Abstract
This article reviews graph-based hazardous event detection methods for automated and autonomous vehicles. Traditional methods have often fallen short due to the complexity of events that involve a multitude of variables with intricate relationships. Therefore, this research explores the opportunity presented by graph-based methods for relational reasoning. Reasoning uses graph structures that can organize heterogeneous data about the scene and its relationships. The article reviews and categorizes state-of-the-art methods, datasets, and evaluation metrics to provide a comprehensive overview of the latest advancements in the field, as well as key research opportunities and open challenges.
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