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Driver Inattention Monitoring System for Intelligent Vehicles: A Review

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4 Author(s)
Yanchao Dong ; Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan ; Zhencheng Hu ; Keiichi Uchimura ; Nobuki Murayama

In this paper, we review the state-of-the-art technologies for driver inattention monitoring, which can be classified into the following two main categories: 1) distraction and 2) fatigue. Driver inattention is a major factor in most traffic accidents. Research and development has actively been carried out for decades, with the goal of precisely determining the drivers' state of mind. In this paper, we summarize these approaches by dividing them into the following five different types of measures: 1) subjective report measures; 2) driver biological measures; 3) driver physical measures; 4) driving performance measures; and 5) hybrid measures. Among these approaches, subjective report measures and driver biological measures are not suitable under real driving conditions but could serve as some rough ground-truth indicators. The hybrid measures are believed to give more reliable solutions compared with single driver physical measures or driving performance measures, because the hybrid measures minimize the number of false alarms and maintain a high recognition rate, which promote the acceptance of the system. We also discuss some nonlinear modeling techniques commonly used in the literature.

Published in:

IEEE Transactions on Intelligent Transportation Systems  (Volume:12 ,  Issue: 2 )