Vehicle trajectory prediction is a crucial task in the field of intelligent transportation systems, with applications in traffic management, autonomous driving, and advanced driver assistance systems. In this project, we aim to demonstrate the capabilities of this technology by collecting and analyzing video footage of traffic in Thu Duc District, Ho Chi Minh city. Utilizing YOLOv7 for object detection and DeepSORT for object tracking, we are able to accurately gather the coordinates of vehicles in the scene. Using these coordinates, we then employ various hybrid models such as CNN-LSTM and CNN-GRU to predict the future trajectory of the vehicles. The results are then visualized and superimposed onto the original video footage to showcase the capabilities of these predictive models.


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    Titel :

    Leverage Deep Learning Methods for Vehicle Trajectory Prediction in Chaotic Traffic


    Weitere Titelangaben:

    Lect. Notes on Data Eng. and Comms.Technol.


    Beteiligte:
    Dao, Nhu-Ngoc (Herausgeber:in) / Thinh, Tran Ngoc (Herausgeber:in) / Nguyen, Ngoc Thanh (Herausgeber:in) / Chau, Tan (Autor:in) / Ngo, Duc-Vu (Autor:in) / Nguyen, Minh-Tri (Autor:in) / Nguyen-Tran, Anh-Duc (Autor:in) / Do, Trong-Hop (Autor:in)

    Kongress:

    International Conference on Intelligence of Things ; 2023 ; Ho Chi Minh City, Vietnam October 25, 2023 - October 27, 2023



    Erscheinungsdatum :

    20.10.2023


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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