A novel adaptive map-matching algorithm in vehicular navigation system

Map-Matching, integrates the vehicle positioning data with digital road network, is an important positioning technique in the vehicle navigation system. An adaptive-fuzzy-network based on C-measure map-matching algorithm and its advantages were briefly summarized firstly, in which the C-measure was defined to represent the certainty of the car's existence on the corresponding road. But, as this algorithm emphasizes on current positioning data only, the matching accuracy decreases in complicated road network due to the lack of data. In order to improve precision of vehicle tracking system, a strategy was proposed. This strategy employed history positioning information to overcome the disadvantage of the original algorithm in information insufficiency, and the distance between two history trajectory curves was defined by an average Fréchet distance measure to implement curves matching instead of point matching. Owing to increase historic information input variable in the fuzzy network, the number of fuzzy reasoning rules was increased, and operating efficiency of the fuzzy network was reduced. For this reason, a scheme to simplify reasoning rules and to enhance the efficiency was proposed by using hierarchical fuzzy control technique. Additionally, the learning algorithm was updated to support the algorithm. The experimental results demonstrate the effectiveness of this proposed algorithm.