High-granularity vehicle trajectory data can help researchers develop traffic simulation models, understand traffic flow characteristics, and thus propose insightful strategies for road traffic management. This paper proposes a novel vehicle trajectory extraction method that can extract high-granularity vehicle trajectories from aerial videos. The proposed method includes video calibration, vehicle detection and tracking, lane marking identification, and vehicle motion characteristics calculation. In particular, the authors propose a Monte-Carlo-based lane marking identification approach to identify each vehicle's lane. This is a challenging problem for vehicle trajectory extraction, especially when the aerial videos are taken from a high altitude. The authors applied the proposed method to extract vehicle trajectories from several high-resolution aerial videos recorded from helicopters. The extracted dataset is named by the High-Granularity Highway Simulation (HIGH-SIM) vehicle trajectory dataset. To demonstrate the effectiveness of the proposed method and understand the quality of the HIGH-SIM dataset, we compared the HIGH-SIM dataset with a well-known dataset, the NGSIM US-101 dataset, regarding the accuracy and consistency aspects. The comparison results showed that the HIGH-SIM dataset has more reasonable speed and acceleration distributions than the NGSIM US-101 dataset. Also, the internal and platoon consistencies of the HIGH-SIM dataset give lower errors compared to the NGSIM US-101 dataset. To benefit future research, the authors have published the HIGH-SIM dataset online for public use.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Video-based trajectory extraction with deep learning for High-Granularity Highway Simulation (HIGH-SIM)


    Beteiligte:
    Xiaowei Shi (Autor:in) / Dongfang Zhao (Autor:in) / Handong Yao (Autor:in) / Xiaopeng Li (Autor:in) / David K. Hale (Autor:in) / Amir Ghiasi (Autor:in)


    Erscheinungsdatum :

    2021




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Deep Learning Highway Traffic Scenario Construction with Trajectory Generators

    van der Heide, Anne / Tampere, Chris M. J. / Nicolai, Mike | IEEE | 2023


    Highway vehicle detection device and method based on deep learning and video

    WANG ZHIHUA / NIU ZHENQIAN / HAO JIAHAO | Europäisches Patentamt | 2024

    Freier Zugriff

    Human-like Highway Trajectory Modeling based on Inverse Reinforcement Learning

    Sun, Ruoyu / Hu, Shaochi / Zhao, Huijing et al. | IEEE | 2019



    Trajectory Planning With Deep Reinforcement Learning in High-Level Action Spaces

    Williams, Kyle R. / Schlossman, Rachel / Whitten, Daniel et al. | IEEE | 2023