The aided navigation method based on track matching has evident advantages in both anti-external interference and independence. Its key concept is to compare high-precision road data in navigation electronic maps or network databases with vehicle trajectory data obtained from sensor measurement. The location of a vehicle can be determined based on road information; hence

the error of an integrated navigation system can be prevented and localization accuracy can be improved to a certain extent. In the case of special road conditions such as intersections

cross bridges

or ramps

several false road matches can occur

which directly affect the positioning of navigation results. A vector road

which is generally composed of discrete point sets

exhibits a high degree of accuracy in positioning coordinates and topological relations. Thus

the relationship between road topology and path can be fully utilized in a matching algorithm. A vector road matching algorithm based on road tracing is proposed to address the problem in matching algorithms based on road shape feature. A road is preprocessed into a ring topology

which includes nodes

roads

and intersections

in accordance with road requirements. The four states are divided as follows:initialization

tracing

intersection

and searching. The algorithm begins to process the road tracing based on actual state characteristics. During road tracing

different processing steps can be performed for the matching. The error correction of vehicle trajectory can be achieved by analyzing the road tracing results. Moreover

the algorithm also statistically analyzes the road tracing conditions for intersection tracking under different thresholds to ensure the accuracy of the matching results. The vector road matching algorithm based on topological path tracing can considerably improve the results of navigation error correction in real time and eliminate mismatches due to complex junctions and other sections. Repositioning can be rapidly performed via the search set from the state of positioning loss. The distance thresholds on the intersection are set for accurate intersection state matching

which can be selected according to the field condition of the corresponding distance value. The vector road data of Beijing city west 5th ring and the Miyun area were obtained via the Global Positioning System (GPS)-Real Time Kinematic

and the inertial navigation trajectory data of field cars were adopted in the simulation experiments. The selection of the intersection distance threshold and the comparison tests for the matching effect and real-time performance between the proposed algorithm and the traditional matching algorithm based on road shape feature are completed during the experiment. Matching accuracy and matching time are used as the standards for every test. The results show that when distance threshold is 20 m

the intersection matching exhibits the highest accuracy

which can achieve a maximum value of 93.5%. This study verifies the effect of the algorithm in a real road test. The experimental results show that the proposed road tracking algorithm demonstrates better matching accuracy for driving on intersections than the traditional algorithm based on shape. Its matching accuracy rate is 90.2%

and its matching speed increases by 4 to 8 times in the same road segment. The vector road matching algorithm based on path tracing has evident advantages in terms of real-time tracking and the matching effect. The algorithm is simple

feasible

and its matching accuracy is higher

thereby effectively fulfilling the requirements for integrated navigation matching. The proposed algorithm has several advantages over the traditional matching algorithm

including higher accuracy rate

excellent stability

and faster match speed. It can be used to aid in integrated navigation under special conditions

such as a GPS signal blind zone and signal interference environment

to compensate for the lack of autonomy and anti-interference in traditional navigation based on satellites. The experiments also demonstrate the effectiveness and practicality of the proposed method.However

the algorithm still requires further optimization and analysis in terms of adjusting parameters as well as more matching methods. We aim to devise more effective aided information and matching methods for integrated navigation in the future.