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:扶持性具有复原力的自主运动规划要求对周围道路使用者的未来行为做出强有力的预测。 为了应对这一需求和相关挑战,我们引入了名为MTP-GO的模型。模型用时间图神经网络对场景进行编码,以便为一个基本运动模型提供投入。该模型使用神经普通差异方程式实施,该方程式与模型的其余部分一起学习国家-过渡功能。多模式概率预测是通过混合密度网络和Kalman过滤概念相结合提供的。结果显示,拟议模型在各种数据集中的预测能力,在一系列计量中优于几种最先进的方法。