The future motion of traffic participants is inherently uncertain. To plan safely, therefore, an autonomous agent must take into account multiple possible trajectory outcomes and prioritize them. Recently, this problem has been addressed with generative neural networks. However, most generative models either do not learn the true underlying trajectory distribution reliably, or do not allow predictions to be associated with likelihoods. In our work, we model motion prediction directly as a density estimation problem with a normalizing flow between a noise distribution and the future motion distribution. Our model, named FloMo, allows likelihoods to be computed in a single network pass and can be trained directly with maximum likelihood estimation. Furthermore, we propose a method to stabilize training flows on trajectory datasets and a new data augmentation transformation that improves the performance and generalization of our model. Our method achieves state-of-the-art performance on three popular prediction datasets, with a significant gap to most competing models.
翻译:交通参与者的未来运动本质上是不确定的。 因此,为了安全地规划未来交通参与者的动作。 因此, 自主代理机构必须考虑到多种可能的轨迹结果并对其进行优先排序。 最近, 这个问题已经通过基因神经网络得到解决。 但是, 多数基因模型要么没有可靠地了解真实的基本轨迹分布, 或不允许预测与可能性相联系。 在我们的工作中, 我们把运动预测直接作为密度估计问题进行模型, 在噪音分布与未来运动分布之间实现正常化。 我们的模型名为Flomo, 允许在单一的网络通过中计算各种可能性, 并且可以直接进行最大可能的估计培训。 此外, 我们提出了一种方法来稳定轨迹数据集的培训流量, 以及一个新的数据增强转换, 来改进模型的性能和普及性能。 我们的方法在三种流行的预测数据集上取得了最新业绩, 与最竞争的模型有很大差距。