Sensor selection for maneuver classification

Kari Torkkola; Srihari Venkatesan; Huan Liu

Sensor selection for maneuver classification

Kari Torkkola, Srihari Venkatesan, Huan Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

To determine when to present information from various devices or services to the driver of an automobile, it is necessary to determine whether a driver is engaged in a difficult driving situation that requires extensive attention. We present simulator experiments in determining what sensors make classification of driving states into such maneuvers possible, using various machine learning techniques. Our findings indicate that a small number of derived sensor signals can accomplish the task.

Original language	English (US)
Title of host publication	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Pages	636-641
Number of pages	6
State	Published - 2004
Event	Proceedings - 7th International IEEE Conference on Intelligent Transportation Systems, ITSC 2004 - Washington, DC, United States Duration: Oct 3 2004 → Oct 6 2004

Other

Other	Proceedings - 7th International IEEE Conference on Intelligent Transportation Systems, ITSC 2004
Country/Territory	United States
City	Washington, DC
Period	10/3/04 → 10/6/04

ASJC Scopus subject areas

General Engineering

Cite this

@inproceedings{637db3964d0e48a094fc8569f968f334,

title = "Sensor selection for maneuver classification",

abstract = "To determine when to present information from various devices or services to the driver of an automobile, it is necessary to determine whether a driver is engaged in a difficult driving situation that requires extensive attention. We present simulator experiments in determining what sensors make classification of driving states into such maneuvers possible, using various machine learning techniques. Our findings indicate that a small number of derived sensor signals can accomplish the task.",

author = "Kari Torkkola and Srihari Venkatesan and Huan Liu",

year = "2004",

language = "English (US)",

pages = "636--641",

booktitle = "IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC",

note = "Proceedings - 7th International IEEE Conference on Intelligent Transportation Systems, ITSC 2004 ; Conference date: 03-10-2004 Through 06-10-2004",

}

TY - GEN

T1 - Sensor selection for maneuver classification

AU - Torkkola, Kari

AU - Venkatesan, Srihari

AU - Liu, Huan

PY - 2004

Y1 - 2004

N2 - To determine when to present information from various devices or services to the driver of an automobile, it is necessary to determine whether a driver is engaged in a difficult driving situation that requires extensive attention. We present simulator experiments in determining what sensors make classification of driving states into such maneuvers possible, using various machine learning techniques. Our findings indicate that a small number of derived sensor signals can accomplish the task.

AB - To determine when to present information from various devices or services to the driver of an automobile, it is necessary to determine whether a driver is engaged in a difficult driving situation that requires extensive attention. We present simulator experiments in determining what sensors make classification of driving states into such maneuvers possible, using various machine learning techniques. Our findings indicate that a small number of derived sensor signals can accomplish the task.

UR - http://www.scopus.com/inward/record.url?scp=13344268935&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=13344268935&partnerID=8YFLogxK

M3 - Conference contribution

SP - 636

EP - 641

BT - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

T2 - Proceedings - 7th International IEEE Conference on Intelligent Transportation Systems, ITSC 2004

Y2 - 3 October 2004 through 6 October 2004

ER -

Sensor selection for maneuver classification

Abstract

Other

ASJC Scopus subject areas

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