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.

翻译：尽管深度学习模型因其灵活性和优异性能，已成为自动驾驶中运动预测的标准方法，但高度的灵活性也带来了可解释性不足以及可能违反物理约束的问题。将差分约束运动模型与这些数据驱动方法相结合，以提供物理可行的轨迹，是一个具有前景的未来方向。本工作基于此前提出的图神经网络模型MTP-GO展开。该神经网络学习计算底层运动模型的输入，从而生成物理可行的轨迹。本研究探究了不同运动模型与数值求解器组合在预测任务中的表现。研究表明，为获得准确预测，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，而采用休恩法等二阶方法可大幅改进预测效果。