A pilot study on the effectiveness of text and humanoid voice reminders for drivers in a simulator-based environment

Zhao Zou; Fady Alnajjar; Michael Lwin; Abdullah Al Mahmud; Aila Khan; Muhammed Swavaf; Omar Mubin

doi:10.1109/ABC64332.2025.11118612

A pilot study on the effectiveness of text and humanoid voice reminders for drivers in a simulator-based environment

Zhao Zou, Fady Alnajjar, Michael Lwin, Abdullah Al Mahmud, Aila Khan, Muhammed Swavaf, Omar Mubin

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

Abstract

As vehicle automation levels increase, the interaction between drivers and vehicles becomes significant. This pilot study investigated which type of driving system reminder, Text Display or humanoid Voice Command, provokes stronger preferences and facilitates driver performance. Six participants were recruited for the experiment, and were tasked with completing two rounds of driving sessions. During these sessions, reminders were presented in both text and voice formats, allowing a comparative analysis of their effects on driver performance. Data analysis was performed using mean value comparison, Wilcoxon Signed-Rank test, and Chi-Square test to evaluate user performance, emotion changes, and user satisfaction. The findings show that the participants showed stronger preferences for voice-based driving reminders, which aligns with the observed behaviors of the drivers. Regarding the emotion results, apart from the emotion of surprise, no significant differences were detected among the other emotions. Despite the limitations of a small sample size of participants, this study aims to improve communication between humans and machines through better alert systems for drivers. It also seeks to enhance the field of advanced driver assistant systems by developing more intuitive and responsive interactions between humans and in-vehicle assistants.

Original language	English
Title of host publication	Proceedings of the 7th International Conference on Activity and Behavior Computing: Integrating Vision, Sensors, and AI for Real-World Behavior Analysis, 21-25 April, 2025, Khalifa University, Abu Dhabi, UAE
Place of Publication	U.S.
Publisher	IEEE
Number of pages	9
ISBN (Electronic)	9798331534370
DOIs	https://doi.org/10.1109/ABC64332.2025.11118612
Publication status	Published - 2025
Event	International Conference on Activity and Behavior Computing - Khalifa University, Al Ain, United Arab Emirates Duration: 21 Apr 2025 → 25 Apr 2025 Conference number: 7th

Conference

Conference	International Conference on Activity and Behavior Computing
Abbreviated title	ABC
Country/Territory	United Arab Emirates
City	Al Ain
Period	21/04/25 → 25/04/25

Keywords

advanced driver assistance system (ADAS)
autonomous vehicles
emotion detecting
human-computer interaction

Access to Document

10.1109/ABC64332.2025.11118612

Cite this

Zou, Z., Alnajjar, F., Lwin, M., Al Mahmud, A., Khan, A., Swavaf, M., & Mubin, O. (2025). A pilot study on the effectiveness of text and humanoid voice reminders for drivers in a simulator-based environment. In Proceedings of the 7th International Conference on Activity and Behavior Computing: Integrating Vision, Sensors, and AI for Real-World Behavior Analysis, 21-25 April, 2025, Khalifa University, Abu Dhabi, UAE IEEE. https://doi.org/10.1109/ABC64332.2025.11118612

Zou, Zhao ; Alnajjar, Fady ; Lwin, Michael et al. / A pilot study on the effectiveness of text and humanoid voice reminders for drivers in a simulator-based environment. Proceedings of the 7th International Conference on Activity and Behavior Computing: Integrating Vision, Sensors, and AI for Real-World Behavior Analysis, 21-25 April, 2025, Khalifa University, Abu Dhabi, UAE. U.S. : IEEE, 2025.

@inproceedings{8813bbe788f1495c806f679abf2f9274,

title = "A pilot study on the effectiveness of text and humanoid voice reminders for drivers in a simulator-based environment",

abstract = "As vehicle automation levels increase, the interaction between drivers and vehicles becomes significant. This pilot study investigated which type of driving system reminder, Text Display or humanoid Voice Command, provokes stronger preferences and facilitates driver performance. Six participants were recruited for the experiment, and were tasked with completing two rounds of driving sessions. During these sessions, reminders were presented in both text and voice formats, allowing a comparative analysis of their effects on driver performance. Data analysis was performed using mean value comparison, Wilcoxon Signed-Rank test, and Chi-Square test to evaluate user performance, emotion changes, and user satisfaction. The findings show that the participants showed stronger preferences for voice-based driving reminders, which aligns with the observed behaviors of the drivers. Regarding the emotion results, apart from the emotion of surprise, no significant differences were detected among the other emotions. Despite the limitations of a small sample size of participants, this study aims to improve communication between humans and machines through better alert systems for drivers. It also seeks to enhance the field of advanced driver assistant systems by developing more intuitive and responsive interactions between humans and in-vehicle assistants.",

keywords = "advanced driver assistance system (ADAS), autonomous vehicles, emotion detecting, human-computer interaction",

author = "Zhao Zou and Fady Alnajjar and Michael Lwin and {Al Mahmud}, Abdullah and Aila Khan and Muhammed Swavaf and Omar Mubin",

year = "2025",

doi = "10.1109/ABC64332.2025.11118612",

language = "English",

booktitle = "Proceedings of the 7th International Conference on Activity and Behavior Computing: Integrating Vision, Sensors, and AI for Real-World Behavior Analysis, 21-25 April, 2025, Khalifa University, Abu Dhabi, UAE",

publisher = "IEEE",

note = "International Conference on Activity and Behavior Computing, ABC ; Conference date: 21-04-2025 Through 25-04-2025",

