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Abstract. Artificial systems with a high degree of autonomy require reliable semantic information about the context they operate in. However, state interpretation is a difficult task. Interpretations may depend on a history states and there may be more than one valid interpretation. We propose a model for spatio-temporal situations using hidden Markov models based on relational state descriptions, which are extracted from the estimated state of an underlying dynamic system. Our model covers concurrent situations, scenarios with multiple agents, and situations of varying durations. In this work we apply our model to the concrete task of traffic analysis.