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

Understand

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.