HapticV1, PascalFrancis, Corpus, bibRecord, 000D97

Learning haptic feedback for guiding driver behavior

Identifieur interne : 000D97 ( PascalFrancis/Corpus ); précédent : 000D96; suivant : 000D98

Learning haptic feedback for guiding driver behavior

Auteurs : Michael A. Goodrich ; Morgan Quigley

Source :

RBID : Pascal:06-0112037

Descripteurs français

Pascal (Inist)
- Rétroaction, Commande force, Intelligence artificielle, Sensibilité tactile, Automobile, Volant, Interface utilisateur, Algorithme apprentissage, Admittance.

English descriptors

KwdEn :
- Admittance, Artificial intelligence, Feedback regulation, Force control, Learning algorithm, Motor car, Steering wheel, Tactile sensitivity, User interface.

Abstract

Information about the driving state can be conveyed to automobile drivers through force feedback signals sent via the pedals and steering wheel. Because the set of possible haptic signals and driver responses is huge, it is desirable to automatically learn which signals are most useful to drivers. Thus, it is instructive to explore how machine learning techniques can be used as a step in the design of a haptic interface system. In this paper, we present a learning algorithm that learns useful haptic feedback and apply the algorithm to learning feedback for automobile drivers. We present evidence to show that the algorithm is sensitive enough to learn useful feedback under some circumstances, but that its scope may be limited by people's ability to act as admittance controllers.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

A08	`01`	`1`	`ENG`	`@1 Learning haptic feedback for guiding driver behavior`
A09	`01`	`1`	`ENG`	`@1 2004 IEEE international conference on systems, man & cybernetics : The Hague, Netherlands, 10-13 october 2004`
A11	`01`	`1`		`@1 GOODRICH (Michael A.)`
A11	`02`	`1`		`@1 QUIGLEY (Morgan)`
A14	`01`			`@1 Computer Science Department Brigham Young University @2 Provo, UT @3 USA @Z 1 aut. @Z 2 aut.`
A18	`01`	`1`		`@1 IEEE Systems, man, and cybernetics society @3 USA @9 org-cong.`
A20				`@2 vol3, 2507-2512`
A21				`@1 2004`
A23	`01`			`@0 ENG`
A25	`01`			`@1 IEEE @2 Piscataway NJ`
A26	`01`			`@0 0-7803-8566-7`
A30	`01`	`1`	`ENG`	`@1 International Conference on Systems, Man and Cybernetics @3 The Hague NLD @4 2004-10-10`
A43	`01`			`@1 INIST @2 y 38703 @5 354000138711662100`
A44				`@0 0000 @1 © 2006 INIST-CNRS. All rights reserved.`
A45				`@0 6 ref.`
A47	`01`	`1`		`@0 06-0112037`
A60				`@1 C`
A61				`@0 A`
A66	`01`			`@0 USA`
C01	`01`		`ENG`	@0 Information about the driving state can be conveyed to automobile drivers through force feedback signals sent via the pedals and steering wheel. Because the set of possible haptic signals and driver responses is huge, it is desirable to automatically learn which signals are most useful to drivers. Thus, it is instructive to explore how machine learning techniques can be used as a step in the design of a haptic interface system. In this paper, we present a learning algorithm that learns useful haptic feedback and apply the algorithm to learning feedback for automobile drivers. We present evidence to show that the algorithm is sensitive enough to learn useful feedback under some circumstances, but that its scope may be limited by people's ability to act as admittance controllers.
C02	`01`	`X`		`@0 001D02C02`
C02	`02`	`X`		`@0 001D02D`
C03	`01`	`X`	`FRE`	`@0 Rétroaction @5 06`
C03	`01`	`X`	`ENG`	`@0 Feedback regulation @5 06`
C03	`01`	`X`	`SPA`	`@0 Retroacción @5 06`
C03	`02`	`X`	`FRE`	`@0 Commande force @5 07`
C03	`02`	`X`	`ENG`	`@0 Force control @5 07`
C03	`02`	`X`	`SPA`	`@0 Control fuerza @5 07`
C03	`03`	`X`	`FRE`	`@0 Intelligence artificielle @5 08`
C03	`03`	`X`	`ENG`	`@0 Artificial intelligence @5 08`
C03	`03`	`X`	`SPA`	`@0 Inteligencia artificial @5 08`
C03	`04`	`X`	`FRE`	`@0 Sensibilité tactile @5 18`
C03	`04`	`X`	`ENG`	`@0 Tactile sensitivity @5 18`
C03	`04`	`X`	`SPA`	`@0 Sensibilidad tactil @5 18`
C03	`05`	`X`	`FRE`	`@0 Automobile @5 19`
C03	`05`	`X`	`ENG`	`@0 Motor car @5 19`
C03	`05`	`X`	`SPA`	`@0 Automóvil @5 19`
C03	`06`	`X`	`FRE`	`@0 Volant @5 20`
C03	`06`	`X`	`ENG`	`@0 Steering wheel @5 20`
C03	`06`	`X`	`SPA`	`@0 Volante dirección @5 20`
C03	`07`	`X`	`FRE`	`@0 Interface utilisateur @5 21`
C03	`07`	`X`	`ENG`	`@0 User interface @5 21`
C03	`07`	`X`	`SPA`	`@0 Interfase usuario @5 21`
C03	`08`	`X`	`FRE`	`@0 Algorithme apprentissage @5 28`
C03	`08`	`X`	`ENG`	`@0 Learning algorithm @5 28`
C03	`08`	`X`	`SPA`	`@0 Algoritmo aprendizaje @5 28`
C03	`09`	`X`	`FRE`	`@0 Admittance @5 29`
C03	`09`	`X`	`ENG`	`@0 Admittance @5 29`
C03	`09`	`X`	`SPA`	`@0 Admitancia @5 29`
N21				`@1 072`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

