is utilised to extract the P3 component evoked during the decision making process that occurs in the brain when a participant decides to lift their foot from the accelerator and depress the brake. EEG analysis shows that both incandescent bulb-based lights are statistically slower to evoke cognitive responses than all tested LED-based lights. Between the LED designs, differences are evident, but not statistically significant, attributed to the significant amount of movement artifact in the EEG signal

Item Type: Article
DOI/Identification number: 10.1109/TITS.2021.3091291
Uncontrolled keywords: Brake light reaction time, bulb vs LED brake light, EEG, P300, road safety
Subjects: T Technology > TE Highway engineering. Roads and pavements
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: [37325] UNSPECIFIED
Depositing User: Palaniappan Ramaswamy
Date Deposited: 02 Jul 2021 16:17 UTC
Last Modified: 04 Mar 2024 16:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/88998 (The current URI for this page, for reference purposes)

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