Virtual running of vehicle trajectories for automatic map generation

Abstract

This paper proposes a new method called Virtual Run for extracting the representative trajectories from a set of similar trajectories by readjusting the sequence of movements of vehicles. This method uses convex hull and Euclidean minimum spanning tree to move the trajectory sets, so that it can generate the representative trajectories. Those representative trajectories are used to generate the digital road map which is the final goal of this research. Generally, the well-known Frechet distance measure is applied to estimate the similarity between a pair of trajectories. However, in this paper, we present that our new method is more effective than the previous methods on the basis of an experiment and evaluation. Also we show the application of generating the digital road map using the presented method. For the evaluation, we collected a set of actual trajectory data from 3,000 taxis in the Gangnam and Seongnam areas. The experimental results show that our approach can solve the problems associated with the special case of loops, which are not clearly solved by the previous approaches. The results also show that our method is capable of generating a digital road map from a set of trajectories.