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.

翻译：鉴于其适应性和鼓舞人心的性能，深度学习模型已成为自动驾驶中运动预测的标准方法。然而，伴随极大灵活性而来的是缺乏可解释性以及可能违反物理约束的问题。将这些数据驱动方法与差分约束运动模型相结合，以提供物理上可行的轨迹，是一个有前景的未来方向。本研究的基础是先前引入的基于图神经网络的模型MTP-GO。该神经网络学习计算底层运动模型的输入，以提供物理上可行的轨迹。本研究探讨了多种运动模型与数值求解器结合在预测任务中的性能。研究表明，为实现精确预测，低阶积分器模型等简单模型优于运动学模型等复杂模型。此外，数值求解器对性能有显著影响，建议避免使用欧拉前向法等常用一阶方法，而采用二阶方法（如海因方法）则可显著改善预测结果。