A Method for Driving Distraction Detection Based on OLS-RBF Neural Network Model

In view of the mass of accidents currently caused by distracted driving, research on distracted driving recognition methods was conducted. Driving simulation tests were carried out in which drivers were driving normally, were using a hand-held answering phone, and were using a hand-free answering phone during the lane change process. A radial basis function neural network model based on orthogonal minimum square method for distraction detection was built. Results show that drivers needed more time to complete a lane-change process and made more operation mistakes when he is answering a phone. Six driving performance parameters as vehicle’s longitudinal velocity, lateral velocity, lateral acceleration, steering angle, steering wheel velocity, and opening degree of accelerator, changed significantly and were chosen as input variables for distraction recognition model. The average recognition accuracy of the model reached 88.7%, which could accurately identify if a driver is distracted in real time.