@article{ref1,
title="Safety critical event prediction through unified analysis of driver and vehicle volatilities: application of deep learning methods",
journal="Accident analysis and prevention",
year="2020",
author="Arvin, Ramin and Khattak, Asad J. and Qi, Hairong",
volume="151",
number="",
pages="e105949-e105949",
abstract="Transportation safety is highly correlated with driving behavior, especially human error playing a key role in a large portion of crashes. Modern instrumentation and  computational resources allow for the monitorization of driver, vehicle, and  roadway/environment to extract leading indicators of crashes from multi-dimensional  data streams. To quantify variations that are beyond normal in driver behavior and  vehicle kinematics, the concept of volatility is applied. The study measures  driver-vehicle volatilities using the naturalistic driving data. By integrating and  fusing multiple real-time streams of data, i.e., driver distraction, vehicular  movements and kinematics, and instability in driving, this study aims to predict  occurrence of safety critical events and generate appropriate feedback to drivers  and surrounding vehicles. The naturalistic driving data is used which contains 7566  normal driving events, and 1315 severe events (i.e., crash and near-crash), vehicle  kinematics, and driver behavior collected from more than 3500 drivers. In order to  capture the local dependency and volatility in time-series data 1D-Convolutional  Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and 1DCNN-LSTM are applied. Vehicle kinematics, driving volatility, and impaired driving (in terms of  distraction) are used as the input parameters. The results reveal that the  1DCNN-LSTM model provides the best performance, with 95.45% accuracy and prediction  of 73.4% of crashes with a precision of 95.67%. Additional features are extracted  with the CNN layers and temporal dependency between observations is addressed, which  helps the network learn driving patterns and volatile behavior. The model can be  used to monitor driving behavior in real-time and provide warnings and alerts to  drivers in low-level automated vehicles, reducing their crash risk.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2020.105949",
url="http://dx.doi.org/10.1016/j.aap.2020.105949"
}