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Lookup NU author(s): Teck CHAN,
Dr Cheng Chin,
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Human factors are the primary catalyst for traffic accidents. Among different factors, fatigue, distraction, drunkenness, and/or recklessness are the most common types of abnormal driving behavior that leads to an accident. With technological advances, modern smartphones have the capabilities for driving behavior analysis. There has not yet been a comprehensive review on methodologies utilizing only a smartphone for drowsiness detection and abnormal driver behavior detection. In this paper, different methodologies proposed by different authors are discussed. It includes the sensing schemes, detection algorithms, and their corresponding accuracy and limitations. Challenges and possible solutions such as integration of the smartphone behavior classification system with the concept of context-aware, mobile crowdsensing, and active steering control are analyzed. The issue of model training and updating on the smartphone and cloud environment is also included.
Author(s): Chan TK, Chin CS, Chen H, Zhong XH
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Intelligent Transportation Systems
Print publication date: 02/10/2020
Online publication date: 19/09/2019
Acceptance date: 06/09/2019
ISSN (print): 1524-9050
ISSN (electronic): 1558-0016
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