Detection of Cognitive and Visual Distraction Using Radial Basis Probabilistic Neural Networks

This paper suggests a real-time method for detecting a driver’s cognitive and visual distraction using lateral driving performance measures. The algorithm adopts radial basis probabilistic neural networks (RBPNNs) to construct classification models. In this study, combinations of two driving performance data measures, including the standard deviation of lane position (SDLP) and steering wheel reversal rate (SRR), were considered as measures of distraction. Data for training and testing the RBPNN models were collected under simulated conditions in which fifteen participants drove on a highway. While driving, they were asked to complete auditory recall tasks or arrow search tasks to create cognitively or visually distracted driving periods. As a result, the best performing model could detect distraction with an average accuracy of 78.0 %, which is a relatively high accuracy in the human factors domain. The results demonstrated that the RBPNN model using SDLP and SRR could be an effective distraction detector with easy-to-obtain and inexpensive inputs.

[1]  T A Ramney,et al.  Driver distraction: a review of the current state-of-the-knowledge , 2008 .

[2]  Jing Zhang,et al.  Driver cognitive workload estimation: a data-driven perspective , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[3]  Johan Engström,et al.  Effects of visual and cognitive load in real and simulated motorway driving , 2005 .

[4]  Joonwoo Son,et al.  Impact of Cognitive Workload on Physiological Arousal and Performance in Younger and Older Drivers , 2017 .

[5]  Joonwoo Son,et al.  Impact of traffic environment and cognitive workload on older drivers’ behavior in simulated driving , 2011 .

[6]  Hao Yu,et al.  Advantages of Radial Basis Function Networks for Dynamic System Design , 2011, IEEE Transactions on Industrial Electronics.

[7]  John D. Lee,et al.  Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines , 2007, IEEE Transactions on Intelligent Transportation Systems.

[8]  Miguel Ángel Sotelo,et al.  Real-time system for monitoring driver vigilance , 2004, Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005..

[9]  Birsen Donmez,et al.  Associations of distraction involvement and age with driver injury severities. , 2015, Journal of safety research.

[10]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[11]  Dot Hs Driver Distraction: A Review of the Current State-of-Knowledge , 2008 .

[12]  Joonwoo Son,et al.  Cognitive Workload Estimation through Lateral Driving Performance , 2011 .

[13]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.

[14]  Hojjat Adeli,et al.  Enhanced probabilistic neural network with local decision circles: A robust classifier , 2010, Integr. Comput. Aided Eng..