To enable safe and efficient navigations through the urban environment, autonomous vehicles need to anticipate the future motions of the walking pedestrians who might collide with them. The dynamic and stochastic behavior characteristics of the pedestrians make the trajectory prediction challengeable for most kinematics-based approaches. This paper presents a comparative study of six state-of-the-art learning-based methods for pedestrian trajectory prediction, including Gaussian Process (GP), LSTM, GP-LSTM, Character-based LSTM, Sequence-to-Sequence (Seq2Seq), and attention-based Seq2Seq. The trajectory prediction is formulated as the regression task or sequence generation problem that predicts future trajectories based on observed trajectories. We evaluate the performance of the learning-based methods on a public real-world pedestrian dataset. To address the concern of data scarcity, we employ three forms of data augmentation (i.e., translation, rotation, and stretch) to enlarge the dataset, which produce the transformed trajectories from the original trajectories. By comparison, those learning-based approaches are ranked based on prediction accuracy from high to low as Seq2Seq, attention-based Seq2Seq, C-LSTM, LSTM, GP, and GP-LSTM. Particularly, Seq2Seq model outperforms all baseline approaches, with the mean and final point errors less than 15cm in normal scenarios when predicting 1s ahead.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Pedestrian Trajectory Prediction with Learning-based Approaches: A Comparative Study


    Beteiligte:
    Li, Yang (Autor:in) / Xin, Long (Autor:in) / Yu, Dameng (Autor:in) / Dai, Pengwen (Autor:in) / Wang, Jianqiang (Autor:in) / Li, Shengbo Eben (Autor:in)


    Erscheinungsdatum :

    2019-06-01


    Format / Umfang :

    1213506 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Pedestrian Trajectory Prediction Combining Probabilistic Reasoning and Sequence Learning

    Li, Yang / Lu, Xiao-Yun / Wang, Jianqiang et al. | IEEE | 2020


    Pedestrian trajectory prediction method and device

    ZHENG WEN / LIU CHUANG / XU GUANGYU et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    A Posture Features Based Pedestrian Trajectory Prediction with LSTM

    Kao, I-Hsi / Zhou, Xiao / Chen, I-Ming et al. | IEEE | 2021


    Crossing pedestrian trajectory prediction method and device

    WANG QING / TAN XIUQUAN | Europäisches Patentamt | 2022

    Freier Zugriff