@article{ref1,
title="Ensemble CNN to detect drowsy driving with in-vehicle sensor data",
journal="Sensors (Basel)",
year="2021",
author="Jeon, Yongsu and Kim, Beomjun and Baek, Yunju",
volume="21",
number="7",
pages="-",
abstract="Drowsy driving is a major threat to the safety of drivers and road traffic. Accurate and reliable drowsy driving detection technology can reduce accidents caused by drowsy driving. In this study, we present a new method to detect drowsy driving with vehicle sensor data obtained from the steering wheel and pedal pressure. From our empirical study, we categorized drowsy driving into long-duration drowsy driving and short-duration drowsy driving. Furthermore, we propose an ensemble network model composed of convolution neural networks that can detect each type of drowsy driving. Each subnetwork is specialized to detect long- or short-duration drowsy driving using a fusion of features, obtained through time series analysis. To efficiently train the proposed network, we propose an imbalanced data-handling method that adjusts the ratio of normal driving data and drowsy driving data in the dataset by partially removing normal driving data. A dataset comprising 198.3 h of in-vehicle sensor data was acquired through a driving simulation that includes a variety of road environments such as urban environments and highways. The performance of the proposed model was evaluated with a dataset. This study achieved the detection of drowsy driving with an accuracy of up to 94.2%.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21072372",
url="http://dx.doi.org/10.3390/s21072372"
}