Lane change behaviour recognition using neural entwork / N. J. Zakaria ... [et al.]

Lane change behaviour recognition is one of the significant elements in advanced vehicle active system for the purpose of collision avoidance and traffic flow stability to ensure a safer driving experience. The system recognizes either the driver in situations of normal or evasive lane change maneuv...

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Main Authors: Zakaria, N. J., Zamzuri, H., Mohamed Ariff, M. H., Azmi, M. Z, Hassan, N.
Format: Article
Language:English
Published: Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2018
Subjects:
Online Access:http://ir.uitm.edu.my/id/eprint/40990/1/40990.pdf
http://ir.uitm.edu.my/id/eprint/40990/
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spelling my.uitm.ir.409902021-01-25T05:19:17Z http://ir.uitm.edu.my/id/eprint/40990/ Lane change behaviour recognition using neural entwork / N. J. Zakaria ... [et al.] Zakaria, N. J. Zamzuri, H. Mohamed Ariff, M. H. Azmi, M. Z Hassan, N. Engineering mathematics. Engineering analysis TJ Mechanical engineering and machinery Lane change behaviour recognition is one of the significant elements in advanced vehicle active system for the purpose of collision avoidance and traffic flow stability to ensure a safer driving experience. The system recognizes either the driver in situations of normal or evasive lane change maneuver which respond and assist the driver negligence. This paper proposes a lane change behaviour recognition using Artificial Neural Network (ANN) model by classifying the behaviour either evasive or normal lane change. An ANN model was adopted in order to combine several vehicle state information to generate the lane change behaviour classification. The vehicle state parameters such as vehicle speed, yaw rate, time taken for one complete steer cycle and steering angle were used as the inputs to develop in the ANN model. The state parameters were acquired from a real-time experiment conducted by several selected normal drivers. The result shows that the proposed ANN model has successfully recognized 94% and 92.8% of the lane change samples in training and test data set respectively. Hence, the proposed ANN model has a promising potential to handle system nonlinearity. Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2018 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/40990/1/40990.pdf Zakaria, N. J. and Zamzuri, H. and Mohamed Ariff, M. H. and Azmi, M. Z and Hassan, N. (2018) Lane change behaviour recognition using neural entwork / N. J. Zakaria ... [et al.]. Journal of Mechanical Engineering (JMechE), SI 6 (1). pp. 1-19. ISSN 18235514
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Engineering mathematics. Engineering analysis
TJ Mechanical engineering and machinery
spellingShingle Engineering mathematics. Engineering analysis
TJ Mechanical engineering and machinery
Zakaria, N. J.
Zamzuri, H.
Mohamed Ariff, M. H.
Azmi, M. Z
Hassan, N.
Lane change behaviour recognition using neural entwork / N. J. Zakaria ... [et al.]
description Lane change behaviour recognition is one of the significant elements in advanced vehicle active system for the purpose of collision avoidance and traffic flow stability to ensure a safer driving experience. The system recognizes either the driver in situations of normal or evasive lane change maneuver which respond and assist the driver negligence. This paper proposes a lane change behaviour recognition using Artificial Neural Network (ANN) model by classifying the behaviour either evasive or normal lane change. An ANN model was adopted in order to combine several vehicle state information to generate the lane change behaviour classification. The vehicle state parameters such as vehicle speed, yaw rate, time taken for one complete steer cycle and steering angle were used as the inputs to develop in the ANN model. The state parameters were acquired from a real-time experiment conducted by several selected normal drivers. The result shows that the proposed ANN model has successfully recognized 94% and 92.8% of the lane change samples in training and test data set respectively. Hence, the proposed ANN model has a promising potential to handle system nonlinearity.
format Article
author Zakaria, N. J.
Zamzuri, H.
Mohamed Ariff, M. H.
Azmi, M. Z
Hassan, N.
author_facet Zakaria, N. J.
Zamzuri, H.
Mohamed Ariff, M. H.
Azmi, M. Z
Hassan, N.
author_sort Zakaria, N. J.
title Lane change behaviour recognition using neural entwork / N. J. Zakaria ... [et al.]
title_short Lane change behaviour recognition using neural entwork / N. J. Zakaria ... [et al.]
title_full Lane change behaviour recognition using neural entwork / N. J. Zakaria ... [et al.]
title_fullStr Lane change behaviour recognition using neural entwork / N. J. Zakaria ... [et al.]
title_full_unstemmed Lane change behaviour recognition using neural entwork / N. J. Zakaria ... [et al.]
title_sort lane change behaviour recognition using neural entwork / n. j. zakaria ... [et al.]
publisher Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM)
publishDate 2018
url http://ir.uitm.edu.my/id/eprint/40990/1/40990.pdf
http://ir.uitm.edu.my/id/eprint/40990/
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score 13.211869