Open Access

ARTICLE

Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

by Yuzhou Li^*, Chuanxia Sun, Yinglei Hu

Information Technology Division, Henan Transportation Development Center, Zhengzhou, 450000, China

* Corresponding Author: Yuzhou Li. Email:

Computers, Materials & Continua 2023, 75(2), 3497-3515. https://doi.org/10.32604/cmc.2023.035878

Received 08 September 2022; Accepted 06 January 2023; Issue published 31 March 2023

Abstract

Driver identification in intelligent transport systems has immense demand, considering the safety and convenience of traveling in a vehicle. The rapid growth of driver assistance systems (DAS) and driver identification system propels the need for understanding the root causes of automobile accidents. Also, in the case of insurance, it is necessary to track the number of drivers who commonly drive a car in terms of insurance pricing. It is observed that drivers with frequent records of paying “fines” are compelled to pay higher insurance payments than drivers without any penalty records. Thus driver identification act as an important information source for the intelligent transport system. This study focuses on a similar objective to implement a machine learning-based approach for driver identification. Raw data is collected from in-vehicle sensors using the controller area network (CAN) and then converted to binary form using a one-hot encoding technique. Then, the transformed data is dimensionally reduced using the Principal Component Analysis (PCA) technique, and further optimal parameters from the dataset are selected using Whale Optimization Algorithm (WOA). The most relevant features are selected and then fed into a Convolutional Neural Network (CNN) model. The proposed model is evaluated against four different use cases of driver behavior. The results show that the best prediction accuracy is achieved in the case of drivers without glasses. The proposed model yielded optimal accuracy when evaluated against the K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) models with and without using dimensionality reduction approaches.

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