An ego vehicle following a virtual lead vehicle planned route is an essential component when autonomous and non-autonomous vehicles interact. Yet, there is a question about the driver's ability to follow the planned lead vehicle route. Thus, predicting the trajectory of the ego vehicle route given a lead vehicle route is of interest. We introduce a new dataset, the FollowMe dataset, which offers a motion and behavior prediction problem by answering the latter question of the driver's ability to follow a lead vehicle. We also introduce a deep spatio-temporal graph model FollowMe-STGCNN as a baseline for the dataset. In our experiments and analysis, we show the design benefits of FollowMe-STGCNN in capturing the interactions that lie within the dataset. We contrast the performance of FollowMe-STGCNN with prior motion prediction models showing the need to have a different design mechanism to address the lead vehicle following settings.

翻译：自车跟随虚拟领车规划路径是自动驾驶车辆与非自动驾驶车辆交互中的核心环节。然而，驾驶员能否有效跟随规划的领车路径仍存在疑问。因此，基于领车路径预测自车轨迹具有重要研究价值。我们提出全新数据集FollowMe数据集，通过回答驾驶员能否跟随领车这一核心问题，构建了运动与行为预测任务。同时，我们引入深度时空图模型FollowMe-STGCNN作为该数据集的基准模型。实验与分析表明，FollowMe-STGCNN在捕获数据集中交互关系方面具有设计优势。通过与现有运动预测模型的性能对比，我们证实了针对领车跟随场景需要采用差异化设计机制的必要性。