Vehicle Moving State Recognition Method Based on Anomaly Detection and Its Applications on Dynamic In-car Navigation Systems

Abstract

In order to efiectively determine whether a vehicle is turning or not, we propose a method to map arbitrary consecutive GPS heading information to two dimensional feature space. Through analyzing the distribution of the data, we decide to use two dimensional Gaussian distribution to build the anomaly detection model. After that, the feature space can be divided into two categories (one indicates the vehicle is traveling on a straight road and the other indicates it is doing a turning maneuver) by setting the threshold of the model. Finally, we make use of the labeled data and F-measure method to choose the threshold value. The experimental results show that the model built in this way has good generalization. Based on the above research achievement, a vehicle moving state recognition learning system for Dynamic In-Car Navigation Systems is designed and implemented, and this system is applied to improving the map-matching algorithm. The improved algorithm is tested on a complex urban road network and the experimental results show that the new algorithm can improve the performance of the junction match.