In the modern era, Autonomous Vehicles (AVs) are pivotal within the transportation system, presenting notable engineering challenges in the development of self-driving systems, particularly in agent detection such as pedestrians, surrounding vehicles, traffic lights, bicycles, etc. The dataset utilized comprises level 5 data, encompassing three key tables: scenes, time frames, and traffic agents. Specifically, the traffic agent table contains Zarr compressed files providing information about aerial maps, velocities, and coordinates of agents. The primary objective of this paper revolves around constructing a Convolution Neural Network (CNN) model to discern the motion trajectories of agents in relation to the AV, a demanding and high-priority task in building an advanced self-driving system. To predict these motion trajectories, baseline CNN models like ResNet34 and ResNet50 were employed, compared to determine the optimal model. The results indicate a significant enhancement in Negative Multi Log Likelihood when compared with non-frozen ResNet models. Our future research aims to establish a pipeline for object classification around AV and implement Generative Adversarial Network (GAN) on noise data to project future trajectories.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Autonomous Vehicle Along with Surrounding Agent's Path Prediction Using CNN-Based Models


    Beteiligte:


    Erscheinungsdatum :

    03.11.2023


    Format / Umfang :

    4598008 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Surrounding vehicle classification and path prediction

    GUNARATNE PUJITHA | Europäisches Patentamt | 2018

    Freier Zugriff

    SURROUNDING VEHICLE CLASSIFICATION AND PATH PREDICTION

    GUNARATNE PUJITHA | Europäisches Patentamt | 2017

    Freier Zugriff

    AUTONOMOUS VEHICLE USING PATH PREDICTION

    DENG KUN / LUO WANGDONG / LIU NANJUN et al. | Europäisches Patentamt | 2018

    Freier Zugriff

    Autonomous vehicle using path prediction

    KUN DENG / WANGDONG LUO / NANJUN LIU et al. | Europäisches Patentamt | 2018

    Freier Zugriff

    Autonomous vehicle using path prediction

    DENG KUN / LUO WANGDONG / LIU NANJUN et al. | Europäisches Patentamt | 2018

    Freier Zugriff