the visualization tool to analyze their contextual characteristics, as illustrated in Figure 6, while Table 3 summarizes their representative operational conditions and specialist observations. Figure 6 – Schematic reconstruction of representative high-risk interaction scenarios not prioritized by the expert system (false negatives). Table 3 – Operational context of the scenarios shown in Figure 6. Ite m Zone Aggresso r speed (km/h) Opponen t speed (km/h) Min distanc e (m) System severit y (%) CAS specialist comment (a) Intersectio n 24-28 4-6 2-3 ~75 Late LV braking; misjudged HT proximity. (b) Haul road 30-35 3-5 2-3 ~74 Late LV braking; misjudged HT proximity. (c) Parking 6-10 4-6 ~1 ~72 LV entered HT blind spot without deceleration. Due to the interpretable and tunable nature of the fuzzy expert system, these scenarios provide useful feedback for iterative calibration of the fuzzy sets and inference rules, helping maintain alignment with evolving operational conditions. 4.3 Risk Severity Aggregation and Propagation Analysis (Stage 3) The aggregated analysis performed in Stage 3 identified several zones with significant risk accumulation within the evaluated operational context, classified as spatial hotspots. Across the analyzed large-scale open-pit mining operations in Brazil and Peru, recurring interaction patterns with elevated severity indicators were consistently observed at mixed-traffic intersections involving haul trucks and light vehicles. These patterns represent recurrent high-risk interaction conditions where vehicle trajectories frequently converge under varying speeds, visibility conditions, and reaction times. As a representative example, Figure 7 illustrates a schematic hotspot scenario corresponding to this type of intersection, derived from patterns consistently observed across
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