Road Surface Identification Using Microwave Sensors

This paper presents a comprehensive review of advancements in road surface classification technology utilizing automotive microwave sensors, covering both active radar and passive radiometer, along with data analysis techniques. Accurate knowledge of road surface type and condition plays an important role in enhancing driving safety, particularly in the goal of achieving fully autonomous driving across various terrains. 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 polarization, etc.) and classification algorithms. These include analyzing 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 neighbors, support vector machines, neural networks). The paper also discusses the current state of the field and proposes assumptions about the future development of surface classification technology.