Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model, titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multi-modal probabilistic predictions are provided by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.

翻译：实现弹性的自主运动规划需要鲁棒地预测周围道路使用者的未来行为。为满足这一需求并应对相关挑战，我们提出了名为MTP-GO的模型。该模型利用时序图神经网络对场景进行编码，为底层运动模型生成输入。运动模型使用神经常微分方程实现，其状态转移函数与模型其他部分共同学习。通过结合混合密度网络与卡尔曼滤波的概念，提供了多模态概率预测。结果展示了所提模型在多个数据集上的预测能力，在多项指标上优于若干最先进方法。