Abstract
This paper aims to substantiate a deterministic, engineering-first approach to incident prevention in industrial logistics and freight transport, where heavy vehicles, pedestrians, and dense maneuvering zones create high-consequence risk. The study employs a systems-engineering and cybernetic risk-management approach, using comparative analysis of safety architectures (from passive barriers to integrated digital ecosystems), cognitive-ergonomics reasoning to address warning effectiveness and alarm fatigue, and reliability engineering methods (P–F interval logic, FRACAS feedback, and condition-based maintenance) to connect operations with predictive control. A 15-year operational retrospective from an enterprise fleet (350 units of specialized heavy machinery) is used to assess changes in near-miss dynamics after deploying the proposed controls. Results indicate that coupling smart infrastructure (flow segregation and proximity enforcement via UWB/LiDAR/radar and dynamic projection), vehicle-level assistance (ADAS, sensor fusion, and V2X-type messaging), and predictive diagnostics (IoT monitoring and digital-twin-supported anomaly detection) reduces exposure to conflict scenarios while improving operational continuity. Scientific novelty lies in the author’s proprietary “Chuikov’s Multi-Contour Safety System” as a unified interaction algorithm across the man–machine–environment triad, including a haptic feedback protocol (seat/steering vibration) that replaces non-actionable acoustic alarms in high-noise environments. The practical value is a transferable technical roadmap for designing, implementing, and auditing safety controls at industrial sites, enabling safety to be managed as an engineered process rather than a probabilistic outcome. The paper notes deployment constraints (cost, legacy integration, and AI liability) and indicates directions for further research.
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