Driver workload classification through neural network modeling using physiological indicators

Raymond Hoogendoorn; Bart Van Arem

doi:10.1109/ITSC.2013.6728565

Driver workload classification through neural network modeling using physiological indicators

Raymond Hoogendoorn, Bart Van Arem

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

Abstract

Advanced Driver Assistance Systems may have a positive effect on traffic flow efficiency, the environment, safety and comfort. However these systems may have a negative impact on driving behavior following a change in driver workload. It is therefore crucial to develop a so-called driver workload manager. In order to manage driver workload an adequate classification of driver workload is indispensible. In this contribution we propose to classify and predict driver workload through physiological indicators of driver workload, driver characteristics and characteristics of the driving condition using a neural network modeling approach. We show that the proposed network yields a very good classification of driver workload. The contribution finishes with a discussion section and recommendations for future research.

Original language	English
Title of host publication	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Pages	2268-2273
Number of pages	6
DOIs	https://doi.org/10.1109/ITSC.2013.6728565
Publication status	Published - 2013
Event	IEEE ITSC 2013, The 16th international IEEE annual conference on intelligent transportation systems , The Hague, The Netherlands - Danvers, The Hague, Netherlands Duration: 6 Oct 2013 → 9 Oct 2013 Conference number: 16

Conference

Conference	IEEE ITSC 2013, The 16th international IEEE annual conference on intelligent transportation systems , The Hague, The Netherlands
Abbreviated title	ITSC 2013
Country/Territory	Netherlands
City	The Hague
Period	6/10/13 → 9/10/13

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10.1109/ITSC.2013.6728565

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title = "Driver workload classification through neural network modeling using physiological indicators",

abstract = "Advanced Driver Assistance Systems may have a positive effect on traffic flow efficiency, the environment, safety and comfort. However these systems may have a negative impact on driving behavior following a change in driver workload. It is therefore crucial to develop a so-called driver workload manager. In order to manage driver workload an adequate classification of driver workload is indispensible. In this contribution we propose to classify and predict driver workload through physiological indicators of driver workload, driver characteristics and characteristics of the driving condition using a neural network modeling approach. We show that the proposed network yields a very good classification of driver workload. The contribution finishes with a discussion section and recommendations for future research.",

author = "Raymond Hoogendoorn and {Van Arem}, Bart",

year = "2013",

doi = "10.1109/ITSC.2013.6728565",

language = "English",

isbn = "9781479929146",

pages = "2268--2273",

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

note = "IEEE ITSC 2013, The 16th international IEEE annual conference on intelligent transportation systems , The Hague, The Netherlands, ITSC 2013 ; Conference date: 06-10-2013 Through 09-10-2013",

}

Hoogendoorn, R & Van Arem, B 2013, Driver workload classification through neural network modeling using physiological indicators. in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC., 6728565, pp. 2268-2273, IEEE ITSC 2013, The 16th international IEEE annual conference on intelligent transportation systems , The Hague, The Netherlands, The Hague, Netherlands, 6/10/13. https://doi.org/10.1109/ITSC.2013.6728565

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AU - Hoogendoorn, Raymond

AU - Van Arem, Bart

N1 - Conference code: 16

PY - 2013

Y1 - 2013

N2 - Advanced Driver Assistance Systems may have a positive effect on traffic flow efficiency, the environment, safety and comfort. However these systems may have a negative impact on driving behavior following a change in driver workload. It is therefore crucial to develop a so-called driver workload manager. In order to manage driver workload an adequate classification of driver workload is indispensible. In this contribution we propose to classify and predict driver workload through physiological indicators of driver workload, driver characteristics and characteristics of the driving condition using a neural network modeling approach. We show that the proposed network yields a very good classification of driver workload. The contribution finishes with a discussion section and recommendations for future research.

AB - Advanced Driver Assistance Systems may have a positive effect on traffic flow efficiency, the environment, safety and comfort. However these systems may have a negative impact on driving behavior following a change in driver workload. It is therefore crucial to develop a so-called driver workload manager. In order to manage driver workload an adequate classification of driver workload is indispensible. In this contribution we propose to classify and predict driver workload through physiological indicators of driver workload, driver characteristics and characteristics of the driving condition using a neural network modeling approach. We show that the proposed network yields a very good classification of driver workload. The contribution finishes with a discussion section and recommendations for future research.

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U2 - 10.1109/ITSC.2013.6728565

DO - 10.1109/ITSC.2013.6728565

M3 - Conference contribution

SN - 9781479929146

SP - 2268

EP - 2273

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

T2 - IEEE ITSC 2013, The 16th international IEEE annual conference on intelligent transportation systems , The Hague, The Netherlands

Y2 - 6 October 2013 through 9 October 2013

ER -

Driver workload classification through neural network modeling using physiological indicators

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