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. Multimodal probabilistic predictions are obtained 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：实现弹性自主运动规划需要准确预测周围道路使用者的未来行为。为应对这一需求及相关挑战，我们提出了MTP-GO模型。该模型利用时序图神经网络对场景进行编码，生成底层运动模型的输入。运动模型采用神经常微分方程实现，其中状态转移函数与模型其他部分共同学习。通过混合密度网络与卡尔曼滤波的概念结合，获得多模态概率预测结果。实验结果展示了该模型在多个数据集上的预测能力，在多项指标上优于当前最先进方法。