| PREDICTION OF DRIVER’S DROWSINESS USING MACHINE LEARNING ALGORITHMS FOR MINIMAL RISK CONDITION
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| Deok Ho Nam 1#, Gyeong Pil Kim 1#, Keon Hee Baek 1, Da Som Lee 1, Ho Yong Lee 2, Myung Won Suh 3 |
1Graduate School of Mechanical Engineering, Sungkyunkwan University
2Urban Railroad Research Department, Korea Railroad Research Institute
3Department of Mechanical Engineering, Sungkyunkwan University |
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| ABSTRACT |
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Use of an Automated Driving System is expected to improve traffic safety by protecting drivers from drowsy
driving. Previous studies on the use of Automated Driving Systems mainly focused on detecting a driver’s level of drowsiness
and protecting drivers from accidents by performing fallback maneuvers. However, maneuvers conducted in drowsy states are
limited in their ability to achieve Minimal Risk Conditions because human drivers show a gradual degradation in their driving
ability as they fall asleep and the probability of an accident increases greatly after a driver becomes drowsy. Thus, current
Automated Driving Systems require algorithms to predict drowsiness and perform maneuvers before the driver becomes too
drowsy. This paper suggests an algorithm that not only detects but also predicts driver drowsiness using 6 vehicle data points.
Driver condition is classified into 4 states and Driver drowsiness can be predicted by detecting the severe fatigue state, which
tends to occur one minute before the drowsy state. The vehicle driving data are collected using a simulator and features that
can be used to distinguish between the 4 states are investigated through data analysis. Ultimat |
| Key Words:
Driver drowsiness, Minimal risk condition, Light fatigue, Severe fatigue, Vehicle driving data, Machine learning algorithms
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Corresponding Author.
Ho Yong Lee , Email. 
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