  Predicting the collective motion of a group of pedestrians (a crowd) under
the vehicle influence is essential for the development of autonomous vehicles
to deal with mixed urban scenarios where interpersonal interaction and
vehicle-crowd interaction (VCI) are significant. This usually requires a model
that can describe individual pedestrian motion under the influence of nearby
pedestrians and the vehicle. This study proposed two pedestrian trajectory
datasets, CITR dataset and DUT dataset, so that the pedestrian motion models
can be further calibrated and verified, especially when vehicle influence on
pedestrians plays an important role. CITR dataset consists of experimentally
designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides
unique ID for each pedestrian, which is suitable for exploring a specific
aspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios in
crowded university campus, which can be used for more general purpose VCI
exploration. The trajectories of pedestrians, as well as vehicles, were
extracted by processing video frames that come from a down-facing camera
mounted on a hovering drone as the recording equipment. The final trajectories
of pedestrians and vehicles were refined by Kalman filters with linear
point-mass model and nonlinear bicycle model, respectively, in which
xy-velocity of pedestrians and longitudinal speed and orientation of vehicles
were estimated. The statistics of the velocity magnitude distribution
demonstrated the validity of the proposed dataset. In total, there are
approximate 340 pedestrian trajectories in CITR dataset and 1793 pedestrian
trajectories in DUT dataset. The dataset is available at GitHub.
