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.

中文翻译：

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GISNet：用于车辆轨迹预测的基于图形的信息共享网络

轨迹预测是自动驾驶系统设计中一个关键且具有挑战性的问题。许多面向人工智能的公司，如谷歌 Waymo、优步和滴滴，正在研究更准确的车辆轨迹预测算法。然而，预测性能受许多复杂因素的控制，例如周围车辆的随机行为、自身轨迹的历史信息以及邻居的相对位置等。在本文中，我们提出了一种新的基于图的信息共享网络（GISNet），允许目标车辆与其周围车辆之间的信息共享。同时，模型对场景中所有车辆的历史轨迹信息进行编码。实验在公开的 NGSIM US-101 和 I-80 数据集上进行，预测性能由均方根误差 (RMSE) 衡量。定量和定性实验结果表明，与现有模型相比，我们的模型显着提高了轨迹预测精度，高达 50.00%。