TY - GEN
T1 - Chatbot and Fatigued Driver
T2 - 2024 CHI Conference on Human Factors in Computing Sytems, CHI EA 2024
AU - Huang, Shaoshuai
AU - Zhao, Xuandong
AU - Wei, Dapeng
AU - Song, Xinheng
AU - Sun, Yuanbo
N1 - Publisher Copyright:
© 2024 Association for Computing Machinery. All rights reserved.
PY - 2024/5/11
Y1 - 2024/5/11
N2 - This study explores the application of Large Language Model (LLM)- based voice assistants, such as ChatGPT-4, in mitigating passive driving fatigue, while aiming to enhance driving performance and safety. Employing an empirical approach with a driving simulator and the LLM-based voice assistant "Driver Mate," the study focuses on conversation complexity and frequency as independent variables, and utilizes electroencephalogram (EEG), driving simulator data, and scales for measures. The findings reveal the effectiveness of in-vehicle LLM-based voice assistants, highlighting that low-complexity, high-frequency conversation is optimal for driver alertness and acceptance, while low-complexity, low-frequency interactions significantly improve driving performance. This study innovatively investigates the role of LLM-based voice assistants in alleviating driving fatigue, offering practical suggestions for future in-vehicle systems.
AB - This study explores the application of Large Language Model (LLM)- based voice assistants, such as ChatGPT-4, in mitigating passive driving fatigue, while aiming to enhance driving performance and safety. Employing an empirical approach with a driving simulator and the LLM-based voice assistant "Driver Mate," the study focuses on conversation complexity and frequency as independent variables, and utilizes electroencephalogram (EEG), driving simulator data, and scales for measures. The findings reveal the effectiveness of in-vehicle LLM-based voice assistants, highlighting that low-complexity, high-frequency conversation is optimal for driver alertness and acceptance, while low-complexity, low-frequency interactions significantly improve driving performance. This study innovatively investigates the role of LLM-based voice assistants in alleviating driving fatigue, offering practical suggestions for future in-vehicle systems.
KW - Driving fatigue
KW - Driving performance
KW - Invehicle systems
KW - LLM
KW - Voice assistant
UR - http://www.scopus.com/inward/record.url?scp=85194141492&partnerID=8YFLogxK
U2 - 10.1145/3613905.3651031
DO - 10.1145/3613905.3651031
M3 - Conference contribution
AN - SCOPUS:85194141492
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2024 - Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Sytems
PB - Association for Computing Machinery
Y2 - 11 May 2024 through 16 May 2024
ER -