Format Inist (serveur)

NO :	PASCAL 06-0112037 INIST
ET :	Learning haptic feedback for guiding driver behavior
AU :	GOODRICH (Michael A.); QUIGLEY (Morgan)
AF :	Computer Science Department Brigham Young University/Provo, UT/Etats-Unis (1 aut., 2 aut.)
DT :	Congrès; Niveau analytique
SO :	International Conference on Systems, Man and Cybernetics/2004-10-10/The Hague NLD; Etats-Unis; Piscataway NJ: IEEE; Da. 2004; vol3, 2507-2512; ISBN 0-7803-8566-7
LA :	Anglais
EA :	Information about the driving state can be conveyed to automobile drivers through force feedback signals sent via the pedals and steering wheel. Because the set of possible haptic signals and driver responses is huge, it is desirable to automatically learn which signals are most useful to drivers. Thus, it is instructive to explore how machine learning techniques can be used as a step in the design of a haptic interface system. In this paper, we present a learning algorithm that learns useful haptic feedback and apply the algorithm to learning feedback for automobile drivers. We present evidence to show that the algorithm is sensitive enough to learn useful feedback under some circumstances, but that its scope may be limited by people's ability to act as admittance controllers.
CC :	001D02C02; 001D02D
FD :	Rétroaction; Commande force; Intelligence artificielle; Sensibilité tactile; Automobile; Volant; Interface utilisateur; Algorithme apprentissage; Admittance
ED :	Feedback regulation; Force control; Artificial intelligence; Tactile sensitivity; Motor car; Steering wheel; User interface; Learning algorithm; Admittance
SD :	Retroacción; Control fuerza; Inteligencia artificial; Sensibilidad tactil; Automóvil; Volante dirección; Interfase usuario; Algoritmo aprendizaje; Admitancia
LO :	INIST-y 38703.354000138711662100
ID :	06-0112037

