PMID- 31381433
OWN - NLM
STAT- MEDLINE
DCOM- 20200203
LR  - 20200203
IS  - 1538-957X (Electronic)
IS  - 1538-9588 (Linking)
VI  - 20
IP  - sup1
DP  - 2019
TI  - The detection of drowsiness using a driver monitoring system.
PG  - S157-S161
LID - 10.1080/15389588.2019.1622005 [doi]
AB  - Objective: Drowsiness is a major cause of driver impairment leading to crashes 
      and fatalities. Research has established the ability to detect drowsiness with 
      various kinds of sensors. We studied drowsy driving in a high-fidelity driving 
      simulator and evaluated the ability of an automotive production-ready driver 
      monitoring system (DMS) to detect drowsy driving. Additionally, this feature was 
      compared to and combined with signals from vehicle-based sensors. Methods: The 
      National Advanced Driving Simulator was used to expose drivers to long, 
      monotonous drives. Twenty participants drove for about 4 h in the simulator 
      between 10 p.m. and 2 a.m. They were allowed to use cruise control and traffic 
      was sparse and semirandom, with both slower- and faster-moving vehicles. 
      Observational ratings of drowsiness (ORDs) were used as the ground truth for 
      drowsiness, and several dependent measures were calculated from vehicle and DMS 
      signals. Drowsiness classification models were created that used only vehicle 
      signals, only driver monitoring signals, and a combination of the 2 sources. 
      Results: The model that used DMS signals performed better than the one that used 
      only vehicle signals; however, the combination of the two performed the best. The 
      models were effective at discriminating low levels of drowsiness from moderate to 
      severe drowsiness; however, they were not effective at telling the difference 
      between moderate and severe levels. A binary model that lumped drowsiness into 2 
      classes had an area under the receiver operating characteristic (ROC) curve of 
      0.897. Conclusions: Blinks and saccades have been shown to be predictive of 
      microsleeps; however, it may be that detection of microsleeps and lane departures 
      occurs too late. Therefore, it is encouraging that the model was able to 
      distinguish mild from moderate drowsy driving. The use of automation may make 
      vehicle-based signals useless for characterizing driver states, providing further 
      motivation for a DMS. Future improvements in impairment detection systems may be 
      expected through a combination of improved hardware, physiological measures from 
      unobtrusive sensors and wearables, and the intelligent integration of 
      environmental variables like time of day and time on task.
FAU - Schwarz, Chris
AU  - Schwarz C
AUID- ORCID: 0000-0003-0291-7157
AD  - a National Advanced Driving Simulator, University of Iowa , Iowa City , Iowa.
FAU - Gaspar, John
AU  - Gaspar J
AD  - a National Advanced Driving Simulator, University of Iowa , Iowa City , Iowa.
FAU - Miller, Thomas
AU  - Miller T
AD  - b Advanced Safety-ADAS, Aisin Technical Center of America , Northville , 
      Michigan.
FAU - Yousefian, Reza
AU  - Yousefian R
AD  - b Advanced Safety-ADAS, Aisin Technical Center of America , Northville , 
      Michigan.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - Traffic Inj Prev
JT  - Traffic injury prevention
JID - 101144385
SB  - IM
MH  - Adolescent
MH  - Adult
MH  - Aged
MH  - Algorithms
MH  - Automation
MH  - Automobile Driving/*psychology
MH  - Computer Simulation
MH  - Female
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - Monitoring, Physiologic/*instrumentation/methods
MH  - Wakefulness/*physiology
MH  - Young Adult
OTO - NOTNLM
OT  - Drowsiness
OT  - driver modeling
OT  - driving simulator
OT  - drowsy driving
OT  - machine learning
EDAT- 2019/08/06 06:00
MHDA- 2020/02/06 06:00
CRDT- 2019/08/06 06:00
PHST- 2019/08/06 06:00 [entrez]
PHST- 2019/08/06 06:00 [pubmed]
PHST- 2020/02/06 06:00 [medline]
AID - 10.1080/15389588.2019.1622005 [doi]
PST - ppublish
SO  - Traffic Inj Prev. 2019;20(sup1):S157-S161. doi: 10.1080/15389588.2019.1622005.