Work

Microwave Sensor Technologies for Road Surface Classification: A Comprehensive Review

Year: 2025

Type: article

Abstract: ABSTRACT This paper presents a comprehensive review of advancements in road surface classification technology utilising automotive microwave sensors, covering both active radar and passive radiometry, along with data analysis techniques. Accurate knowledge of road surface type and condition is crucial for improving driving safety, especially in the pursuit of fully autonomous driving. The paper begins with a comparative analysis of different sensing technologies, including microwave, optical, LIDAR and sonar sensors. It subsequently highlights the distinct advantages of microwave sensors, particularly in scenarios with low visibility, where other sensing methods are not sufficiently effective. The analysis of road surface classification methods using radar or radiometer data includes both technical aspects (signal parameters, sensor type, position and number of antennas, signal polarisation, etc.) and classification algorithms. These include analysing backscattered or emitted signal parameters based on specific criteria and making decisions based on this analysis or using statistical classification methods (e.g., k‐nearest neighbours, support vector machines, neural networks). The paper also discusses the current state of the field and explores future directions and potential advancements in surface classification technology.

Source: IET Radar Sonar & Navigation

DOI: 10.1049/rsn2.70080

Authors: Aleksandr Bystrov, Fatemeh Norouzian, Edward Hoare, Viktor Djigan, Marina Gashinova, Mikhail Cherniakov

Institutions: University of Birmingham, National Research University of Electronic Technology

Language: English

Topic: Microwave and Dielectric Measurement Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,620