Given their adaptability 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 ones, 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 significantly
improve predictions.

研究了在自动驾驶中，配合基于图神经网络的支持物理运动模型的数据驱动方法的表现，结果表明，相比于动力学模型，更简单的低阶积分器模型更具优势，并且更高阶的数值求解器可以显著提高预测准确性。