Development and application of a generalized fatigue driving monitoring system for open-pit mining vehicles: A case study in a myanmar copper mine

• Developed a Generalized Fatigue Driving Monitoring System (GFDMS) integrating data acquisition, transmission, and analysis for open-pit mining vehicles. • Demonstrated superior Beidou positioning antenna performance with minimal signal blind spots in challenging mining environments. • Achieved over a 70% reduction in traffic incident rates and improved vehicle utilization within one year of implementation in S&K mine. • Enabled real-time detection of unsafe driving behaviors, including speeding, fatigue driving, and irregular operations. • Proposed future enhancements in hardware optimization, multimodal data fusion, and blockchain-based data security. This study presents the development and implementation of a Generalized Fatigue Driving Monitoring System (GFDMS) for open-pit mining vehicles at the S&K copper open-pit mine in Myanmar. The GFDMS integrates a three-layer architecture comprising data acquisition, transmission and analysis, utilizing Beidou positioning antennas, speed sensors and Driver Status Monitoring (DSM) cameras to monitor driver behaviors and vehicle conditions, while incorporating both Multi-Sensor-Based Hypo Vigilance Detection Systems (S-HDx) and Vision-Based Hypo-Vigilance Detection Systems (V-HDx) characteristics. Engineering practices yielding the following outcomes: (1) The Beidou positioning antenna demonstrated superior performance in the S&K mine, ensuring accurate vehicle tracking and minimal signal blind spots in challenging open-pit mining environments. (2) The GFDMS effectively identifies unsafe driving behaviors, including speeding, fatigue driving and irregular operations, providing real-time alerts to enhance safety. (3) A comprehensive analysis of system performance demonstrated its effectiveness, reducing traffic incident rates by over 70% for mining dump trucks and auxiliary vehicles within a year while improving vehicle utilization and operational reliability. (4) Challenges in improving the GFDMS persist in areas such as environmental adaptability, algorithm generalization, and scalability. Future enhancements may include hardware optimization, multimodal data fusion, and blockchain-based data security.