Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning of autonomous vehicles. This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories. This network tackles the spatial interactions using a graph convolutional network (GCN), and captures the temporal features with a convolutional neural network (CNN). The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Besides, we propose a weighted adjacency matrix to describe the intensities of mutual influence between vehicles, and the ablation study demonstrates the effectiveness of our proposed scheme. Our network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM).Comparisons in three aspects, including prediction errors, model sizes, and inference speeds, show that our network can achieve state-of-the-art performance.
翻译:预测相邻车辆的轨迹是自主车辆的决策和运动规划的关键步骤。本文件提出一个基于图表的空间时空演变网络(GSTCN),以预测使用过去轨迹的所有相邻车辆的未来轨迹分布。这个网络利用一个图变轨迹网络(GCN)处理空间互动,并用一个动态神经网络(CNN)捕捉时间特征。空间时空特征由一个封闭的经常性单位(GRU)网络编码和解码,以产生未来的轨迹分布。此外,我们提出一个加权对称矩阵,以描述车辆之间相互影响的强度,以及模拟研究显示我们提议的计划的有效性。我们的网络在两个真实世界自由轨道数据集上进行了评估:下一代模拟(NGSIM)的I-80和US-101。Comparis在三个方面,包括预测错误、模型大小和推断速度,显示我们的网络能够达到状态性能。