<p>With the development of intelligent transportation systems, the requirements for monitoring and sensing systems have become increasingly stringent. This study proposes a novel self-powered sensing system based on triboelectric nanogenerators (TENGs) for vehicle monitoring and energy harvesting in intelligent transportation systems. The system utilizes the friction between vehicle tires and road materials to generate electricity, enabling real-time monitoring and analysis of driving behavior through collected signals. The research designed a road-compatible triboelectric nanogenerator (r-TENG) using common materials such as natural rubber and asphalt as friction layers, successfully achieving identification and differentiation of various vehicle types. Machine learning algorithms were employed to further enhance recognition accuracy, reaching an identification rate of over 80%. Furthermore, this study demonstrates the potential of r-TENG in practical applications, such as vehicle identification, road traffic monitoring, and driver training, providing a sustainable and cost-effective technical solution for intelligent transportation systems. Experimental results show that r-TENG exhibits good electrical performance, durability, and environmental adaptability, capable of stable operation in complex traffic environments, offering an innovative perspective and theoretical basis for the future development of intelligent traffic management systems.</p>