Evidence-Driven Multi-Sensor Monitoring for Railway Track Safety
Danush M · Aug 15, 2026
Abstract : Railway safety systems operate in an environment where a missed obstacle can have severe consequences, while excessive false alarms can reduce the usefulness of an alerting system. Vision systems provide rich semantic information but can degrade under poor lighting or adverse weather. Radar offers complementary distance and motion information and is less dependent on visible illumination, while other short-range sensors can provide inexpensive confirmation signals. This paper proposes an evidence-driven framework, called Virtual Dome, for combining heterogeneous sensors into a layered railway track-monitoring system. The central idea is not to treat any single sensor or machine-learning model as an authority. Instead, the framework separates sensing, feature extraction, evidence fusion, confidence estimation, and alert generation. A track event is escalated when multiple independent signals support the same interpretation or when a high-risk signal persists beyond a defined temporal window. The paper also proposes a practical evaluation protocol covering detection performance, false alarms, latency, environmental robustness, sensor disagreement, and computational cost. Particular attention is given to data scarcity and the difficulty of representing mixed human, animal, object, and infrastructure events in a single dataset. The resulting framework is intended as a research and prototyping architecture rather than a claim of deployment readiness.