论文标题
GISNET：用于车辆轨迹预测的基于图的信息共享网络
GISNet: Graph-Based Information Sharing Network For Vehicle Trajectory Prediction
论文作者
论文摘要
轨迹预测是自主驾驶系统设计中的一个至关重要且具有挑战性的问题。许多面向AI的公司，例如Google Waymo，Uber和Didi，都在研究更准确的车辆轨迹预测算法。但是，预测性能受许多纠缠因素的控制，例如周围车辆的随机行为，自我推动的历史信息以及邻居的相对位置等。在本文中，我们提出了一个基于图形的新信息共享网络（GISNET），该网络（GISNET）允许目标车辆及其周围车辆之间的信息共享。同时，该模型编码现场所有车辆的历史轨迹信息。实验是在公共NGSIM US-101和I-80数据集上进行的，预测性能通过均方根误差（RMSE）来衡量。定量和定性实验结果表明，与现有模型相比，我们的模型可显着提高轨迹预测准确性高达50.00％。
The trajectory prediction is a critical and challenging problem in the design of an autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber and DiDi, are investigating more accurate vehicle trajectory prediction algorithms. However, the prediction performance is governed by lots of entangled factors, such as the stochastic behaviors of surrounding vehicles, historical information of self-trajectory, and relative positions of neighbors, etc. In this paper, we propose a novel graph-based information sharing network (GISNet) that allows the information sharing between the target vehicle and its surrounding vehicles. Meanwhile, the model encodes the historical trajectory information of all the vehicles in the scene. Experiments are carried out on the public NGSIM US-101 and I-80 Dataset and the prediction performance is measured by the Root Mean Square Error (RMSE). The quantitative and qualitative experimental results show that our model significantly improves the trajectory prediction accuracy, by up to 50.00%, compared to existing models.
