elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Lethaus, Firas and Baumann, Martin and Köster, Frank and Lemmer, Karsten (2013) A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data. Neurocomputing, 121, pp. 108-130. Elsevier. DOI: 10.1016/j.neucom.2013.04.035 ISSN 0925-2312

Full text not available from this repository.

Official URL: http://www.sciencedirect.com/science/article/pii/S0925231213005705

Abstract

Gaze behaviour is known to indicate information gathering. It is therefore suggested that it could be used to derive information about the driver's next planned objective in order to identify intended manoeuvres without relying solely on car data. Ultimately this would be practically realised by an Advanced Driver Assistance System (ADAS) using gaze data to correctly infer the intentions of the driver from what is implied by the incoming gaze data available to it. Neural Networks' ability to approximate arbitrary functions from observed data therefore makes them a candidate for modelling driver intent. Previous work has shown that significantly distinct gaze patterns precede each of the driving manoeuvres analysed indicating that eye movement data might be used as input to ADAS supplementing sensors, such as CAN-Bus (Controller Area Network), laser, radar or LIDAR (Light Detection and Ranging) in order to recognise intended driving manoeuvres. In this study, drivers' gaze behaviour was measured prior to and during the execution of different driving manoeuvres performed in a dynamic driving simulator. Artificial Neural Networks (ANNs), Bayesian Networks (BNs), and Naive Bayes Classifiers (NBCs) were then trained using gaze data to act as classifiers that predict the occurrence of certain driving manoeuvres. This has previously been successfully demonstrated with real traffic data [1]. Issues considered here included the amount of data that is used for predictive purposes prior to the manoeuvre, the accuracy of the predictive models at different times prior to the manoeuvre taking place and the relative difficulty of predicting a lane change left manoeuvre against predicting a lane change right manoeuvre.

Item URL in elib:https://elib.dlr.de/84190/
Document Type:Article
Title:A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Lethaus, FirasDLRUNSPECIFIED
Baumann, MartinDLRUNSPECIFIED
Köster, FrankDLRUNSPECIFIED
Lemmer, KarstenDLRUNSPECIFIED
Date:2013
Journal or Publication Title:Neurocomputing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:121
DOI :10.1016/j.neucom.2013.04.035
Page Range:pp. 108-130
Publisher:Elsevier
ISSN:0925-2312
Status:Published
Keywords:Artificial Neural Networks, Bayesian Networks, Naive Bayes Classifiers, Driver intent, Eye tracking, Supervised learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Terrestrial Vehicles (old)
DLR - Research area:Transport
DLR - Program:V BF - Bodengebundene Fahrzeuge
DLR - Research theme (Project):V - Projekt Fahrerassistenz (old)
Location: Braunschweig
Institutes and Institutions:Institute of Transportation Systems > Automotive
Deposited By: Lethaus, Firas
Deposited On:11 Sep 2013 09:21
Last Modified:06 Sep 2019 15:18

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.