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Volume 7 Issue 1
Jan.  2020

IEEE/CAA Journal of Automatica Sinica

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Parham M. Kebria, Abbas Khosravi, Syed Moshfeq Salaken and Saeid Nahavandi, "Deep Imitation Learning for Autonomous Vehicles Based on Convolutional Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 82-95, Jan. 2020. doi: 10.1109/JAS.2019.1911825
Citation: Parham M. Kebria, Abbas Khosravi, Syed Moshfeq Salaken and Saeid Nahavandi, "Deep Imitation Learning for Autonomous Vehicles Based on Convolutional Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 82-95, Jan. 2020. doi: 10.1109/JAS.2019.1911825

Deep Imitation Learning for Autonomous Vehicles Based on Convolutional Neural Networks

doi: 10.1109/JAS.2019.1911825
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  • Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed, equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties, performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance. Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.

     

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    Highlights

    • A comprehensive evaluation and comparison of the three major architectural parameters, including the number of layers, filters, and kernel size in the design of a CNN, and their impact on the network’s overall performance. This comparison gives the researchers an overview of the most effective way to optimally design their deep networks to achieve the best possible performance.
    • A new MSE-based ensemble methodology for regression problems that improves the performance according to the average performance of each model throughout the previous observation samples.
    • As a popular ensemble approach, Bagging method is also considered to comparatively illustrate the superiority of the proposed ensemble approach.
    • Demonstrative comparison between the developed models provides the information about the impact of design parameters on the overall performance which leads to optimal structures for better performances.

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