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ThinkMind // ACCSE 2016, The First International Conference on Advances in Computation, Communications and Services // View article accse_2016_2_10_90022


Real-Time Detection and Classification of Driver Distraction Using Lateral Control Performance

Authors:
Joonwoo Son
Myoungouk Park

Keywords: driver distraction; distraction classsification; driving performance; machine learning; neural network.

Abstract:
This paper suggests a real-time method for detecting both visual and cognitive distraction using lateral control performance measures including standard deviation of lane position (SDLP) and steering wheel reversal rate (SRR). The proposed method adopts neural networks to construct detection models. Data for training and testing the models were collected in a driving simulator in which fifteen participants drove through a highway. They were asked to complete either visual tasks or cognitive tasks while driving to create distracted driving periods. As a result, the best performing model could detect distraction with an average accuracy of 93.1%.

Pages: 13 to 15

Copyright: Copyright (c) IARIA, 2016

Publication date: May 22, 2016

Published in: conference

ISSN: 2519-8459

ISBN: 978-1-61208-481-7

Location: Valencia, Spain

Dates: from May 22, 2016 to May 26, 2016

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