Roadside sensing fusion system based on vehicle-road cooperation and optimization method

Abstract

The invention discloses an optimization method based on a roadside perception fusion system, and the method comprises the steps: taking vehicle-end positioning (vehicle-end high-precision GNSS, inertial navigation or high-precision map combined positioning) as a positioning truth value, and transmitting the positioning truth value to a roadside unit based on a vehicle-road cooperation mode; and determining the sensing accuracy of the roadside sensing equipment by comparing the vehicle positioning value acquired by the roadside sensing equipment with the positioning truth value fed back by the vehicle end based on the vehicle-road cooperation mode. When the difference value is within a certain range, all target positioning sensed by the roadside sensing equipment is corrected based on the vehicle end positioning data by using a factor graph positioning method, the accuracy of the road target data sensed by the roadside sensing equipment is improved, and correction information is also sent to the cloud platform through the roadside edge computing unit; and when the difference value exceeds a set error allowable range, a data abnormity early warning is reported to a platform end, so that monitoring of the roadside sensing data is realized. According to the invention, under the vehicle-road cooperation condition, the error of the data acquired by the roadside sensing device is corrected by using the factor graph method, so that the accuracy of the data acquired by the roadside sensing device is ensured.

Description

technical field [0001] The invention relates to the field of vehicle-road coordination roadside perception fusion, in particular to a roadside perception fusion system and optimization method based on vehicle-road coordination. Background technique [0002] Vehicle-road coordination technology refers to the use of advanced wireless communication and new-generation Internet technology to comprehensively implement vehicle-vehicle and vehicle-road dynamic real-time information interaction, and to carry out vehicle active safety control and road safety control on the basis of full-time and spatial dynamic traffic information collection and integration. Collaborative management fully realizes the effective coordination of people, vehicles and roads, ensures traffic safety, improves traffic efficiency, and forms a safe, efficient and environmentally friendly road traffic system. [0003] At present, the positioning accuracy requirements for self-driving vehicles are getting higher...

AI Technical Summary

Problems solved by technology

[0009] It can be seen that none of the existing technologies has solved the problem of sensory data accuracy error changes caused by the calibration of the roadside sensing equipment on the entire road section and the slight displacement of the sensor for a long time, resulting in the deviation of the sensory data provided to the surrounding road environment of the networked vehicle.

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