Optimization Of System Structure And Sampling Algorithm For Intelligent Road Sensors

Road traffic has played an extremely important role in the economic development of our country. As the artery of road traffic, highway, with the continuous growth of its traffic mileage and coverage, its safety and clear has become more and more important. Establish a complete highway pavement condition monitoring and early warning system has become more urgent than ever. Intelligent road sensor system, which is an important part of the warning system, has been controlled by foreign corporate for a long time. On this condition, the price and maintenance costs of intelligent road sensor system are very high, which is the main reason that intelligent road sensor system can not be used massively, as a result of which influences the development of our national economy greatly. Thus, the importance of achieving the localization of intelligent road sensor systems can be easily seen by all of us.Facing the current issue of intelligent road sensor systems that exist, firstly, we conducted an investigation for the current sensor system at home and abroad for common road covering discrimination technology. At the same time, we analyzed several road cover that related to this article and multi-frequency detection principle. Then by analyzing the structure of the sensitive capacitive of multi-frequency module, and establishing fringe capacitance model, the excitation and sampling strategy have been improved in this paper. And also, by separating calibration data and system program and updating calibration data through serial ports, this paper greatly improved the production efficiency and maintainability. Meanwhile, by standardizing the sensor system program, this paper achieved a unified calibration procedures and final determination procedures. By separating the calibration data and programs, storing the calibration data on a programmable on-chip memory, calibration data can be updated via the serial port.In the course of experiment and analysis for the optimized system, it shows that the optimized system can effectively increase the difference between the sample variance on the road conditions of dry, water and ice, as a result of which the resolution of road sensor system for the road conditions of dry, water and ice can be significantly improved. And also, the road sensor systems in this paper gain the ability to distinguish snow road. By implementing output mode selection and updating calibration data via the serial port, the optimized program improved production efficiency and system maintainability greatly.