Abstract:Physical motion models offer interpretable predictions for the motion of vehicles. However, some model parameters, such as those related to aero-and hydrodynamics, are expensive to measure and are often only roughly approximated reducing prediction accuracy. Recurrent neural networks achieve high prediction accuracy at low cost, as they can use cheap measurements collected during routine operation of the vehicle, but their results are hard to interpret. To precisely predict vehicle states without expensive mea… Show more
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