The Context of Crash Occurrence: A Complexity-Infused Approach Integrating Semantic, Contextual, and Kinematic Features

TL;DR

This paper presents a two-stage framework that combines semantic, contextual, and kinematic features to improve crash prediction accuracy in complex driving environments, demonstrating the effectiveness of integrated features and AI-generated annotations.

Contribution

It introduces a novel two-stage method that integrates roadway complexity features for crash prediction, and demonstrates the superiority of AI-generated annotations over human annotations.

Findings

Achieved 90.15% accuracy with combined features

Ablation studies show combined features outperform individual ones

AI-generated annotations outperform human annotations in accuracy

Abstract

Understanding the context of crash occurrence in complex driving environments is essential for improving traffic safety and advancing automated driving. Previous studies have used statistical models and deep learning to predict crashes based on semantic, contextual, or vehicle kinematic features, but none have examined the combined influence of these factors. In this study, we term the integration of these features ``roadway complexity''. This paper introduces a two-stage framework that integrates roadway complexity features for crash prediction. In the first stage, an encoder extracts hidden contextual information from these features, generating complexity-infused features. The second stage uses both original and complexity-infused features to predict crash likelihood, achieving an accuracy of 87.98\% with original features alone and 90.15\% with the added complexity-infused features. Ablation studies confirm that a combination of semantic, kinematic, and contextual features yields the best results, which emphasize their role in capturing roadway complexity. Additionally, complexity index annotations generated by the Large Language Model outperform those by Amazon Mechanical Turk, highlighting the potential of AI-based tools for accurate, scalable crash prediction systems.