Cited 4 time in webofscience Cited 5 time in scopus

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

Title
Detection of Cognitive and Visual Distraction Using Radial Basis Probabilistic Neural Networks
Authors
Son, JoonwooPark, Myoungouk
DGIST Authors
Son, Joonwoo
Issue Date
2018-10
Citation
International Journal of Automotive Technology, 19(5), 935-940
Type
Article
Article Type
Article
Author Keywords
DistractionDriving performanceCognitive distractionVisual distractionNeural networks
Keywords
REAL
ISSN
1229-9138
Abstract
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. © 2018, The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.
URI
http://hdl.handle.net/20.500.11750/9332
DOI
10.1007/s12239-018-0090-4
Publisher
Korean Society of Automotive Engineers
Related Researcher
Files:
There are no files associated with this item.
Collection:
Companion Diagnostics and Medical Technology Research Group1. Journal Articles


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