@article{ref1,
title="Crash report data analysis for creating scenario-wise, spatio-temporal attention guidance to support computer vision-based perception of fatal crash risks",
journal="Accident analysis and prevention",
year="2020",
author="Li, Yu and Karim, Muhammad Monjurul and Qin, Ruwen and Sun, Zeyi and Wang, Zuhui and Yin, Zhaozheng",
volume="151",
number="",
pages="e105962-e105962",
abstract="Reducing traffic fatal crashes has been an important mission of transportation. With the rapid development of sensor and Artificial Intelligence (AI) technologies, the  computer vision (CV)-based crash anticipation in the near-crash phase is receiving  growing attention. The ability to perceive fatal crash risks in an early stage is of  paramount importance as well because it can improve the reliability of crash  anticipation. Yet this task is challenging because it requires establishing a  relationship between the driving scene information that CV can recognize and the  fatal crash features that CV will not get until the crash occurrence. Image data  with the annotation for directly training a reliable AI model for the early visual  perception of fatal crash risks are not abundant. The Fatality Analysis Reporting  System (FARS) contains big data on fatal crashes, which is a reliable data source  for finding fatal crash clusters and discovering their distribution patterns to tell  the association between driving scene characteristics and fatal crash features. To  enhance CV's ability to perceive fatal crash risks earlier, this paper develops a  data analytics model from fatal crash report data, which is named scenario-wise,  spatio-temporal attention guidance. First, the paper identifies five descriptive  variables that are sparse and thus allow for decomposing the 5-year (2013-2017)  fatal crash dataset to develop scenario-wise attention guidance. Then, an  exploratory analysis of location- and time-related descriptive variables suggests  dividing fatal crashes into spatially defined groups. A group's temporal  distribution pattern is an indicator of the similarity of fatal crashes in the  group. Hierarchical clustering and K-means clustering further merge the spatially  defined groups into six clusters according to the similarity of their temporal  patterns. After that, association rule mining discovers the statistical relationship  between the temporal information of driving scenes with fatal crash features, such  as the first harmful event and the manner of collisions, for each cluster. The paper  illustrates how the developed attention guidance supports the design and  implementation of a preliminary CV model that can identify agents of a possibility  to involve in fatal crashes from their environmental and context information.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2020.105962",
url="http://dx.doi.org/10.1016/j.aap.2020.105962"
}