Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/9021
Title: Intelligent driver behavior and emotion detection system
Authors: Lau, Chi-Yat 
Dr. YUEN Man-Ching, Connie 
Yung, Chi-Wai 
Yeung, Ka Ho 
Chung, Ho Yin 
Ngai, Ho Wang 
Wong, Chau Wai 
Wong, Ching Yee 
Li, Alan 
Issue Date: 2023
Publisher: IOS Press
Source: Lau, Chi Yat, Yuen, Man Ching, Yung, Chi Wai, Yeung, Ka Ho, Chung, Ho Yin, Ngai, Ho Wang, Wong, Chau Wai, Wong, Ching Yee & Li, Alan (2023). Intelligent driver behavior and emotion detection system. In Tallón-Ballesteros, A.J.& Beltrán-Barba, R. (Eds.). Fuzzy systems and data mining IX : Proceedings of FSDM 2023. FSDM 2023, Chongqing, China (pp. 858-866). IOS Press.
Conference: The 9th International Conference on Fuzzy Systems and Data Mining 
Abstract: According to the road traffic injuries fact sheet from the World Health Organization, approximately 1.3 million people die each year because of road traffic crashes. There are many risk factors and one of the most important is distracted driving, especially caused by using a mobile phone. The fact sheet shows that drivers using mobile phones are approximately 4 times more likely to be involved in a crash than drivers not using a mobile phone. Apart from this, dangerous driving behaviors and driver emotions are growing concerns for road safety. This project aims to develop a model for detecting dangerous driving behaviors and analyzing drivers’ emotions in order to avoid and minimize traffic accidents. We focus on detecting several distractive behaviors, bad driving practices, and abnormal emotions. We carry out experiments to evaluate the system performance through the integration of our system with existing infrastructure. To the best of our knowledge, we design and develop the first small and portable device to collect data from drivers, then the collected data is transferred to the cloud for further analysis. The trial service is already available for drivers for a few months in Hong Kong.
Type: Conference Paper
URI: http://hdl.handle.net/20.500.11861/9021
ISBN: 9781643684703
9781643684710
DOI: 10.3233/FAIA231097
Appears in Collections:Applied Data Science - Publication

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