Self-Diagnosis of Radar System State in RSU Applications

Chia Hsing Yang, Ming Chun Lee, Ta Sung Lee

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

To realize the intelligent transportation, environmental awareness of roadside units (RSUs) is of paramount importance. One of the approaches to enable the environmental awareness of RSUs is to equip RSUs with radar systems. However, as more and more radar systems are installed, manually monitoring whether these radar systems work in their normal states becomes impossible. To resolve this issue, a radar system state self-diagnosis method is proposed in this paper by using the radar sensing information with deep learning techniques. Specifically, by using the proposed feature extraction approach, we first effectively convert the huge amount of radar sensing data into useful features. Then, by using the proposed deep neural network to interpret the extracted features, the radar systems can self-diagnose whether there exist faults on the systems. We verify our proposed method via real-world experiments. Results show that our proposed method can accurately diagnose the radar system and report the faults.

原文English
主出版物標題2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1-5
頁數5
ISBN(電子)9781665413688
DOIs
出版狀態Published - 2021
事件94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
持續時間: 27 9月 202130 9月 2021

出版系列

名字IEEE Vehicular Technology Conference
2021-September
ISSN(列印)1550-2252

Conference

Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
國家/地區United States
城市Virtual, Online
期間27/09/2130/09/21

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