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

Zelin Zhang, Jun Ohya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods
EditorsMaria De Marsico, Gabriella Sanniti di Baja, Ana Fred
PublisherSciTePress
Pages377-385
Number of pages9
ISBN (Electronic)9789897584862
Publication statusPublished - 2021
Event10th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2021 - Virtual, Online
Duration: 2021 Feb 42021 Feb 6

Publication series

NameICPRAM 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

Keywords

  • Autonomous driving
  • Deep learning
  • End-to-end
  • Vehicle-to-vehicle communication

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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