Vehicle Autonomy to Ecosystem Intelligence: A Systematic Review of Dynamic Vision Architectures in Surface Mining Operations

Le résumé fourni par la source

Autonomous Haulage Systems (AHS) have significantly transformed surface mining operations by improving safety, productivity, and operational consistency. Currently, implementations predominantly rely on vehicle-centric perception architectures. Onboard LiDAR, radar, cameras, and Global Navigation Satellite Systems (GNSS) perform sensing, interpretation, and decision-making in individual systems. Decision-making is done using onboard LiDAR, radar, and cameras, and Global Navigation Satellite Systems (GNSS) in individual systems. These approaches enable collision avoidance and path tracking. They remain limited in their ability to account for the broader, dynamic mining environment characterized by dust, terrain degradation, geotechnical instability, heterogeneous traffic, and rapidly evolving operational conditions. This paper presents a systematic review of dynamic vision systems deployed in surface mining. It critically analyses the transition from solitary vehicle autonomy to interconnected, ecosystem-aware intelligence. The review synthesizes literature from mining automation, robotics, intelligent transportation systems, and multi-agent perception. This is to assess sensing technologies, perception algorithms, sensor fusion strategies, and environmental robustness techniques. Attention is given to the limitations of ego-centric perception models in complex open-pit ecosystems. Building on identified gaps, the paper proposes a conceptual framework for Ecosystem-Centric Dynamic Vision (ECDV). This perception is augmented through integration with fleet communication networks, dispatch systems, digital twins, geotechnical monitoring platforms, and environmental sensing infrastructure. The framework outlines a multi-layer architecture enabling cooperative perception, predictive hazard modeling, and risk-aware decision support at the mine-wide level. The review concludes by defining a research agenda for transitioning from vehicle autonomy to ecosystem intelligence in surface mining. It highlights opportunities in cooperative perception, adaptive sensor fusion under degraded visibility, and digital twin integrated predictive safety systems.

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