Citation

Le AS, Xuan NH, Aoki H. Traffic Injury Prev. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Informa - Taylor and Francis Group)

DOI

10.1080/15389588.2022.2051019

PMID

35333671

Abstract

OBJECTIVE: A driver's internal state is a critical factor influencing driving performance, especially in cases of surprise or shock in response to unexpected incidents while driving. This study was designed to investigate the brain activity of a senior driver in response to simulated unexpected vehicle motion, compared with a relaxed state and normal driving.

METHODS: To accomplish this, we created a driving simulator paradigm wherein participants were involved in one of the following three scenarios: sitting down and relaxing, normal driving around the city with traffic signals and other vehicles, and the exiting of a parking area. In the scenario where the driver was to exit the parking area, the gear was switched automatically by the CarMaker software without the driver being notified, leading to an unexpected condition. The driver's internal states were classified by artificial intelligence, based on information obtained through four-channel near-infrared spectroscopy.

RESULTS: Significant differences were observed between the hemodynamic responses obtained in the three conditions. Ultimately, this method can be used to update advanced driver assistance systems, with a view to preventing future traffic accidents, by activating in-vehicle safety functions based on the driver's condition.

CONCLUSIONS: A driver's internal states in a panic situation while driving can be detected using near-infrared spectroscopy and artificial intelligence.

Language: en

Keywords

machine learning; ADAS; NIRS; Panic; unexpected conditions