Review of Graph-Based Hazardous Event Detection Methods for Autonomous Driving Systems

Automated and autonomous vehicles are often required to operate in complex road environments with potential hazards that may lead to hazardous events causing injury or even death. Therefore, a reliable autonomous hazardous event detection system is a key enabler for highly autonomous vehicles (e.g., Level 4 and 5 autonomous vehicles) to operate without human supervision for significant periods of time. One promising solution to the problem is the use of graph-based methods that are powerful tools for relational reasoning. Using graphs to organise heterogeneous knowledge about the operational environment, link scene entities (e.g., road users, static objects, traffic rules) and describe how they affect each other. Due to a growing interest and opportunity presented by graph-based methods for autonomous hazardous event detection, this paper provides a comprehensive review of the state-of-the-art graph-based methods that we categorise as rule-based, probabilistic, and machine learning-driven. Additionally, we present an in-depth overview of the available datasets to facilitate hazardous event training and evaluation metrics to assess model performance. In doing so, we aim to provide a thorough overview and insight into the key research opportunities and open challenges.

Automated summary

Automated and autonomous vehicles require a reliable autonomous hazardous event detection system to operate without human supervision in complex road environments. The use of graph-based methods is a promising solution, as it allows for relational reasoning and organizing knowledge about the operational environment. This paper provides a comprehensive review of state-of-the-art graph-based methods, categorizing them as rule-based, probabilistic, and machine learning-driven, and also discusses available datasets and evaluation metrics for hazardous event detection.