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Reducing traffic fatalities and serious injuries is a top priority of the USDepartment of Transportation. The computer vision (CV)-based crash anticipationin the near-crash phase is receiving growing attention. The ability to perceivefatal crash risks earlier is also critical because it will improve thereliability of crash anticipation. Yet, annotated image data for training areliable AI model for the early visual perception of crash risks are notabundant. The Fatality Analysis Reporting System contains big data of fatalcrashes. It is a reliable data source for learning the relationship betweendriving scene characteristics and fatal crashes to compensate for thelimitation of CV. Therefore, this paper develops a data analytics model, namedscenario-wise, Spatio-temporal attention guidance, from fatal crash reportdata, which can estimate the relevance of detected objects to fatal crashesfrom their environment and context information. First, the paper identifiesfive sparse variables that allow for decomposing the 5-year fatal crash datasetto develop scenario-wise attention guidance. Then, exploratory analysis oflocation- and time-related variables of the crash report data suggests reducingfatal crashes to spatially defined groups. The group's temporal pattern is anindicator of the similarity of fatal crashes in the group. Hierarchicalclustering and K-means clustering merge the spatially defined groups into sixclusters according to the similarity of their temporal patterns. After that,association rule mining discovers the statistical relationship between thetemporal information of driving scenes with crash features, for each cluster.The paper shows how the developed attention guidance supports the design andimplementation of a preliminary CV model that can identify objects of apossibility to involve in fatal crashes from their environment and contextinformation.

Crash Report Data Analysis for Creating Scenario-Wise, Spatio-Temporal  Attention Guidance to Support Computer Vision-based Perception of Fatal Crash  Risks