Research On Positioning Method Of Vehicle Driving Road Segment Based On GPS And Altimeter

For intelligent vehicles,determining the position of the vehicle in the road section is the key to control and decision-making.It is the basis for acquiring traffic prior information in the road network database in real time,and it is also a necessary condition for performing various tasks of the macro-planning.Broadly speaking,intelligent vehicle segment positioning includes: obtainning positioning information;and determining the position of the intelligent vehicle in a road network database.In order to ensure positioning accuracy,intelligent vehicles generally use high-precision instruments,which are expensive and limit the production of intelligent vehicles.The positioning accuracy of general navigation devices is relatively low,and they are mostly concentrated in two-dimension,which cannot meet the positioning requirements of intelligent vehicles.In order to reduce costs and improve positioning accuracy,this article uses low-cost GPS and altimeter to obtain three-dimensional positioning information,then uses kalman filtering to improve accuracy and uses coordinate increments in map matching to modify the positioning information,complete on the positioning in the network database,finally realizes the accurate three-dimensional positioning function of the intelligent vehicle.Research on three-dimensional position estimation of intelligent vehicle based on kalman filtering.In view of the problem of high altitude error for low cost GPS,this paper selects altimeter to obtain height information and coordinates position information acquired by GPS to obtain the three-dimensional position coordinates of the intelligent vehicle.Aiming at the problem of poor adaptability in the traditional current statistical model,an improved current statistical model is introduced,and the best forgetting factor is introduced.An adaptive kalman filter algorithm based on an improved current statistical model is established to accurately estimate the three-dimensional position coordinates.Research on positioning of intelligent vehicles in road network database.Through in-depth study of map matching concepts and typical map matching algorithms,traditional algorithms cannot guarantee the real-time and accuracy of map matching at the same time.This paper uses the kalman filter based on improved current statistical model to obtain three-dimensional positioning information.Selecting road map selection based on DS evidence theory map matching algorithm to determine the initial projection point,and then using the coordinate increment to correct the positioning information based on the projection point,and finally projecting onto the corresponding road segment to complete the positioning of intelligent vehicle in the road network database.This paper only uses map matching algorithm based on DS evidence theory to select road segments and determine the initial projection points.It changes the shortcomings of road selection and projection every time and every moment in traditional algorithms to ensure the real-time performance of the entire map matching;Correct the positioning point to reduce the error and improve the accuracy of map matching.The simulation of the positioning scheme of the driving segment of the intelligent vehicle was carried out.By setting the conditions and comparing with other algorithms,the adaptive kalman filter based on the improved current statistical model can guarantee the accuracy of the three-dimensional positioning,and the filtering process is not affected by the parameter settings and the self-adaptability is better.In addition,using the development vehicle driving simulator road network database developed by the State Key Laboratory of Automotive Simulation and Control of Jilin University to simulate the map of prior information stored in the navigation and positioning,the simulation of the map matching algorithm based on incremental coordinates was performed.It is proved that the algorithm has high accuracy and can realize the positioning of three-dimensional driving sections of intelligent vehicles in the road network database.