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A Comprehensive Review of Driver Behavior Analysis Utilizing Smartphones

Lookup NU author(s): Teck CHAN, Professor Cheng Chin, Hao Chen

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Abstract

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.

Publication metadata

Author(s): Chan TK, Chin CS, Chen H, Zhong XH

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Intelligent Transportation Systems

Year: 2020

Volume: 21

Issue: 10

Pages: 4444-4475

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

Publisher: IEEE

URL: https://doi.org/10.1109/TITS.2019.2940481

DOI: 10.1109/TITS.2019.2940481

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