Hazard Boundary

Hazard boundary identification focuses on defining the conditions under which a system, particularly those incorporating machine learning (ML), transitions from safe to unsafe operation. Current research emphasizes developing methods to efficiently and accurately locate this boundary, employing techniques like cooperative co-evolutionary algorithms and novel deep learning architectures tailored to specific hazard types (e.g., road debris, industrial process failures). This work is crucial for enhancing the safety and reliability of autonomous systems and industrial processes, enabling proactive risk mitigation and improved decision-making.