Links to Exploration step

Pascal:06-0112037

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Learning haptic feedback for guiding driver behavior</title>
<author><name sortKey="Goodrich, Michael A" sort="Goodrich, Michael A" uniqKey="Goodrich M" first="Michael A." last="Goodrich">Michael A. Goodrich</name>
<affiliation><inist:fA14 i1="01"><s1>Computer Science Department Brigham Young University</s1>
<s2>Provo, UT</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
<author><name sortKey="Quigley, Morgan" sort="Quigley, Morgan" uniqKey="Quigley M" first="Morgan" last="Quigley">Morgan Quigley</name>
<affiliation><inist:fA14 i1="01"><s1>Computer Science Department Brigham Young University</s1>
<s2>Provo, UT</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">INIST</idno>
<idno type="inist">06-0112037</idno>
<date when="2004">2004</date>
<idno type="stanalyst">PASCAL 06-0112037 INIST</idno>
<idno type="RBID">Pascal:06-0112037</idno>
<idno type="wicri:Area/PascalFrancis/Corpus">000D97</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Learning haptic feedback for guiding driver behavior</title>
<author><name sortKey="Goodrich, Michael A" sort="Goodrich, Michael A" uniqKey="Goodrich M" first="Michael A." last="Goodrich">Michael A. Goodrich</name>
<affiliation><inist:fA14 i1="01"><s1>Computer Science Department Brigham Young University</s1>
<s2>Provo, UT</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
<author><name sortKey="Quigley, Morgan" sort="Quigley, Morgan" uniqKey="Quigley M" first="Morgan" last="Quigley">Morgan Quigley</name>
<affiliation><inist:fA14 i1="01"><s1>Computer Science Department Brigham Young University</s1>
<s2>Provo, UT</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
</analytic>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Admittance</term>
<term>Artificial intelligence</term>
<term>Feedback regulation</term>
<term>Force control</term>
<term>Learning algorithm</term>
<term>Motor car</term>
<term>Steering wheel</term>
<term>Tactile sensitivity</term>
<term>User interface</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr"><term>Rétroaction</term>
<term>Commande force</term>
<term>Intelligence artificielle</term>
<term>Sensibilité tactile</term>
<term>Automobile</term>
<term>Volant</term>
<term>Interface utilisateur</term>
<term>Algorithme apprentissage</term>
<term>Admittance</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Information about the driving state can be conveyed to automobile drivers through force feedback signals sent via the pedals and steering wheel. Because the set of possible haptic signals and driver responses is huge, it is desirable to automatically learn which signals are most useful to drivers. Thus, it is instructive to explore how machine learning techniques can be used as a step in the design of a haptic interface system. In this paper, we present a learning algorithm that learns useful haptic feedback and apply the algorithm to learning feedback for automobile drivers. We present evidence to show that the algorithm is sensitive enough to learn useful feedback under some circumstances, but that its scope may be limited by people's ability to act as admittance controllers.</div>
</front>
</TEI>
<inist><standard h6="B"><pA><fA08 i1="01" i2="1" l="ENG"><s1>Learning haptic feedback for guiding driver behavior</s1>
</fA08>
<fA09 i1="01" i2="1" l="ENG"><s1>2004 IEEE international conference on systems, man & cybernetics : The Hague, Netherlands, 10-13 october 2004</s1>
</fA09>
<fA11 i1="01" i2="1"><s1>GOODRICH (Michael A.)</s1>
</fA11>
<fA11 i1="02" i2="1"><s1>QUIGLEY (Morgan)</s1>
</fA11>
<fA14 i1="01"><s1>Computer Science Department Brigham Young University</s1>
<s2>Provo, UT</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</fA14>
<fA18 i1="01" i2="1"><s1>IEEE Systems, man, and cybernetics society</s1>
<s3>USA</s3>
<s9>org-cong.</s9>
</fA18>
<fA20><s2>vol3, 2507-2512</s2>
</fA20>
<fA21><s1>2004</s1>
</fA21>
<fA23 i1="01"><s0>ENG</s0>
</fA23>
<fA25 i1="01"><s1>IEEE</s1>
<s2>Piscataway NJ</s2>
</fA25>
<fA26 i1="01"><s0>0-7803-8566-7</s0>
</fA26>
<fA30 i1="01" i2="1" l="ENG"><s1>International Conference on Systems, Man and Cybernetics</s1>
<s3>The Hague NLD</s3>
<s4>2004-10-10</s4>
</fA30>
<fA43 i1="01"><s1>INIST</s1>
<s2>y 38703</s2>
<s5>354000138711662100</s5>
</fA43>
<fA44><s0>0000</s0>
<s1>© 2006 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>6 ref.</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>06-0112037</s0>
</fA47>
<fA60><s1>C</s1>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA66 i1="01"><s0>USA</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>Information about the driving state can be conveyed to automobile drivers through force feedback signals sent via the pedals and steering wheel. Because the set of possible haptic signals and driver responses is huge, it is desirable to automatically learn which signals are most useful to drivers. Thus, it is instructive to explore how machine learning techniques can be used as a step in the design of a haptic interface system. In this paper, we present a learning algorithm that learns useful haptic feedback and apply the algorithm to learning feedback for automobile drivers. We present evidence to show that the algorithm is sensitive enough to learn useful feedback under some circumstances, but that its scope may be limited by people's ability to act as admittance controllers.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>001D02C02</s0>
