Automatic Calibration of Road Intersection Topology using Trajectories

Abstract

The inaccuracy of road intersection in digital road map easily brings serious effects on the mobile navigation and other applications. Massive traveling trajectories of thousands of vehicles enable frequent updating of road intersection topology. In this paper, we first expand the road intersection detection issue into a topology calibration problem for road intersection influence zone. Distinct from the existing road intersection update methods, we not only determine the location and coverage of road intersection, but figure out incorrect or missing turning paths within whole influence zone based on unmatched trajectories as compared to the existing map. The important challenges of calibration issue include that trajectories are mixing with exceptional data, and road intersections are of different sizes and shapes, etc. To address above challenges, we propose a three-phase calibration framework, called CITT. It is composed of trajectory quality improving, core zone detection, and topology calibration within road intersection influence zone. From such components it can automatically obtain high quality topology of road intersection influence zone. Extensive experiments compared with the state-of-the-art methods using trajectory data obtained from Didi Chuxing and Chicago campus shuttles demonstrate that CITT method has strong stability and robustness and significantly outperforms the existing methods.