On-Road Object Identification with Time Series Automotive Millimeter-wave Radar Information

Takashi Nakamura, Kentaroh Toyoda, Tomoaki Ohtsuki

研究成果: Conference contribution

抄録

Identifying objects with radar is in great demand to avoid road accidents. Recent research tried to identify moving objects on a road by inputting radar information to a machine learning classifier. In the conventional method, the features used in the machine learning are extracted from the observed radar information with a short time interval. Since the movement of the objects is different depending on the objects, time series information is effective for classification, which has not been exploited before. In this paper, we propose an on-road object identification considering time series of radar information. We measured objects with 79.5 GHz millimeter-wave radar and extract features from a series of time windows by calculating the mean and variance of object information, i.e., velocity, distance, and signal power. The classification performance was evaluated with a dataset obtained by on-road experiments. It is shown that our method outperforms the conventional one and the proposed features significantly contribute to the accurate identification.

本文言語English
ホスト出版物のタイトル2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728152073
DOI
出版ステータスPublished - 2020 5
外部発表はい
イベント91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
継続期間: 2020 5 252020 5 28

出版物シリーズ

名前IEEE Vehicular Technology Conference
2020-May
ISSN(印刷版)1550-2252

Conference

Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020
CountryBelgium
CityAntwerp
Period20/5/2520/5/28

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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