Human-Factors-in-Driving-Loop: Driver Identification and Verification via a Deep Learning Approach using Psychological Behavioral Data

Xu, Jiawei, Pan, Sicheng, Sun, Poly Z. H. , Park, Seop Hyeong and Guo, Kun (2023) Human-Factors-in-Driving-Loop: Driver Identification and Verification via a Deep Learning Approach using Psychological Behavioral Data. IEEE Transactions on Intelligent Transportation Systems . pp. 1-12. ISSN 1524-9050

Full content URL: https://doi.org/10.1109/TITS.2022.3225782

Documents
Human-Factors-in-Driving-Loop: Driver Identification and Verification via a Deep Learning Approach using Psychological Behavioral Data
Author's published manuscript

Request a copy
Human-Factors-in-Driving-Loop: Driver Identification and Verification via a Deep Learning Approach using Psychological Behavioral Data
Author's accepted manuscript
[img]
[Download]
[img] PDF
Human-Factors-in-Driving-Loop.pdf - Whole Document
Restricted to Repository staff only

3MB
[img] Microsoft Word
Personal-Driving-Behavior-in-Loop-final (1).docx - Whole Document
Available under License Creative Commons Attribution 4.0 International.

4MB
Item Type:Article
Item Status:Live Archive

Abstract

Driver identification has been popular in the field of driving behavior analysis, which has a broad range of applications in anti-thief, driving style recognition, insurance strategy, and fleet management. However, most studies to date have only researched driver identification without a robust verification stage. This paper addresses driver identification and verification through a deep learning (DL) approach using psychological behavioral data, i.e., vehicle control operation data and eye movement data collected from a driving simulator and an eye tracker, respectively. We design an architecture that analyzes the segmentation windows of three-second data to capture unique driving characteristics and then differentiate drivers on that basis. The proposed model includes a fully convolutional network (FCN) and a squeeze-and-excitation (SE) block. Experimental results were obtained from 24 human participants driving in 12 different scenarios. The proposed driver identification system achieves an accuracy of 99.60% out of 15 drivers. To tackle driver verification, we combine the proposed architecture and a Siamese neural network, and then map all behavioral data into two embedding layers for similarity computation. The identification system achieves significant performance with average precision of 96.91%, recall of 95.80%, F1 score of 96.29%, and accuracy of 96.39%, respectively. Importantly, we scale out the verification system to imposter detection and achieve an average verification accuracy of 90.91%. These results imply the invariable characteristics from human factors rather than other traditional resources, which provides a superior solution for driving behavior authentication systems.

Keywords:Driver identification and verification, driver forensics, human factors in driving loop
Subjects:C Biological Sciences > C830 Experimental Psychology
Divisions:College of Social Science > School of Psychology
Related URLs:
ID Code:52974
Deposited On:30 Jan 2023 09:40

Repository Staff Only: item control page