Autonomous driving system with feature extraction using a binarized autoencoder

Kota Hisafuru, Ryotaro Negishi, Soma Kawakami, Dai Sato, Kazuki Yamashita, Keisuke Fukada, Nozomu Togawa

研究成果: Conference contribution

抄録

In this study, we present an autonomous driving sys-tem that utilizes a binarized autoencoder implemented on a Field Programmable Gate Array (FPGA). The binarized autoencoder compresses the image into optimal features in this system. The recurrent neural network then determines the following control based on the feature values extracted from the autoencoder and the rotation speed of the motor. We reduced the model size by binarizing the autoencoder because of the limited on-chip memory of the FPGA. We implemented the system on an Ultra96-V2, a board with a programmable logic and processing system. The robot employing our implemented system exhibits robust control by recognizing the entire road marking and road edge line as a feature and drives autonomously along the specified route.

本文言語English
ホスト出版物のタイトルFPT 2022 - 21st International Conference on Field-Programmable Technology, Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665453363
DOI
出版ステータスPublished - 2022
イベント21st International Conference on Field-Programmable Technology, FPT 2022 - Hong Kong, Hong Kong
継続期間: 2022 12月 52022 12月 9

出版物シリーズ

名前FPT 2022 - 21st International Conference on Field-Programmable Technology, Proceedings

Conference

Conference21st International Conference on Field-Programmable Technology, FPT 2022
国/地域Hong Kong
CityHong Kong
Period22/12/522/12/9

ASJC Scopus subject areas

  • ハードウェアとアーキテクチャ
  • ソフトウェア
  • コンピュータ サイエンスの応用
  • 制御と最適化

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