TY  - JOUR
PY  - 2023//
TI  - Sequence analysis of monitored drowsy driving
JO  - Transportation research record
A1  - Schwarz, Chris
A1  - Gaspar, John
A1  - Yousefian, Reza
SP  - 553
EP  - 562
VL  - 2677
IS  - 8
N2  - Driver monitoring systems are growing in importance as well as capability. This paper reports on drowsy driving detection models generated from multiple sources of driver monitoring data. Behavioral (driver) data were provided by a camera-based production-type driver monitoring system manufactured by Aisin Technical Center of America (from the Aisin Group). Vehicular data were recorded from the National Advanced Driving Simulator's large-excursion motion-base driving simulator. Forty participants drove the simulator for up to 3 h after being awake for at least 16 h. Periodic measurements of drowsiness were made every 10 min using both observational ratings of drowsiness by an external rater and the self-reported Karolinska Sleepiness Scale. A novel application of sequence analysis with clustering and hidden Markov models resulted in models that tracked well with the subjective drowsiness measures. The area under Receiver Operating Characteristic curves evaluating the models ranged from 0.85 to 0.87. By allowing for many distinct patterns observed in driving sequences, the hope is that the method will offer a robust way to accommodate pattern variability that naturally occurs over time and among drivers.<p />  <p>Language: en</p>
LA  - en
SN  - 0361-1981
UR  - http://dx.doi.org/10.1177/03611981231157401
ID  - ref1
ER  -