Context-Aware Driver Distraction Severity Classification using LSTM Network
Fasanmade, A., Aliyu, S., He, Y., Al-Bayatti, A. H., Sharif, S. and Alfakeeh, A. S. 2019. Context-Aware Driver Distraction Severity Classification using LSTM Network. in: Proceedings: 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE) IEEE.
|Authors||Fasanmade, A., Aliyu, S., He, Y., Al-Bayatti, A. H., Sharif, S. and Alfakeeh, A. S.|
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 orientation 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
|Keywords||Context awareness; Driver Distraction; Severity prediction; dynamic Bayesian networks (DBN); LSTM networks; time series|
|Book title||Proceedings: 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE)|
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|Event||IEEE International Conference on Computing, Electronics & Communications Engineering 2019 (IEEE iCCECE '19)|
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