Improvement on vehicle trajectory reconstruction using Geometric Road Network (GRN)

Abstract

This paper presents the technique using geometric road network (GRN) to improve data fusion method for vehicle trajectory analysis and reconstruction. The experiment used two methods of data fusion which are Linear-interpolation-based one and Kalman-filter-based one and used the simulation of detection and localization of vehicle moving in straight, big zigzag, small zigzag and arbitrary trajectories, with localization errors and multi-level loss of data. By comparing the data fusion methods with GRN and the ones without GRN, the result shows that the data fusion methods with GRN can reduce localization errors better than the ones without GRN in all level of data loss and all type of trajectories. In conclusion, using GRN can improve the efficiency of vehicle trajectory analysis and reconstruction.