Radar-Based Road Surface Classification Using Range-Fast Fourier Transform Learning Models.

ABSTRACT: Traffic accidents caused by black ice have become a serious public safety concern due to their high fatality rates and the limitations of conventional detection systems under low visibility. Millimeter-wave (mmWave) radar, capable of operating reliably in adverse weather and lighting conditions, offers a promising alternative for road surface monitoring. In this study, six representative road surface conditions-dry, wet, thin-ice, ice, snow, and sludge-were experimentally implemented on asphalt and concrete specimens using a temperature and humidity-controlled chamber. mmWave radar data were repeatedly collected to analyze the temporal variations in reflected signals. The acquired signals were transformed into range-based spectra using Range-Fast Fourier Transform (Range-FFT) and converted

SUBMITTER: Lee H 

PROVIDER: S-EPMC12473171 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature