Abstract
Fatigued driving is a major cause of road accidents. For this reason, the method in this paper is based on the steering wheel angles (SWA) and yaw angles (YA) information under real driving conditions to detect drivers’ fatigue levels. It analyzes the operation features of SWA and YA under different fatigue statuses, then calculates the approximate entropy (ApEn) features of a short sliding window on time series. Using the nonlinear feature construction theory of dynamic time series, with the fatigue features as input, designs a “2-6-6-3” multi-level back propagation (BP) Neural Networks classifier to realize the fatigue detection. An approximately 15-h experiment is carried out on a real road, and the data retrieved are segmented and labeled with three fatigue levels after expert evaluation, namely “awake”, “drowsy” and “very drowsy”. The average accuracy of 88.02% in fatigue identification was achieved in the experiment, endorsing the value of the proposed method for engineering applications.
Highlights
As reported by the World Health Organization, car accidents kill more than 1.3 million people worldwide every year [1], of which fatigued driving is a major cause
To obtain the driver fatigue level evaluation and experiment data, this paper selects the expressway from Beijing to Qinhuangdao as the driving route
The fatigue identification system using steering wheel angles (SWA) and yaw angles (YA) time series enjoys higher robustness and reliability, which has been proven by the real road driving test, instead of a lab simulation test
Summary
As reported by the World Health Organization, car accidents kill more than 1.3 million people worldwide every year [1], of which fatigued driving is a major cause. It causes thousands of automobile crashes [2] and about 35–45% of vehicle accidents [3,4]. Fatigued driving usually means the disorder of mental and physical functions after a long-lasting drive, subsequently leading to a weakening of the driver’s ability to control the vehicle. The technology in automatic detection of driver fatigue under real driving conditions is meaningful for reducing road accidents caused by fatigued driving. The existing detection systems for driver fatigue, according to the source of the surveillance data, fall into two categories: intrusive and non-intrusive. Intrusive systems use physiological data of drivers and analyze their rules of change during the driving process, so as to monitor the drivers’
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