论文标题
多类SGCN：用代理类嵌入的基于图形的稀疏轨迹预测
Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding
论文作者
论文摘要
在现实世界中，道路使用者的轨迹预测很具有挑战性，因为它们的运动模式是随机且复杂的。以前以行人为导向的作品已经成功地模拟了行人之间的复杂交互作用，但是当涉及其他类型的道路用户（例如，汽车，骑自行车的人等）时，无法预测轨迹，因为他们忽略了用户类型。尽管最近的一些作品与用户标签信息构建了密集连接的图形，但它们遭受了多余的空间相互作用和时间依赖性。为了解决这些问题，我们提出了多类SGCN，这是一种基于稀疏的图形卷积网络的多类轨迹预测方法，该方法考虑了速度和代理标签信息，并使用新颖的相互作用掩模来适应基于其交互作用评分的剂的空间和时间连接。所提出的方法在斯坦福无人机数据集上大大优于最先进的方法，提供了更现实和合理的轨迹预测。
Trajectory prediction of road users in real-world scenarios is challenging because their movement patterns are stochastic and complex. Previous pedestrian-oriented works have been successful in modelling the complex interactions among pedestrians, but fail in predicting trajectories when other types of road users are involved (e.g., cars, cyclists, etc.), because they ignore user types. Although a few recent works construct densely connected graphs with user label information, they suffer from superfluous spatial interactions and temporal dependencies. To address these issues, we propose Multiclass-SGCN, a sparse graph convolution network based approach for multi-class trajectory prediction that takes into consideration velocity and agent label information and uses a novel interaction mask to adaptively decide the spatial and temporal connections of agents based on their interaction scores. The proposed approach significantly outperformed state-of-the-art approaches on the Stanford Drone Dataset, providing more realistic and plausible trajectory predictions.
