Road state monitoring system based on multiple millimeter-wave radars and working method thereof

Abstract

The invention discloses a road state monitoring system based on multiple millimeter-wave radars, which comprises a detection layer, a centralized processing layer and a remote processing layer, and the detection layer is used for detecting vehicles on a road in real time through the multiple millimeter-wave radars and transmitting detection data back to the centralized processing layer; the centralized processing layer is used for receiving and processing vehicle detection data of a plurality of millimeter waves of the detection layer so as to obtain vehicle state information on the road, realize real-time detection of the road state and report the obtained detection information to the remote processing layer; and the remote processing layer is used for receiving and storing the detectioninformation uploaded by the centralized processing layer. The invention further discloses a working method of the road state monitoring system based on the millimeter wave radars. According to the road state monitoring system based on the multiple millimeter-wave radars and the working method of the road state monitoring system, a more reliable, efficient and open system architecture is provided on the basis of edge computing and software definition technologies, and the robustness and engineering application value of the system are improved.

Description

technical field [0001] The invention relates to a road state monitoring system based on multiple millimeter-wave radars and a working method thereof, belonging to the technical field of intelligent traffic road state monitoring. Background technique [0002] At present, due to the high resolution, good directivity, strong anti-interference ability and good detection performance of millimeter wave, at the same time, its atmospheric attenuation is small, it has better penetration to smoke and dust, and is less affected by weather. Therefore, millimeter wave radar It is the only sensor that can work "all-weather and all-day". It is used in the field of automatic driving, and has also begun to be tested in the fields of intelligent transportation, UAV anti-collision flight control, security monitoring and other fields. At present, the system that uses millimeter-wave radar for traffic state monitoring adopts the front-end for target detection and detection, and the back-end for ...

AI Technical Summary

Problems solved by technology

At present, the system that uses millimeter-wave radar for traffic state monitoring adopts the front-end for target detection and detection, and the back-end for road state information detection. Among them, in terms of target detection, the integrated design of front-end detection and back-end processing is adopted. In terms of road status monitoring, cloud computing is used for processing, and the actual deployment and application will bring the following problems; due to the low power of millimeter-wave radar and the limited detection distance (not greater than 200 meters), a single millimeter-wave radar cannot detect multiple correlations. Full coverage and multi-angle detection of targets at intersections or long sections of roads; system functions and performance rely on front-end millimeter-wave radar detection and processing equipment, and the system is less robust; functional modules of millimeter-wave radar detection and processing equipment are tightly coupled, and multi-device When deploying or system upgrading, each device needs to be debugged and calibrated locally, which greatly increases the difficulty of system maintenance, and the value of engineering application is far from reaching the highest level; road status monitoring and processing based on cloud computing requires the vehicle detection of millimeter-wave radar The data is sent back to the remote processing system, which requires high network bandwidth and high delay, and the timeliness of processing results is not high; the degree of detection data sharing is low, which is not conducive to the development and deployment of intelligent transportation-related algorithms and applications, and the utilization rate of equipment is low. high

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