Human trajectory forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution. Following the success of Social Generative Adversarial Networks (SGAN), recent works propose various GAN-based designs to better model human motion in crowds. Despite superior performance in reducing distance-based metrics, current networks fail to output socially acceptable trajectories, as evidenced by high collisions in model predictions. To counter this, we introduce SGANv2: an improved safety-compliant SGAN architecture equipped with spatio-temporal interaction modelling and a transformer-based discriminator. The spatio-temporal modelling ability helps to learn the human social interactions better while the transformer-based discriminator design improves temporal sequence modelling. Additionally, SGANv2 utilizes the learned discriminator even at test-time via a collaborative sampling strategy that not only refines the colliding trajectories but also prevents mode collapse, a common phenomenon in GAN training. Through extensive experimentation on multiple real-world and synthetic datasets, we demonstrate the efficacy of SGANv2 to provide socially-compliant multimodal trajectories.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Safety-Compliant Generative Adversarial Networks for Human Trajectory Forecasting


    Beteiligte:
    Kothari, Parth (Autor:in) / Alahi, Alexandre (Autor:in)


    Erscheinungsdatum :

    01.04.2023


    Format / Umfang :

    8169298 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Generative adversarial inverse trajectory optimization for probabilistic vehicle forecasting

    VERNAZA PAUL / CHOI WONGUN / RHINEHART NICHOLAS | Europäisches Patentamt | 2020

    Freier Zugriff

    Generative adversarial inverse trajectory optimization for probabilistic vehicle forecasting

    VERNAZA PAUL / CHOI WONGUN / RHINEHART NICHOLAS | Europäisches Patentamt | 2020

    Freier Zugriff

    GENERATIVE ADVERSARIAL INVERSE TRAJECTORY OPTIMIZATION FOR PROBABILISTIC VEHICLE FORECASTING

    VERNAZA PAUL / CHOI WONGUN / RHINEHART NICHOLAS | Europäisches Patentamt | 2019

    Freier Zugriff

    Vehicle Trajectory Prediction at Intersections using Interaction based Generative Adversarial Networks

    Roy, Debaditya / Ishizaka, Tetsuhiro / Mohan, C. Krishna et al. | IEEE | 2019


    SA-SGAN: A Vehicle Trajectory Prediction Model Based on Generative Adversarial Networks

    Zhou, Danyang / Wang, Huxiao / Li, Wei et al. | IEEE | 2021