Self-driving or autonomous vehicles need to efficiently and continuously navigate in complex traffic environments by analyzing the surrounding scene, understanding the behavior of other traffic-agents, and predicting their future trajectories. The main goal is to plan a safe motion and reduce the reaction time for possibly imminent hazards. A critical and challenging problem considered in this paper is to explore the movement patterns of surrounding traffic-agents and accurately predict their future trajectories for helping the vehicle make reasonable decision. To solve the problem, a deep learning-based framework is proposed in this paper for predicting trajectories of autonomous vehicles. The key is to train a social GAN (generative adversarial network) deep model for prediction of vehicle trajectory. The presented experimental results have verified that the proposed social GAN-based approach outperforms the traditional Social LSTM (long short-term memory)-based method.


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    Titel :

    Vehicle Trajectory Prediction based on Social Generative Adversarial Network for Self-Driving Car Applications


    Beteiligte:
    Kang, Li-Wei (Autor:in) / Hsu, Chih-Chung (Autor:in) / Wang, I-Shan (Autor:in) / Liu, Ting-Lei (Autor:in) / Chen, Shih-Yu (Autor:in) / Chang, Chuan-Yu (Autor:in)


    Erscheinungsdatum :

    01.11.2020


    Format / Umfang :

    245738 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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