Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components

Escano, JM; Ridao-Olivar, MA; Ierardi, C; Sanchez, AJ; Rouzbehi, K

Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components

Escano, JM Ridao-Olivar, MA Ierardi, C Sanchez, AJ Rouzbehi, K

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2020, 20, (19), pp. 11454-11462
Issue Date:: 2020-10-01

Closed Access

	Filename	Description	Size
	09097169.pdf	Published version	2.74 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Escano, JM
dc.contributor.author	Ridao-Olivar, MA
dc.contributor.author	Ierardi, C
dc.contributor.author	Sanchez, AJ
dc.contributor.author	Rouzbehi, K https://orcid.org/0000-0002-3623-4840
dc.date.accessioned	2020-12-20T04:24:48Z
dc.date.available	2020-12-20T04:24:48Z
dc.date.issued	2020-10-01
dc.identifier.citation	IEEE Sensors Journal, 2020, 20, (19), pp. 11454-11462
dc.identifier.issn	1530-437X
dc.identifier.issn	1558-1748
dc.identifier.uri	http://hdl.handle.net/10453/144865
dc.description.abstract	© 2001-2012 IEEE. This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers' behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the need to add extra hardware (smart phones, cameras, etc.), is a challenge. The main advantage of using only the data center signals of modern vehicles is economical. The classification of the driving behavior and the warning to the driver of dangerous behaviors without the need to add extra hardware (and their software) to the vehicle, would allow the direct integration of these classifiers into the current vehicles without incurring a greater cost in the manufacture of the vehicles and therefore be an added value. In this work, the classification is obtained based only on speed, acceleration and inertial measurements which are already present in many modern vehicles. The proposed algorithm is based on a structure made by several Neurofuzzy systems with the combination of projected data in components of various Principal Component Analysis. A comparison with several types of classical classifying algorithms has been made.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Sensors Journal
dc.relation.isbasedon	10.1109/JSEN.2020.2995921
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	0205 Optical Physics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Analytical Chemistry
dc.title	Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components
dc.type	Journal Article
utslib.citation.volume	20
utslib.for	0205 Optical Physics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-12-20T04:24:43Z
pubs.issue	19
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	19

Abstract:

© 2001-2012 IEEE. This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers' behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the need to add extra hardware (smart phones, cameras, etc.), is a challenge. The main advantage of using only the data center signals of modern vehicles is economical. The classification of the driving behavior and the warning to the driver of dangerous behaviors without the need to add extra hardware (and their software) to the vehicle, would allow the direct integration of these classifiers into the current vehicles without incurring a greater cost in the manufacture of the vehicles and therefore be an added value. In this work, the classification is obtained based only on speed, acceleration and inertial measurements which are already present in many modern vehicles. The proposed algorithm is based on a structure made by several Neurofuzzy systems with the combination of projected data in components of various Principal Component Analysis. A comparison with several types of classical classifying algorithms has been made.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/144865