Context-Aware Driver Distraction Severity Classification using LSTM Network

Adebamigbe Fasanmade, Suleiman Aliyu, Ying He, Ali H. Al-Bayatti, Mhd Saeed Sharif, Ahmed S. Alfakeeh
2019 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE)  
Advanced Driving Assistance Systems (ADAS) has been a critical component in vehicles and vital to the safety of vehicle drivers and public road transportation systems. In this paper, we present a deep learning technique that classifies drivers' distraction behaviour using three contextual awareness parameters: speed, manoeuver and event type. Using a video coding taxonomy, we study drivers' distractions based on events information from Regions of Interest (RoI) such as hand gestures, facial
more » ... ntation and eye gaze estimation. Furthermore, a novel probabilistic (Bayesian) model based on the Long shortterm memory (LSTM) network is developed for classifying driver's distraction severity. This paper also proposes the use of frame-based contextual data from the multi-view TeleFOT naturalistic driving study (NDS) data monitoring to classify the severity of driver distractions. Our proposed methodology entails recurrent deep neural network layers trained to predict driver distraction severity from time series data.
doi:10.1109/iccece46942.2019.8941966 fatcat:5vjzlznxwrfrpllebhjndlbr4m