Adaptive Lane-Departure Prediction Method with Support Vector Machine and Gated Recurrent Unit Models
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 11
Abstract
Ignoring the driver’s corrective actions is the main reason for false warnings in the lane-departure prediction method. We proposed a lane-departure prediction (LDP) method based on a support vector machine (SVM) and improved gated recurrent unit (GRU) models. The driver’s visual distraction state was analyzed by using a radial basis function (RBF) based SVM model. The vehicle’s lateral deviation was predicted by using a GRU model. The characteristic parameters of the GRU model were extracted from vehicle time series data using the time to lane crossing (TLC) model and the vehicle-road model. Considering that the farther the vehicle deviates from the lane centerline, the higher its deviation risk, a lateral deviation risk (LDR) loss function was proposed to improve the accuracy of the GRU model. By combining the SVM model and LDR-GRU model, the proposed LDP method can predict the future trajectory of the vehicle and adaptively adjust the safety boundary according to the driver’s state. Naturalistic driving data from 52 drivers were collected to train and validate the adaptive LDP method. Finally, we compared the proposed LDR-GRU-SVM model with the TLC model, GRU model, LDR-GRU model, and GRU-SVM model. Experimental results show that the TLC model presents the highest false warning rate of 23.1% within a prediction time of 1s, while the proposed method is able to reduce the false warning rate to 1.2%.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request. This data is in the form of some M script and Excel files.
Acknowledgments
This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 51975089 and 52175078), and the Natural Science Foundation Program of Liaoning Province (Grant No. 2021-MS-127).
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 11 • November 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jan 26, 2022
Accepted: Jun 30, 2022
Published online: Sep 14, 2022
Published in print: Nov 1, 2022
Discussion open until: Feb 14, 2023
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