Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research

Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on...
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Autor*in:	António Lobo [verfasserIn] Sara Ferreira [verfasserIn] António Couto [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	driver distraction drowsiness retrospective data driver monitoring systems

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 20(2020), 14, p 3836
Übergeordnetes Werk:	volume:20 ; year:2020 ; number:14, p 3836

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s20143836

Katalog-ID:	DOAJ025629409

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spelling	10.3390/s20143836 doi (DE-627)DOAJ025629409 (DE-599)DOAJ1f71385d49fc46649049f858af1d54f3 DE-627 ger DE-627 rakwb eng TP1-1185 António Lobo verfasserin aut Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment. driver distraction drowsiness retrospective data driver monitoring systems Chemical technology Sara Ferreira verfasserin aut António Couto verfasserin aut In Sensors MDPI AG, 2003 20(2020), 14, p 3836 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:14, p 3836 https://doi.org/10.3390/s20143836 kostenfrei https://doaj.org/article/1f71385d49fc46649049f858af1d54f3 kostenfrei https://www.mdpi.com/1424-8220/20/14/3836 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 14, p 3836
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allfieldsSound	10.3390/s20143836 doi (DE-627)DOAJ025629409 (DE-599)DOAJ1f71385d49fc46649049f858af1d54f3 DE-627 ger DE-627 rakwb eng TP1-1185 António Lobo verfasserin aut Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment. driver distraction drowsiness retrospective data driver monitoring systems Chemical technology Sara Ferreira verfasserin aut António Couto verfasserin aut In Sensors MDPI AG, 2003 20(2020), 14, p 3836 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:14, p 3836 https://doi.org/10.3390/s20143836 kostenfrei https://doaj.org/article/1f71385d49fc46649049f858af1d54f3 kostenfrei https://www.mdpi.com/1424-8220/20/14/3836 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 14, p 3836
language	English
source	In Sensors 20(2020), 14, p 3836 volume:20 year:2020 number:14, p 3836
sourceStr	In Sensors 20(2020), 14, p 3836 volume:20 year:2020 number:14, p 3836
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	driver distraction drowsiness retrospective data driver monitoring systems Chemical technology
isfreeaccess_bool	true
container_title	Sensors
authorswithroles_txt_mv	António Lobo @@aut@@ Sara Ferreira @@aut@@ António Couto @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
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The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. 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callnumber-first	T - Technology
author	António Lobo
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topic_title	TP1-1185 Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research driver distraction drowsiness retrospective data driver monitoring systems
topic	misc TP1-1185 misc driver distraction misc drowsiness misc retrospective data misc driver monitoring systems misc Chemical technology
topic_unstemmed	misc TP1-1185 misc driver distraction misc drowsiness misc retrospective data misc driver monitoring systems misc Chemical technology
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title	Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research
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title_full	Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research
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title_auth	Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research
abstract	Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment.
abstractGer	Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment.
abstract_unstemmed	Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment.
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title_short	Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research
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