Collision Avoidance Systems (CAS) are widely deployed in large-scale mining operations to support real-time collision prevention between mobile assets. These systems typically rely on GNSS-based positioning to determine equipment location, speed, and heading; vehicle-to-vehicle communications (commonly via RF) to detect nearby assets; and predictive logic to assess potential conflicts and generate alerts (Ruff, 2007; Hexagon Mining, 2020). Industry vehicleinteraction frameworks also emphasize that these controls operate within complex, highly dynamic conditions where human factors, traffic density, and operational context strongly influence collision risk (EMESRT, 2024). While CAS technologies provide reliable real-time proximity awareness between mobile assets, interpreting interaction alerts within the operational context remains challenging in complex mining environments (Ruff, 2007; Hrica et al., 2022). In medium- to large-scale open-pit operations, these alerts are generated continuously as part of normal operational monitoring under highly dynamic traffic conditions. Given this operational variability, safety and dispatch teams often benefit from additional analytical tools that help interpret CAS alerts according to the specific operational context in which they occur. In current industry practice, post-operational analysis of CAS alerts is often performed using simple rule-based or univariate criteria, such as alert duration, minimum distance, or aggressor speed; for example, events exceeding a fixed time threshold or involving equipment traveling above a predefined speed may be treated as higher risk (Ruff, 2007; EMESRT, 2023). While useful for basic filtering, such criteria may not fully capture the multidimensional nature of collision risk in dynamic mining environments. Integrating contextual operational variables such as traffic conditions, spatial characteristics, and equipment combinations can enhance the interpretation of interaction alerts and support safety teams in identifying patterns that may require operational attention (EMESRT, 2024; Hrica et al., 2022). Consequently, many operations continue to rely on reactive safety practices, where attention is focused on isolated alerts rather than on systemic risk patterns. Dispatch and safety teams are often required to review large numbers of interaction events with limited contextual information, reducing the effectiveness of preventive interventions. This situation highlights the need for methodologies capable of translating CAS interaction alerts into structured, context-aware risk indicators that integrate multiple operational variables, adapt to different mine zones, and provide interpretable outputs for preventive decision-making. In response to this gap, this work proposes an adaptive, context-aware multi-expert framework designed to support contextual interpretation and prioritization of CAS interaction alerts and identify spatial risk patterns using only standard CAS telemetry. By integrating intelligent mine segmentation, fuzzy expert reasoning, and spatial aggregation models, the approach aims to translate collision alerts into actionable safety information and support proactive, data-driven safety management in real mining operations. 2. OBJECTIVES AND SCOPE This work addresses three main topic areas of the World Mining Congress: AI and DataDriven Decision Making, Health, Safety and Well-being, and Smart Operations and Systems
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