Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction
This work addresses the trade-off between accuracy and interpretability in vehicle motion prediction for domains like ships and quadcopters, but it is incremental as it builds on existing hybrid approaches.
The paper tackles the problem of predicting vehicle states without expensive physical parameter measurements by proposing a hybrid model combining deep learning and physical motion models, achieving interpretability without accuracy loss compared to existing deep learning methods.
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.