Multiple model estimation for improving conflict detection algorithms

We present a framework for improving conflict detection algorithms using a hybrid control paradigm. This allows us to separate the problem into two parts: state/mode estimation and threat prediction. Since the dynamic equations for a conflict can change discretely depending on the situation, we propose the use of multiple model (MM) estimators to predict the situation and ultimately improve threat assessment. We provide an example using two different MM estimators for a rear-end collision warning system. The estimators can be used to determine the scenario mode as well as improve the state estimates

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