Paper: "Leverage Deep Learning Methods for Vehicle Trajectory Prediction in Chaotic Traffic"
Paper link: Leverage Deep Learning Methods for Vehicle Trajectory Prediction in Chaotic Traffic
Students involved:
Chau Tan – 20520926 – KHDL2020: Main author.
Ngo Duc Vu – 20520950 – KHDL2020: Co-author.
Nguyen Minh Tri – 20522052 - KHDL2020: Co-author.
Nguyen Tran Anh Duc – 20521198 – KHDL2020: Co-author.
Supervisor: M.S. Do Trong Hop
Summary of the paper:
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.
"We would like to express our gratitude to Mr. Do Trong Hop for accompanying and guiding our group throughout the research and publication process of this international scientific paper."
The ICIT 2023 conference is the International Conference on Intelligence of Things sponsored by Springer. This conference focuses on the latest technologies, applications, and research in the field of the Internet of Things, Intelligence of Things (IoT2/AIoT), and related areas of Information Technology. Accepted papers presented at the conference will be published by Springer in the Lecture Notes on Data Engineering and Communications Technologies series, indexed by SCOPUS (Q3).
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Hai Bang - Media Collaborator, University of Information Technology
Nhat Hien - Translation Collaborator, University of Information Technology