Real-Time Distraction Detection from Driving Data Based Personal Driving Model Using Deep Learning

Distracted driving is one of the main cause of traffic accidents. Car manufacurers are now developing various driving support systems to ensure safe driving because it is an important activity of people as their major means of transportation. In this work, we have examined the method of detecting distracted driving from the driving data collected from different sensors attached to a driving simulator while driving with various road conditions and cognitive loads. In our study, we used a driving simulator for collecting data of drivers while driving in normal state with concentration and in distracted state by imposing cognitive load to simulate cognitive distraction. Based on the collected data, we developed driver specific model of driving behaviour in several scenario with increasing cognitive load and attempted to detect distracted driving in real time from the individual driving model to send alert to the driver. We explored machine learning algorithms including deep neural networks for the proposed development of real time cognitive distraction detection method from driving data. It is found that different drivers have different driving behaviour and use of personal driving model is important for the detection of distracted driving in real time. It is also found that convolutional neural network (CNN) is a promising tool for the development of a personalized driving assistance system which can detect distracted driving for alerting a driver in real time.