Enhancing Rail Transit Safety: Edge Computing‐Based Driver State Monitoring System

Ensuring the safety and alertness of railway drivers is essential, yet many existing monitoring systems still experience noticeable delays, limited real‐time performance, and inefficient use of communication resources. To address these challenges, we present a rail transit driver state monitoring system that integrates edge computing with cloud‐based management. The system employs multiple onboard cameras, including a cabin surveillance camera and an AI assisted camera that tracks the driver's movements, to provide a broad view of driver and cabin activity. A Jetson Orin edge unit performs real‐time inference using deep learning models for driver authentication, facial‐state analysis, behaviour recognition, and cabin personnel monitoring. To reduce bandwidth consumption, the system transmits compact feature representations during normal operation and uploads raw video to the cloud only when abnormal events are detected, enabling network‐wide supervision via a central platform. Experiments demonstrate that the proposed edge‐first pipeline achieves an average end‐to‐end decision latency of 173.73 ms on the edge device, while maintaining strong detection performance on the test set, reaching 96.82% accuracy for blink detection, 98.33% accuracy for yawn detection, and 97.22% accuracy for driver behaviour monitoring.