AI Guardian for Aircraft Safety and Rescue: A Review of Predictive Health Monitoring, Collision Avoidance and Autonomous Emergency Response

Abstract

Severe weather, mid-flight system failures and hazardous terrain continue to challenge aviation safety, and traditional safeguards depend heavily on pilot intervention and ground communication that may be limited during sudden emergencies. This paper reviews the concept of an AI Guardian, an intelligent framework that supports flight safety before, during and after an incident. Three capability areas are examined. First, predictive health monitoring uses deep learning on engine and flight-data telemetry to estimate remaining useful life and detect anomalies before they become failures. Second, decision support for collision avoidance and emergency handling draws on airborne collision-avoidance logic, neural-network policy compression and formal verification of safety-critical networks. Third, post-incident rescue support coordinates drones with computer vision to locate crash sites, deliver medical kits and relay location data to rescue teams. A five-layer architecture that integrates onboard sensing, edge analytics, pilot advisory, air–ground communication and rescue coordination is proposed. Research gaps, objectives, a comparison of techniques and the certification challenges set out in the EASA AI roadmap are discussed. The review concludes that AI can reduce human error and response time if it remains verifiable, explainable and subordinate to pilot authority.