An improved hidden Markov model-based map matching algorithm considering candidate point grouping and trajectory connectivity

Abstract

ABSTRACT The hidden Markov model-based map matching algorithm (HMM-MM) is an effective method for online vehicle navigation and offline trajectory position correction. Common HMM-MMs are susceptible to the influence of adjacent road segment endpoints and similar parallel roads, because the multi-index probability model may ignore some indexes when the probability of other indexes is high. This makes the map-matching result not meet the assumption that vehicles always travel the shortest or optimal path, and it cannot guarantee that the trajectory points can match to the nearest position of the maximum likelihood road segment, resulting in poor accuracy. In this paper, an IHMM-MM is proposed. IHMM-MM (1) modifies the definition of transition probability and no longer takes the straight-line distance between trajectory points as the reference for the shortest path length between candidate point pairs. (2) supplements the definition of observation probability and introduces the point-line relation function to screen and group candidate points. (3) adds additional logic outside the HMM probability model to consider the trajectory connectivity and fill in the key trajectory points where the vehicles travel. Experiments show that the IHMM-MM can effectively improve the sampling frequency of trajectory data and has better performance in complex urban road environments.