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Title:

A Real-Time Fatigue Driving Recognition Method Incorporating Contextual Features and Two Fusion Levels

Accession Number:

01623653

Record Type:

Component

Abstract:

Though experimental results have shown a strong correlation between contextual features and driver’s fatigue state, contextual features have been applied only offline to evaluate a driver’s fatigue state. This study identifies three of the most effective contextual features, i.e., continuous driving time, sleep duration time, and current time, to facilitate the real-time (online) recognition of fatigue state. By applying grey relational analysis, the three contextual features, together with the most effective facial and vehicle behavior features, are introduced in a two-level fusion structure to improve fatigue driving recognition. In the first level of fusion, labelled the feature-level fusion, three separate multi-class support vector machine (MCSVM) classifiers are used for the three feature sources, i.e., contextual features, driver’s facial features and vehicle behavior features, to fuse information. These three MCSVM classifiers output probabilities as inputs for the three real-time dynamic basic probability assignments (BPAs) at the second level of fusion, labelled decision-level fusion. These BPAs, and the fusion result of the previous time step, are fused in the decision-level fusion based on Dempster-Shafer evidence theory. This includes modifying the BPAs to accommodate the decision conflict among the different feature sources. Field experiments show that the proposed recognition method can outperform the single-fatigue-feature method and the single-source fusion-based method, in terms of reliably identifying a driver’s fatigue state in real-time.

Supplemental Notes:

This paper was sponsored by TRB committee AND30 Standing Committee on Simulation and Measurement of Vehicle and Operator Performance.

Monograph Accession #:

01618707

Report/Paper Numbers:

17-00375

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Sun, Wei

ORCID 0000-0002-2191-5352

He, Xiaozheng (Sean)
Peeta, Srinivas
Zhang, Xiaorui

Pagination:

19p

Publication Date:

2017

Conference:

Transportation Research Board 96th Annual Meeting

Location: Washington DC, United States
Date: 2017-1-8 to 2017-1-12
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; Maps; References (28) ; Tables

Subject Areas:

Highways; Safety and Human Factors

Source Data:

Transportation Research Board Annual Meeting 2017 Paper #17-00375

Files:

TRIS, TRB, ATRI

Created Date:

Dec 8 2016 10:02AM