Vehicle Trajectory Dynamic Error Correction Based On Kalman Filtering And Its Research In Map Matching

With the rapid development of society, the phenomenon of traffic jam grows serious witheach passing day. For the safety and efficiency of transportation, the timeliness and accuracyof navigation system on the vehicle is endowed with higher requirements. As a keytechnology of the navigation of location system on the vehicle, Map Matching can greatlyreduce the system hardware resources and cost, and effectively overcome the naturalpositioning error in the limitations of current technology. Research of map matching invehicle navigation system has become a hot issue.Map Matching technology is corrected in real time with matching algorithm for vehiclelocation information, and display accurately on the corresponding position of the digital roadmap. The process of Map Matching mainly includes three segments, that is, the determinationof the error region, the choice of the matching path and the correction of the positioningresults. Its accuracy is mainly to overcome the GPS vehicle dynamic error and digital maperror, so that the GPS locus can be matched to the corresponding position on the digital roadmap. This paper proceeds from GPS vehicle dynamic positioning error, establishes a propererror model, and corrects the error based on the advantages of Kalman-filtering for errorprocessing, then the revised positioning information can be applied to map matching process.Firstly the research background, significance and the domestic and foreign developmentcondition of the map matching technology are introduced. And then the influence factors ofthe map matching algorithm is given, meanwhile the commonly used map matchingalgorithms are described. Then it’s introduced the effect of map matching error, including theerror of GPS vehicle navigation positioning, the error of coordinate transformation, and theerror of digital map. It’s emphatically analyzed the error of analysis of GPS vehicle dynamicpositioning, given the error model based on Second-order Markov. Then the error isprocessed with Kalman-filtering, and its effectiveness and feasibility of the error correctioncan be tested by the simulation experiments. Finally, the modified GPS vehicle locationinformation is integrate into the map matching process, and the algorithm can enhance theaccuracy of path selection to some extent, and can reduce the error of direction along the road.