Driver identification in intelligent vehicle systems using machine learning algorithms. Issue 1 (20th June 2018)

• Record Type: Journal Article
• Authors: Li, Zhengping; Zhang, Kai; Chen, Bokui; Dong, Yuhan; Zhang, Lin
• Is Part Of: IET intelligent transport systems. Volume 13:Issue 1(2019)
• Page Start: 40
• Page End: 47
• Publication Date: 2018-06-20
• Subjects: learning (artificial intelligence) -- intelligent transportation systems -- feature extraction -- traffic engineering computing -- image classification -- calibration -- statistical analysis; RFs algorithm -- random forests algorithm -- multiple classifier system -- supervised classification algorithms -- data calibration method -- sensor installation -- raw 3‐axis sensor data -- interpretable statistics feature extraction -- data transformation -- lateral accelerations -- longitudinal accelerations -- vertical accelerations -- 3‐axis accelerometer -- driving data collection -- machine learning algorithms -- intelligent vehicle systems -- driver identification method; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2017.0254
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : This study proposes an applicable driver identification method using machine learning algorithms with driving information. The driving data are collected by a 3‐axis accelerometer, which records the lateral, longitudinal and vertical accelerations. In this research, a data transformation way is developed to extract interpretable statistics features from raw 3‐axis sensor data and utilise machine learning algorithms to identify drivers. To eliminate the bias caused by the sensor installation and ensure the applicability of their approach, they present a data calibration method which proves to be necessary for a comparative test. Four basic supervised classification algorithms are used to perform on the data set for comparison. To improve classification performance, they propose a multiple classifier system, which combines the outputs of several classifiers. Experimental results based on real‐world data show that the proposed algorithm is effective on solving driver identification problem. Among the four basic algorithms, random forests (RFs) algorithm has the greatest performance on accuracy, recall and precision. With the proposed multiple classifier system, a greater performance can be achieved in small number of drivers' groups. RFs algorithm takes the lead in running speed. In their experiment, ten drivers are involved and over 5, 500, 000 driving records per driver are collected.