.png){kind=icon}
.png){kind=icon}
Return
Simultaneous Rain Detection and Inter-vehicle Distance Estimation Using a Single FMCW Automotive Radar
.png){kind=icon}
PRE
AI
M
K
I
DOI：10.1007/s11220-026-00769-9.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents a novel method for simultaneous inter-vehicle distance estimation and rainfall detection using a single 60 GHz FMCW radar. The proposed system enables accurate ranging and environmental sensing without the need for additional sensors. A trihedral corner reflector and a metallic vehicle body were used as targets to evaluate signal attenuation and distance estimation under various rainfall conditions. Experimental results confirmed that, at a distance of 3.4 m, the received power decreased by approximately 5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} and 2.5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} for rainfall intensities of 30 mm/h and 15 mm/h, respectively. The theoretical model based on ITU-R P.838-3 showed strong agreement with measured attenuation, validating the feasibility of rainfall estimation from radar signal amplitude. Moreover, the ranging error remained within +/- 0.09 m, demonstrating high robustness of distance estimation even in adverse weather conditions. The proposed approach highlights the potential of millimeter-wave radar to function as both a distance and environmental sensor, paving the way for next-generation intelligent transportation systems and weather-adaptive autonomous vehicles.
Keywords:
FMCW radar
Inter-vehicle distance estimation
Rainfall attenuation
Intelligent transportation systems
Journal
S
IF:
2
Papers:
103
Citations:
618
Organization
Cited Papers