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 measurements of physical
parameters, we propose a hybrid approach combining deep learning and physical
motion models including a novel two-phase training procedure. We achieve
interpretability by restricting the output range of the deep neural network as
part of the hybrid model, which limits the uncertainty introduced by the neural
network to a known quantity. We have evaluated our approach for the use case of
ship and quadcopter motion. The results show that our hybrid model can improve
model interpretability with no decrease in accuracy compared to existing deep
learning approaches.
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