Topview Trajectories: A Pedestrian Dataset of VehicleCrowd Interaction from Controlled Experiments and Crowded Campus 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 vehiclecrowd 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 downfacing camera mounted on a hovering drone as the recording equipment. The final trajectories of pedestrians and vehicles were refined by Kalman filters with linear pointmass model and nonlinear bicycle model, respectively, in which xyvelocity 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.

Constructing a Highly Interactive Vehicle Motion Dataset Research in the areas related to driving behavior, e.g., behavior modeling and prediction, requires datasets with highly interactive vehicle motions. Existing public vehicle motion datasets emphasize increasing the number of vehicles and time duration, but behaviorrelated researchers are suffering from two factors. First, strong interactions among vehicles are not well addressed and datasets are of relatively lowdensity to observe meaningful interactions. Second, most of the existing datasets are missing the map information with reference paths which is essential for drivingbehaviorrelated research. To address this issue, a dataset with highly interactive vehicle motions is constructed in this paper. A variety of challenging driving scenarios such as unsignalized intersections and roundabouts are included. Reference paths are also constructed from motion data along with highdefinition maps so that key features can be generated for both prediction and planning algorithms. Moreover, we propose a set of metrics to extract the interactive motions in different maps, including the minimum difference of time to collision point (MDTTC) and duration of waiting period. Such metrics are used to quantity the interaction density of the dataset. We also give several representative results on prediction and motion generation utilizing the constructed dataset to demonstrate how the dataset can facilitate research in the area of driving behavior.