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Proximity movements between vehicles transporting materials in manufacturing plants, or “interfaces”, result in occupational injuries and fatalities. Risk assessment for interfaces is currently limited to techniques such as safety audits, originally designed for static environments. A data-driven alternative for dynamic environments is desirable to quantify interface risks and to enable the development of effective countermeasures. We present a method to estimate the Risk Prioritization Number (RPN) for mobile vehicle interfaces in manufacturing environments, based on the Probability-Severity-Detectability (PSD) formulation. The highlight of the method is the estimation of the probability of occurrence (P) of vehicle interfaces using machine learning and computer vision techniques. A PCA-based sparse feature vector for machine learning characterizes vehicle geometry from a top-down perspective. Supervised classification on sparse feature vectors using Support Vector Machines (SVMs) is employed to detect vehicles. Computer vision techniques are used for position tracking to identify interfaces and to calculate their probability of occurrence (P). This leads to an automated calculation of RPN based on the PSD formulation. Experimental data is collected in the laboratory using a sample work area layout and scale versions of vehicles. Vehicle interfaces and movements were physically simulated to train and test the machine learning model. The performance of the automated system is compared with human annotation to validate the approach.