  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.
