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Predicting the collective motion of a group of pedestrians (a crowd) underthe vehicle influence is essential for the development of autonomous vehiclesto deal with mixed urban scenarios where interpersonal interaction andvehicle-crowd interaction (VCI) are significant. This usually requires a modelthat can describe individual pedestrian motion under the influence of nearbypedestrians and the vehicle. This study proposed two pedestrian trajectorydatasets, CITR dataset and DUT dataset, so that the pedestrian motion modelscan be further calibrated and verified, especially when vehicle influence onpedestrians plays an important role. CITR dataset consists of experimentallydesigned fundamental VCI scenarios (front, back, and lateral VCIs) and providesunique ID for each pedestrian, which is suitable for exploring a specificaspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios incrowded university campus, which can be used for more general purpose VCIexploration. The trajectories of pedestrians, as well as vehicles, wereextracted by processing video frames that come from a down-facing cameramounted on a hovering drone as the recording equipment. The final trajectoriesof pedestrians and vehicles were refined by Kalman filters with linearpoint-mass model and nonlinear bicycle model, respectively, in whichxy-velocity of pedestrians and longitudinal speed and orientation of vehicleswere estimated. The statistics of the velocity magnitude distributiondemonstrated the validity of the proposed dataset. In total, there areapproximate 340 pedestrian trajectories in CITR dataset and 1793 pedestriantrajectories in DUT dataset. The dataset is available at GitHub.

Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus