Movement control with vehicle-to-vehicle communication by using end-to-end deep learning for autonomous driving

Zelin Zhang, Jun Ohya

研究成果: Conference contribution

抄録

In recent years, autonomous driving through deep learning has gained more and more attention. This paper proposes a novel Vehicle-to-Vehicle (V2V) communication based autonomous vehicle driving system that takes advantage of both spatial and temporal information. The proposed system consists of a novel combination of CNN layers and LSTM layers for controlling steering angle and speed by taking advantage of the information from both the autonomous vehicle and cooperative vehicle. The CNN layers process the input sequential image frames, and the LSTM layers process historical data to predict the steering angle and speed of the autonomous vehicle. To confirm the validity of the proposed system, we conducted experiments for evaluating the MSE of the steering angle and vehicle speed using the Udacity dataset. Experimental results are summarized as follows. (1) “with a cooperative car” significantly works better than “without”. (2) Among all the network, the Res-Net performs the best. (3) Utilizing the LSTM with Res-Net, which processes the historical motion data, performs better than “no LSTM”. (4) As the number of inputted sequential frames, eight frames turn out to work best. (5) As the distance between the autonomous host and cooperative vehicle, ten to forty meters turn out to achieve the robust result on the autonomous driving movement control.

本文言語English
ホスト出版物のタイトルICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods
編集者Maria De Marsico, Gabriella Sanniti di Baja, Ana Fred
出版社SciTePress
ページ377-385
ページ数9
ISBN(電子版)9789897584862
出版ステータスPublished - 2021
イベント10th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2021 - Virtual, Online
継続期間: 2021 2 42021 2 6

出版物シリーズ

名前ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods

Conference

Conference10th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2021
CityVirtual, Online
Period21/2/421/2/6

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識

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