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：实现鲁棒的自主运动规划需要对周围交通参与者的未来行为进行可靠预测。针对这一需求及其相关挑战，我们提出了MTP-GO模型。该模型通过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。运动模型采用神经常微分方程实现，其中状态转移函数与模型其余部分联合学习。通过混合密度网络与卡尔曼滤波相结合，获得了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，在多项指标上优于多种现有最优方法。