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

TL;DR

This paper develops a data-driven, scenario-wise spatio-temporal attention guidance model from fatal crash report data to enhance early perception of crash risks in computer vision systems, addressing data scarcity issues.

Contribution

It introduces a novel approach using crash report data to create attention guidance that improves early crash risk perception in CV models, filling data gaps for safer driving systems.

Findings

Identified five key variables for scenario decomposition.

Clustered crash data into six groups based on spatial and temporal patterns.

Demonstrated how attention guidance improves object relevance estimation in crash scenarios.

Abstract

Reducing traffic fatalities and serious injuries is a top priority of the US Department of Transportation. The computer vision (CV)-based crash anticipation in the near-crash phase is receiving growing attention. The ability to perceive fatal crash risks earlier is also critical because it will improve the reliability of crash anticipation. Yet, annotated image data for training a reliable AI model for the early visual perception of crash risks are not abundant. The Fatality Analysis Reporting System contains big data of fatal crashes. It is a reliable data source for learning the relationship between driving scene characteristics and fatal crashes to compensate for the limitation of CV. Therefore, this paper develops a data analytics model, named scenario-wise, Spatio-temporal attention guidance, from fatal crash report data, which can estimate the relevance of detected objects to fatal crashes from their environment and context information. First, the paper identifies five sparse variables that allow for decomposing the 5-year fatal crash dataset to develop scenario-wise attention guidance. Then, exploratory analysis of location- and time-related variables of the crash report data suggests reducing fatal crashes to spatially defined groups. The group's temporal pattern is an indicator of the similarity of fatal crashes in the group. Hierarchical clustering and K-means clustering 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 crash features, for each cluster. The paper shows how the developed attention guidance supports the design and implementation of a preliminary CV model that can identify objects of a possibility to involve in fatal crashes from their environment and context information.