TY - GEN
T1 - Autonomous driving system with feature extraction using a binarized autoencoder
AU - Hisafuru, Kota
AU - Negishi, Ryotaro
AU - Kawakami, Soma
AU - Sato, Dai
AU - Yamashita, Kazuki
AU - Fukada, Keisuke
AU - Togawa, Nozomu
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - autoencoder
KW - autonomous driving
KW - bina-rized neural network
KW - FPGA
UR - http://www.scopus.com/inward/record.url?scp=85145564737&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145564737&partnerID=8YFLogxK
U2 - 10.1109/ICFPT56656.2022.9974267
DO - 10.1109/ICFPT56656.2022.9974267
M3 - Conference contribution
AN - SCOPUS:85145564737
T3 - FPT 2022 - 21st International Conference on Field-Programmable Technology, Proceedings
BT - FPT 2022 - 21st International Conference on Field-Programmable Technology, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st International Conference on Field-Programmable Technology, FPT 2022
Y2 - 5 December 2022 through 9 December 2022
ER -