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 GitHub1


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    Title :

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


    Contributors:


    Publication date :

    2019-06-01


    Size :

    1566361 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    TOP-VIEW TRAJECTORIES: A PEDESTRIAN DATASET OF VEHICLE-CROWD INTERACTION FROM CONTROLLED EXPERIMENTS AND CROWDED CAMPUS

    Yang, Dongfang / Li, Linhui / Redmill, Keith et al. | British Library Conference Proceedings | 2019



    Pedestrian detection in crowded scenes

    Leibe, B. / Seemann, E. / Schiele, B. | IEEE | 2005


    Feeling crowded yet?: Crowd simulations for VR

    Pelechano Gómez, Núria / Allbeck, Jan M. | BASE | 2016

    Free access