Road vehicle flow detection method, device, computer equipment and storage medium

Abstract

The invention relates to a road vehicle flow detection method and device, computer equipment and a storage medium. The method comprises the steps: obtaining data of a Doppler microwave radar sensor deployed at an edge of a road side so as to obtain detection data; preprocessing the detection data to obtain entering detection data; judging whether current statistics is a traffic flow value of a single lane or not; if so, judging whether a vehicle passes through a detection area covered by the signal of the Doppler microwave radar sensor according to the entering detection data and a lane signalthreshold value; if the vehicle passes by, adding one to the traffic flow value of the lane to form a traffic flow statistical result of the lane; and if not, judging whether the vehicle passes through the detection area covered by the signal of the Doppler microwave radar sensor according to the entering detection data and an adjacent lane signal threshold value. In the invention, a vehicle flowcan be accurately detected, applicability is high, and problems of high equipment cost and high complexity in an existing detection method are solved.

Description

technical field [0001] The invention relates to a vehicle flow detection method, more specifically to a road vehicle flow detection method, device, computer equipment and storage medium. Background technique [0002] Traffic is the main driving force for urban development. The surge in car ownership has caused increasingly serious problems such as traffic jams, traffic accidents, and environmental pollution, which have severely restricted urban social and economic development. Therefore, traffic reform is urgent. As a key part of building a human-vehicle-road network collaborative comprehensive perception body, smart roads will complement smart driving technology, strongly support smart driving applications, and promote the construction of intelligent traffic. As an important part of smart roads, traffic flow detection will be widely used in traffic analysis and other fields. Among them, through the data collection and analysis of road traffic flow, the road traffic flow in...

AI Technical Summary

Problems solved by technology

[0003] Most of the existing traffic flow detection methods are based on high-cost sensors such as cameras. At the same time, there are many unavoidable disadvantages. In addition to the high deployment cost, for the traffic flow detection based on cameras, the robustness to the environment is poor, and the detection accuracy is poor. Susceptible to the influence of the environment, for example, large vehicles are easy to block the accompanying small vehicles, shadows, water or day and night changes will cause detection errors, making the system detection inaccurate; vehicle detection systems based on geomagnetic sensors are time-consuming in deployment and maintenance At the same time, it needs to excavate the road surface, which will affect road traffic; the traffic flow detection system based on high-cost microwave sensors is only suitable for single-point detection, the equipment cost is high, and the detection accuracy depends on the complexity and complexity of the detection algorithm. The data processing capability of the controller, the system complexity is high; the traffic flow detection system based on the millimeter wave radar has high requirements on the performance of a single millimeter wave radar sensor, the equipment cost is high, it is not suitable for mass production, the complexity is high, and the calculation process Using the trajectory identification matrix, the detection accuracy depends on the complexity of the detection algorithm and the data processing capability of the controller. The propagation loss of millimeter waves is large, and the robustness to the environment is poor.

[0004] Therefore, it is necessary to design a new method, which can not only accurately detect the traffic flow, but also has strong applicability, so as to solve the problems of high equipment cost and high complexity in the existing detection methods

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