  Given their flexibility and encouraging performance, deep-learning models are
becoming standard for motion prediction in autonomous driving. However, with
great flexibility comes a lack of interpretability and possible violations of
physical constraints. Accompanying these data-driven methods with
differentially-constrained motion models to provide physically feasible
trajectories is a promising future direction. The foundation for this work is a
previously introduced graph-neural-network-based model, MTP-GO. The neural
network learns to compute the inputs to an underlying motion model to provide
physically feasible trajectories. This research investigates the performance of
various motion models in combination with numerical solvers for the prediction
task. The study shows that simpler models, such as low-order integrator models,
are preferred over more complex, e.g., kinematic models, to achieve accurate
predictions. Further, the numerical solver can have a substantial impact on
performance, advising against commonly used first-order methods like Euler
forward. Instead, a second-order method like Heun's can greatly improve
predictions.