</fC02>
<fC02 i1="02" i2="X"><s0>001D02D</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE"><s0>Rétroaction</s0>
<s5>06</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG"><s0>Feedback regulation</s0>
<s5>06</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA"><s0>Retroacción</s0>
<s5>06</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE"><s0>Commande force</s0>
<s5>07</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG"><s0>Force control</s0>
<s5>07</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA"><s0>Control fuerza</s0>
<s5>07</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Intelligence artificielle</s0>
<s5>08</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Artificial intelligence</s0>
<s5>08</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Inteligencia artificial</s0>
<s5>08</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Sensibilité tactile</s0>
<s5>18</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Tactile sensitivity</s0>
<s5>18</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Sensibilidad tactil</s0>
<s5>18</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Automobile</s0>
<s5>19</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Motor car</s0>
<s5>19</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Automóvil</s0>
<s5>19</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Volant</s0>
<s5>20</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Steering wheel</s0>
<s5>20</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Volante dirección</s0>
<s5>20</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Interface utilisateur</s0>
<s5>21</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>User interface</s0>
<s5>21</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Interfase usuario</s0>
<s5>21</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Algorithme apprentissage</s0>
<s5>28</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Learning algorithm</s0>
<s5>28</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Algoritmo aprendizaje</s0>
<s5>28</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Admittance</s0>
<s5>29</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Admittance</s0>
<s5>29</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Admitancia</s0>
<s5>29</s5>
</fC03>
<fN21><s1>072</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
<server><NO>PASCAL 06-0112037 INIST</NO>
<ET>Learning haptic feedback for guiding driver behavior</ET>
<AU>GOODRICH (Michael A.); QUIGLEY (Morgan)</AU>
<AF>Computer Science Department Brigham Young University/Provo, UT/Etats-Unis (1 aut., 2 aut.)</AF>
<DT>Congrès; Niveau analytique</DT>
<SO>International Conference on Systems, Man and Cybernetics/2004-10-10/The Hague NLD; Etats-Unis; Piscataway NJ: IEEE; Da. 2004; vol3, 2507-2512; ISBN 0-7803-8566-7</SO>
<LA>Anglais</LA>
<EA>Information about the driving state can be conveyed to automobile drivers through force feedback signals sent via the pedals and steering wheel. Because the set of possible haptic signals and driver responses is huge, it is desirable to automatically learn which signals are most useful to drivers. Thus, it is instructive to explore how machine learning techniques can be used as a step in the design of a haptic interface system. In this paper, we present a learning algorithm that learns useful haptic feedback and apply the algorithm to learning feedback for automobile drivers. We present evidence to show that the algorithm is sensitive enough to learn useful feedback under some circumstances, but that its scope may be limited by people's ability to act as admittance controllers.</EA>
<CC>001D02C02; 001D02D</CC>
<FD>Rétroaction; Commande force; Intelligence artificielle; Sensibilité tactile; Automobile; Volant; Interface utilisateur; Algorithme apprentissage; Admittance</FD>
<ED>Feedback regulation; Force control; Artificial intelligence; Tactile sensitivity; Motor car; Steering wheel; User interface; Learning algorithm; Admittance</ED>
<SD>Retroacción; Control fuerza; Inteligencia artificial; Sensibilidad tactil; Automóvil; Volante dirección; Interfase usuario; Algoritmo aprendizaje; Admitancia</SD>
<LO>INIST-y 38703.354000138711662100</LO>
<ID>06-0112037</ID>
</server>
</inist>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/HapticV1/Data/PascalFrancis/Corpus

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000D97 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/PascalFrancis/Corpus/biblio.hfd -nk 000D97 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Ticri/CIDE
   |area=    HapticV1
   |flux=    PascalFrancis
   |étape=   Corpus
   |type=    RBID
   |clé=     Pascal:06-0112037
   |texte=   Learning haptic feedback for guiding driver behavior
}}

This area was generated with Dilib version V0.6.23.
Data generation: Mon Jun 13 01:09:46 2016. Site generation: Wed Mar 6 09:54:07 2024

	Serveur d'exploration sur les dispositifs haptiques
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration sur les dispositifs haptiques

Learning haptic feedback for guiding driver behavior

Learning haptic feedback for guiding driver behavior

Source :

Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri