Predicting vehicle trajectories plays an important role in autonomous driving and ITS applications. Although multiple deep learning algorithms are devised to predict vehicle trajectories, their reliant on specific graph structure (e.g., Graph Neural Network) or explicit intention labeling limit their flexibilities. In this study, we propose a pure Transformer-based network with multiple modals considering their neighboring vehicles. Two separate tracks are employed. One track focuses on predicting the trajectories while the other focuses on predicting the likelihood of each intention considering neighboring vehicles. Study finds that the two track design can increase the performance by separating spatial module from the trajectory generating module. Also, we find the the model can learn an ordered group of trajectories by predicting residual offsets among K trajectories.

翻译：车辆轨迹预测在自动驾驶和智能交通系统应用中发挥着重要作用。尽管已有多种深度学习算法被设计用于车辆轨迹预测，但它们对特定图结构（如图神经网络）或显式意图标注的依赖限制了其灵活性。本研究提出了一种基于纯Transformer架构的网络，该网络在考虑邻域车辆的情况下支持多模态预测。网络采用两个独立分支：一个分支专注于轨迹预测，另一个分支则结合邻域车辆信息预测每种意图的可能性。研究发现，通过将空间模块与轨迹生成模块分离，双分支设计能够提升模型性能。此外，我们发现该模型可通过预测K条轨迹之间的残差偏移量来学习一组有序的轨迹集合。