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Personalizing driver safety interfaces via driver cognitive factors inference

Authors :
Emily S. Sumner
Jonathan DeCastro
Jean Costa
Deepak E. Gopinath
Everlyne Kimani
Shabnam Hakimi
Allison Morgan
Andrew Best
Hieu Nguyen
Daniel J. Brooks
Bassam ul Haq
Andrew Patrikalakis
Hiroshi Yasuda
Kate Sieck
Avinash Balachandran
Tiffany L. Chen
Guy Rosman
Source :
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract Recent advances in AI and intelligent vehicle technology hold the promise of revolutionizing mobility and transportation through advanced driver assistance systems (ADAS). Certain cognitive factors, such as impulsivity and inhibitory control have been shown to relate to risky driving behavior and on-road risk-taking. However, existing systems fail to leverage such factors in assistive driving technologies adequately. Varying the levels of these cognitive factors could influence the effectiveness and acceptance of ADAS interfaces. We demonstrate an approach for personalizing driver interaction via driver safety interfaces that are are triggered based on the inference of the driver’s latent cognitive states from their driving behavior. To accomplish this, we adopt a data-driven approach and train a recurrent neural network to infer impulsivity and inhibitory control from recent driving behavior. The network is trained on a population of human drivers to infer impulsivity and inhibitory control from recent driving behavior. Using data collected from a high-fidelity vehicle motion simulator experiment, we demonstrate the ability to deduce these factors from driver behavior. We then use these inferred factors to determine instantly whether or not to engage a driver safety interface. This approach was evaluated using leave-one-out cross validation using actual human data. Our evaluations reveal that our personalized driver safety interface that captures the cognitive profile of the driver is more effective in influencing driver behavior in yellow light zones by reducing their inclination to run through them.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.b91b1dd9f44f49d2be101b53adfb8556
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-024-65144-8