Research On RSU-Assisted Cooperative Positioning Method For A Connected Vehicle Environment

With the rapid development of Intelligent Transportation System(ITS)and Connected Vehicle(CV)technologies,the use of intelligent CV applications based on emerging CV devices and Vehicle-to-Everything(V2X)communication technologies has provided new solutions to traditional traffic safety and efficiency issues.However,current intelligent CV devices often provide positioning services for these intelligent CV applications only through a single GPS or Beidou navigation module,and the positioning accuracy provided by these modules is often insufficient to support the safety and reliability of security applications.Considering the high cost of highprecision positioning or ranging equipment,how to increase the positioning accuracy of GPS in the CV environment without adding additional equipment and only using information that can be obtained by existing CV devices has become the focus of attention of many scholars and enterprises at home and abroad.The focus of this research is to carry out research on RSU-assisted cooperative positioning methods for a CV environment to improve the accuracy of GPS positioning.The main tasks completed in the study include the following:Firstly,on the basis of summarizing the relevant positioning methods in CV environment,considering the complexity,cost and accuracy of the positioning methods,the applicability of each positioning method is analyzed,and the RSS data between the CV equipment communication was determined to improve the positioning accuracy of the connected vehicles.Secondly,in view of the RSS ranging problem and the characteristics of signal attenuation in the CV environment,a logarithmic path loss model was selected as the RSS ranging model in this study.The RSS values corresponding to different distances were collected by field test,and the path loss models in both sunny and rainy environments were calibrated.Then,this study proposed the RSU-assisted GPS positioning method and the RSUassisted RSS positioning method in the CV environment,respectively,for the situation where single GPS or RSS data are available.The former method used the RSU device to broadcast the MAP information in real time and combines the vehicle's own GPS position information to perform lane matching.The latter method is based on RSS ranging and position of the RSU equipment,combined with Dead Reckoning(DR)to complete the vehicle position estimation.These two methods required only one RSU device to complete the position estimation,and get rid of the dependence on the number of RSU device deployments.Finally,this paper proposed a RSU-assisted GPS and RSS cooperative positioning method for a CV environment.The rough position information of the GPS was combined with RSS ranging and DR to obtain preliminary position estimated coordinates of the connected vehicles.Bayesian filtering is performed on the preliminary position estimate to obtain a more accurate preliminary position estimate.The final position estimated coordinates obtained after the data fusion are combined with the MAP data sent by the RSU device to match the lane where the vehicle is located.The simulation and field test results show that as the vehicle speed increases and the communication distance of the intelligent CV equipment increases,the positioning accuracy decreases.In the range of 0 to 40 meters from the OBU to the RSU device,the average lane positioning accuracy of GPS can be improved by 21%,and the positioning accuracy can also be improved by at least 9.5% at the distance boundary.