Real-Time Road Surface Identification Using Ultrasound, Microwave and Sub-THz Radar

This paper investigates the use of sonar and radar-based sensors for real-time road surface recognition in automotive systems, with the goal of improving road safety. The study demonstrates that statistical analysis of backscattered signals from actual road surfaces, when enhanced by machine learning, can significantly improve the accuracy of surface type classification. The research also shows that fusing data from multiple sensors and increasing radar signal frequency further improve classification accuracy, with an 8% to 12% increase in recognition observed in our experimental data. These findings lay a strong foundation for the development of advanced automotive systems capable of accurately recognizing road surfaces in real time during vehicle motion.