Non-intrusive drowsiness detection by employing Support Vector Machine

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Abstract

Monitoring the driver's action while during driving by examining the maneuvered of the vehicle can be a very prominent task in order to enhance driving safety. Differentiation between unintentional and intentional car steering wheel movements could be a main key element to detect drowsiness during driving. There is a growth of interests in applying computerised automotive techniques to overcome those safety problems. This paper presents a new method to detect the drowsiness of drivers non-intrusively, which may trigger warning to drivers, so as to prevent accidents and to improve safety on the motorways. This method employs Support Vector Machine (SVM) to train the classifier by using steering wheel angle and distance to outside lane as input parameters to the SVM. All the parameters extracted from vehicle parametrical data collected in a driving simulator. With all considered features, a SVM drowsiness detection model has successfully been constructed.

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