Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the research on detection of distracted drivers through pose estimation is scarce. This work introduces an ensemble of ResNets, which is named Optimally-weighted Image-Pose Approach (OWIPA), to classify the distraction through original and pose estimation images. The pose estimation images are generated from HRNet and ResNet. We use ResNet101 and ResNet50 to classify the original images and the pose estimation images, respectively. An optimum weight is determined through grid search method, and the predictions from both models are weighted through this parameter. The experimental results show that our proposed approach achieves 94.28% accuracy on AUC Distracted Driver Dataset.
This study focuses on the application of the Malaysian Driving Behaviour Questionnaire(DBQ). The aim of this study is
to investigate and analyse the significant driving behaviour of the ageing Malaysian automobile drivers. The sets of
questionnaire was completed by a total number of 102 ageing drivers consists of 58 males (56.86%) and 44 females
(43.14%). The age of respondents ranges from 50 to 75 years (M = 57.21) and (SD = 5.60). The DBQ contains 12 items
of demographic questions and 41 items measuring driving behaviour in traffic. The driving behaviours were classified
into four factors which are driving distractions, violations, errors and lapses. The most significant correlation
coefficient is between age and distractions (r = 0.456, p