352 Table 2 – Operational context of the scenarios shown in Figure 5. Item Zone Aggressor speed (km/h) Opponent speed (km/h) Min distance (m) System severity (%) CAS specialist comment a) Haul road 25-30 6-9 3-4 ~85 Late LV braking near descending HT. (b) Haul road 18-22 5-10 1-2 ~90 LV remained in HT blind spot. (c) Intersection 28-32 5-8 <1.5 ~80 LV entered blind spot with late braking. (d) Loading 24-28 3-5 2-4 ~80 No HT deceleration; LV evasive maneuver. The false negative rate was 2.8%, indicating that a small proportion of interactions interpreted as high risk by the CAS specialist were assigned a lower severity level by the expert system. These cases were primarily associated with zones of low interaction density and operating conditions close to the transition boundaries between severity levels defined by the fuzzy model. Representative examples of these scenarios were reconstructed from CAS operational logs using 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. Item Zone Aggressor speed (km/h) Opponent speed (km/h) Min distance (m) System severity (%) CAS specialist comment (a) Intersection 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.
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