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

• Main Authors: Zakaria, N. J. (Author), Zamzuri, H. (Author), Mohamed Ariff, M. H. (Author), Azmi, M. Z (Author), Hassan, N. (Author)
• Format: Book
• Published: Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM), 2018.
• Subjects: Article; PeerReviewed
• Online Access: Link Metadata
• Notes: https://ir.uitm.edu.my/id/eprint/40990/1/40990.pdf
• Keywords: 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.