基于图形的轨迹预测的差分约束运动模型评估（英文版）

英文标题：Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction中文摘要：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。英文摘要：Given their adaptability and encouraging performance, deep-learning modelsare becoming standard for motion prediction in autonomous driving. However,with great flexibility comes a lack of interpretability and possible violationsof physical constraints. Accompanying these data-driven methods withdiff