Driving Safer: A Look at How Men and Women Use Advanced Driver Assistance Systems

Objective — This study investigates the nuanced landscape of gender-specific patterns in utilization of Advanced Driver Assistance Systems (ADAS), aiming to contribute insights to enhance road safety. Methods — Leveraging data from diverse sources, including Scopus, Web of Science, and Google Scholar, the method employed involves a meticulous examination of studies exploring gender differences in driving behaviors and ADAS adoption. Key factors influencing these differences, were analyzed including age, experience, and attitudes toward technology. Cultural and societal influences on gender-specific driving behaviors are explored, shedding light on the intricate interplay of perceptions and expectations. Results — The review identifies challenges in ADAS adoption, including limited awareness, misconceptions, and resistance to change. Opportunities for improvement are then outlined, encompassing comprehensive user training, clear communication of ADAS features, customizable settings, and incentives for ADAS-equipped vehicles. This study showed that men tend towards aggressive driving behaviors, while women prioritize caution and adherence to traffic rules. Women propensity for safety-oriented features such as lane-keeping assistance, while men display a preference for convenience-focused features such as adaptive cruise control and parking assistance. Conclusion — By addressing challenges and embracing opportunities, the study advocates for a future where ADAS technologies contribute significantly to safer and more equitable roadways for all drivers.

Authors

Institutions

Publication Details

Journal
Russian Open Medical Journal
Published
2024-12-30
DOI
https://doi.org/10.15275/rusomj.2024.0404
Citations
3
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
1.93
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article

Driving Safer: A Look at How Men and Women Use Advanced Driver Assistance Systems

M. Ghasemzadeh, Alireza Razzaghi, Kiavash Hushmandi, Salman Daneshi et al.
3 citations
Russian Open Medical Journal
Human-Automation Interaction and Safety
1.93
article

Driving Safer: A Look at How Men and Women Use Advanced Driver Assistance Systems

M. Ghasemzadeh, Alireza Razzaghi, Kiavash Hushmandi, Salman Daneshi, Abolfazl Afshari, Alireza Azadnia, Shiva Yazdani
article en
3 citations

Abstract

Objective — This study investigates the nuanced landscape of gender-specific patterns in utilization of Advanced Driver Assistance Systems (ADAS), aiming to contribute insights to enhance road safety. Methods — Leveraging data from diverse sources, including Scopus, Web of Science, and Google Scholar, the method employed involves a meticulous examination of studies exploring gender differences in driving behaviors and ADAS adoption. Key factors influencing these differences, were analyzed including age, experience, and attitudes toward technology. Cultural and societal influences on gender-specific driving behaviors are explored, shedding light on the intricate interplay of perceptions and expectations. Results — The review identifies challenges in ADAS adoption, including limited awareness, misconceptions, and resistance to change. Opportunities for improvement are then outlined, encompassing comprehensive user training, clear communication of ADAS features, customizable settings, and incentives for ADAS-equipped vehicles. This study showed that men tend towards aggressive driving behaviors, while women prioritize caution and adherence to traffic rules. Women propensity for safety-oriented features such as lane-keeping assistance, while men display a preference for convenience-focused features such as adaptive cruise control and parking assistance. Conclusion — By addressing challenges and embracing opportunities, the study advocates for a future where ADAS technologies contribute significantly to safer and more equitable roadways for all drivers.

Russian Open Medical JournalVol. 13(4)
Yazd University (IR), Baqiyatallah University of Medical Sciences (IR), Western University (CA), University of Jiroft (IR), Sharif University of Technology (IR), George Mason University (US), Qazvin University of Medical Sciences (IR)
Openalex Percentile: Top 13%
Human-Automation Interaction and Safety
1.93
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