Distracted driver detection using learning representations

With the current market's growing need for electric vehicles and technologies in high-end vehicles, distracted driver detection requires the artificial intelligence's attention. In this paper, new strategies for improving the performance of the driver detection methodology are proposed. The proposed approach consists of two sub-systems namely driver activity detection and driver fatigue detection systems. The former one detects the activities of driver. The latter one is based on the facial feature recognition and determines the driver's fatigue level. The proposed model is evaluated on the activity detection and attained the classification accuracy of 99.69%, compared to the 94.32% accuracy in the state-of-the-art comparison. The KNN classifier had the best accuracy for detecting driver fatigue, with a 76.33% success rate. Experimental results reveal the superiority of proposed model over the existing models. The proposed model can be applied in the real-life environment.

Automated summary

With the increasing demand for electric vehicles and high-end vehicle technologies in the current market, attention is being given to distracted driver detection with the use of artificial intelligence. This paper proposes new strategies to improve the performance of driver detection methodology, including driver activity detection and driver fatigue detection systems. The proposed model achieved a classification accuracy of 99.69% in activity detection, compared to the state-of-the-art comparison's accuracy of 94.32%. The KNN classifier showed the best accuracy of 76.33% in detecting driver fatigue. Experimental results demonstrate the superiority of the proposed model over existing models, making it applicable in real-life environments.