论文标题
比例网络：可扩展的车辆轨迹预测网络在相互作用的车辆随机数下通过边缘增强图形卷积神经网络
SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network
论文作者
论文摘要
在随机变化的交通水平上预测周围车辆的未来轨迹是开发自动驾驶汽车最具挑战性的问题之一。由于没有预定义的相互作用车辆参与，因此预测网络必须相对于车辆数量可扩展，以确保准确性和计算负载的一致性。在本文中，提出了第一个完全可扩展的轨迹预测网络Scale-NET，可以确保更高的预测性能和一致的计算负载，而不管周围车辆的数量多少。该比例网络采用边缘卷积神经网络（EGCN）用于行进间相互作用嵌入网络。由于所提出的EGCN在图形节点（本研究中的代理）方面可固有地扩展，因此该模型可以独立于考虑的车辆总数。我们通过比较每个单个驾驶场景的计算时间和预测准确性，相对于不同的车辆编号，评估了公开可用的NGSIM数据集上比例网络的可伸缩性。实验测试表明，计算时间和比例网络的预测性能始终优于先前模型的表现，而不管交通复杂度的水平如何。
Predicting the future trajectory of surrounding vehicles in a randomly varying traffic level is one of the most challenging problems in developing an autonomous vehicle. Since there is no pre-defined number of interacting vehicles participate in, the prediction network has to be scalable with respect to the vehicle number in order to guarantee the consistency in terms of both accuracy and computational load. In this paper, the first fully scalable trajectory prediction network, SCALE-Net, is proposed that can ensure both higher prediction performance and consistent computational load regardless of the number of surrounding vehicles. The SCALE-Net employs the Edge-enhance Graph Convolutional Neural Network (EGCN) for the inter-vehicular interaction embedding network. Since the proposed EGCN is inherently scalable with respect to the graph node (an agent in this study), the model can be operated independently from the total number of vehicles considered. We evaluated the scalability of the SCALE-Net on the publically available NGSIM datasets by comparing variations on computation time and prediction accuracy per single driving scene with respect to the varying vehicle number. The experimental test shows that both computation time and prediction performance of the SCALE-Net consistently outperform those of previous models regardless of the level of traffic complexities.
