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We consider the recognition of dangerous situations in vehicle traffic. Unscented Kalman filters are used to predict vehicle trajectories within a short prediction horizon [t ₀ , t ₀ + Deltat]. Based on this prediction, for each vehicle pair the mutual distance is computed for [t ₀ , t ₀ + Deltat], whereby the distance accounts for the geometric distance, for the prediction uncertainties as well as for the spatial dimensions of the vehicles. If at least one of the mutual distances falls below a distance threshold isin within [t ₀ , t ₀ + Deltat], then a dangerous situation arises for the cooperative group and may lead to an autonomous cooperative driving manoeuvre. This approach allows the usage of the system in a mixed environment (only some vehicles are cooperative and cognitive). Obstacles can also be handled. The key issues in this ongoing research work are the recognition and classification of dangerous situations and the …