Performance analysis of K-nearest neighbor, support vector machine, and artificial neural network classifiers for driver drowsiness detection with different road geometries

Zhenlong Li, Qingzhou Zhang and Xiaohua Zhao

International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 9, 1550147717733391

Abstract: This article comparatively analyzed the performance of K-nearest neighbor, support vector machine, and artificial neural network classifiers for driver drowsiness detection with different road geometries (straight segments and curve segments) based on a driving simulator. First, vehicle performance measures (speed, acceleration, brake pedal, gas pedal, steering angle, and lateral position) were collected through sensors. These measures were analyzed, and their correlation with drowsiness on different road segments was examined. The analysis was based on data obtained from a study that involved 22 subjects in the driving simulator located in the Traffic Research Center, Beijing University of Technology. Second, six classifiers were constructed for six curve segments, respectively, while only one classifier was constructed for all straight segments because the waveforms by subtracting the road curvature from the steering angle in the curve segments were different from the waveforms of the straight segments. Furthermore, the less the radius of curvature, the more the difference. Third, the performance of K-nearest neighbor, support vector machine, and artificial neural network classifiers were compared and evaluated. The experimental results illustrate that the support vector machine classifier achieved the fastest classification time and the highest accuracy (80.84%). Support vector machine and artificial neural network are effective classification methods for detecting drowsy driving on different road segments.

Keywords: Sensors; driver drowsiness detection; road geometries; pattern classification; performance comparison (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:9:p:1550147717733391

DOI: 10.1177/1550147717733391

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