| DEVELOPMENT OF THREE DRIVER STATE DETECTION MODELS
FROM DRIVING INFORMATION USING VEHICLE SIMULATOR;
NORMAL, DROWSY AND DRUNK DRIVING |
| Kang Hee Lee, Keon Hee Baek, Su Bin Choi, Nak Tak Jeong, Hyung Uk Moon, Eun Seong Lee, Hyung Min Kim, Myung Won Suh |
| Sungkyunkwan University |
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| ABSTRACT |
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Detection of drivers’ states is the essential technology not only to prevent car accidents related with their state
but to develop self-driving car. Detecting technology generally uses two types of methods; physiological measures and
vehicle-based measures. Vehicle-based measures have advantages compared to physiological method such as non-additional
device, unsophisticated process and less computational power. For these reasons, vehicle-based measures are used for this
study to build the detection system about 3 states; normal, drowsy and drunk driving. In order to achieve this purpose, three
types of algorithm models are suggested using vehicle simulator experiments with twelve participants on three states; normal,
drowsy and drunk. By analyzing the accuracy of each input packet data combination, the feature values, the configuration of
the input data calculated through the vehicle driving data is used to derive the influential factors for predicting the driver state.
The results of the models indicate high accuracy and give the possibility to be applied on detecting 3 states in real driving
vehicles with the system using combination of developed models. |
| Key Words:
Drowsy driving, Drunk driving, Normal driving, Acceleration, Steering angle, Random forest, Artificial
neural network, Vehicle safety, Vehicle simulator, Driver’s state, Self-driving car |
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Corresponding Author.
Myung Won Suh , Email. 
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