}

Zou, Z, Alnajjar, F, Lwin, M, Al Mahmud, A, Khan, A, Swavaf, M & Mubin, O 2025, A pilot study on the effectiveness of text and humanoid voice reminders for drivers in a simulator-based environment. in Proceedings of the 7th International Conference on Activity and Behavior Computing: Integrating Vision, Sensors, and AI for Real-World Behavior Analysis, 21-25 April, 2025, Khalifa University, Abu Dhabi, UAE. IEEE, U.S., International Conference on Activity and Behavior Computing, Al Ain, United Arab Emirates, 21/04/25. https://doi.org/10.1109/ABC64332.2025.11118612

A pilot study on the effectiveness of text and humanoid voice reminders for drivers in a simulator-based environment. / Zou, Zhao; Alnajjar, Fady; Lwin, Michael et al.
Proceedings of the 7th International Conference on Activity and Behavior Computing: Integrating Vision, Sensors, and AI for Real-World Behavior Analysis, 21-25 April, 2025, Khalifa University, Abu Dhabi, UAE. U.S.: IEEE, 2025.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - A pilot study on the effectiveness of text and humanoid voice reminders for drivers in a simulator-based environment

AU - Zou, Zhao

AU - Alnajjar, Fady

AU - Lwin, Michael

AU - Al Mahmud, Abdullah

AU - Khan, Aila

AU - Swavaf, Muhammed

AU - Mubin, Omar

N1 - Conference code: 7th

PY - 2025

Y1 - 2025

N2 - As vehicle automation levels increase, the interaction between drivers and vehicles becomes significant. This pilot study investigated which type of driving system reminder, Text Display or humanoid Voice Command, provokes stronger preferences and facilitates driver performance. Six participants were recruited for the experiment, and were tasked with completing two rounds of driving sessions. During these sessions, reminders were presented in both text and voice formats, allowing a comparative analysis of their effects on driver performance. Data analysis was performed using mean value comparison, Wilcoxon Signed-Rank test, and Chi-Square test to evaluate user performance, emotion changes, and user satisfaction. The findings show that the participants showed stronger preferences for voice-based driving reminders, which aligns with the observed behaviors of the drivers. Regarding the emotion results, apart from the emotion of surprise, no significant differences were detected among the other emotions. Despite the limitations of a small sample size of participants, this study aims to improve communication between humans and machines through better alert systems for drivers. It also seeks to enhance the field of advanced driver assistant systems by developing more intuitive and responsive interactions between humans and in-vehicle assistants.

AB - As vehicle automation levels increase, the interaction between drivers and vehicles becomes significant. This pilot study investigated which type of driving system reminder, Text Display or humanoid Voice Command, provokes stronger preferences and facilitates driver performance. Six participants were recruited for the experiment, and were tasked with completing two rounds of driving sessions. During these sessions, reminders were presented in both text and voice formats, allowing a comparative analysis of their effects on driver performance. Data analysis was performed using mean value comparison, Wilcoxon Signed-Rank test, and Chi-Square test to evaluate user performance, emotion changes, and user satisfaction. The findings show that the participants showed stronger preferences for voice-based driving reminders, which aligns with the observed behaviors of the drivers. Regarding the emotion results, apart from the emotion of surprise, no significant differences were detected among the other emotions. Despite the limitations of a small sample size of participants, this study aims to improve communication between humans and machines through better alert systems for drivers. It also seeks to enhance the field of advanced driver assistant systems by developing more intuitive and responsive interactions between humans and in-vehicle assistants.

KW - advanced driver assistance system (ADAS)

KW - autonomous vehicles

KW - emotion detecting

KW - human-computer interaction

UR - http://www.scopus.com/inward/record.url?scp=105015529408&partnerID=8YFLogxK

UR - https://go.openathens.net/redirector/westernsydney.edu.au?url=https://doi.org/10.1109/ABC64332.2025.11118612

U2 - 10.1109/ABC64332.2025.11118612

DO - 10.1109/ABC64332.2025.11118612

M3 - Conference Paper

AN - SCOPUS:105015529408

BT - Proceedings of the 7th International Conference on Activity and Behavior Computing: Integrating Vision, Sensors, and AI for Real-World Behavior Analysis, 21-25 April, 2025, Khalifa University, Abu Dhabi, UAE

PB - IEEE

CY - U.S.

T2 - International Conference on Activity and Behavior Computing

Y2 - 21 April 2025 through 25 April 2025

ER -

Zou Z, Alnajjar F, Lwin M, Al Mahmud A, Khan A, Swavaf M et al. A pilot study on the effectiveness of text and humanoid voice reminders for drivers in a simulator-based environment. In Proceedings of the 7th International Conference on Activity and Behavior Computing: Integrating Vision, Sensors, and AI for Real-World Behavior Analysis, 21-25 April, 2025, Khalifa University, Abu Dhabi, UAE. U.S.: IEEE. 2025 doi: 10.1109/ABC64332.2025.11118612

A pilot study on the effectiveness of text and humanoid voice reminders for drivers in a simulator-based environment

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this