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 - Publisher Copyright:
©2025 IEEE.
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 - https://www.scopus.com/pages/publications/105015529408
UR - https://www.scopus.com/pages/publications/105015529408#tab=citedBy
U2 - 10.1109/ABC64332.2025.11118612
DO - 10.1109/ABC64332.2025.11118612
M3 - Conference contribution
AN - SCOPUS:105015529408
T3 - 2025 International Conference on Activity and Behavior Computing, ABC 2025
BT - 2025 International Conference on Activity and Behavior Computing, ABC 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 International Conference on Activity and Behavior Computing, ABC 2025
Y2 - 21 April 2025 through 25 April 2025
ER -