Movement Planning, as a principal innovation of programmed routes for Autonomous vehicles, is yet an open testing issue, all things considered, traffic circumstances and is generally applied by the model-based methodologies. Nonetheless, because of the multifaceted nature of the traffic circumstances and the vulnerability of the edge cases, it is difficult to devise an overall movement arranging framework for Autonomous vehicles. Spurred by this expanded fame, we give Deep-learning based ways to deal with vehicle motion prediction with practically 80% Accuracy in this paper. The 3D Convolutional Neural Network (3D-CNN) filter size 25x224x224 is applied to extricate the spatiotemporal data from the multi-outline data. At last, the completely associated neural organizations are utilized to develop a control model for Autonomous vehicle steering angle. The analyses exhibited that the proposed technique could produce accurate and exact visual movement arranging results for Autonomous vehicles. Movement Planning, as a principal innovation of programmed routes for Autonomous vehicles, is yet an open testing issue, all things considered, traffic circumstances and is generally applied by the model-based methodologies. Nonetheless, because of the multifaceted nature of the traffic circumstances and the vulnerability of the edge cases, it is difficult to devise an overall movement arranging framework for Autonomous vehicles. Spurred by this expanded fame, we give Deep-learning based ways to deal with vehicle motion prediction with practically 80% Accuracy in this paper. The 3D Convolutional Neural Network (3D-CNN) filter size 25x224x224 is applied to extricate the spatiotemporal data from the multi-outline data. At last, the completely associated neural organizations are utilized to develop a control model for Autonomous vehicl


    Access

    Download


    Export, share and cite



    Title :

    Expression of Concern for: Vehicle Motion Prediction for Autonomous Navigation system Using 3 Dimensional Convolutional Neural Network




    Publication date :

    2021-04-08


    Size :

    31837 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vehicle Motion Prediction for Autonomous Navigation system Using 3 Dimensional Convolutional Neural Network

    Pardhi, Prachi / Yadav, Kiran / Shrivastav, Siddhansh et al. | IEEE | 2021


    Deep convolutional neural network based autonomous drone navigation

    Amer, Karim / Samy, Mohamed / Shaker, Mahmoud et al. | SPIE | 2021


    A Convolutional Neural Network Vision System Approach to Indoor Autonomous Quadrotor Navigation

    Garcia, Adriano / Mittal, Sandeep S. / Kiewra, Edward et al. | IEEE | 2019


    Expression of Concern for: Improved face recognition using convolutional neural network with unaided learning

    Malathy, S / Vanitha, C.N. / Mohanasundari, M. et al. | IEEE | 2021

    Free access