Lane-Level Road Map Construction considering Vehicle Lane-Changing Behavior

Abstract

In recent years, the construction of lane-level road maps has received extensive attention from industry and academia. It has been widely studied because this kind of map provides the foundation for much research, such as high-precision navigation, driving behavior analysis, and traffic analysis. Trajectory-based crowd-mapping is an emerging approach to lane-level map construction. However, the major problem is that existing methods neglect modeling the trajectory distribution in the longitudinal direction of the road, which significantly impacts precision. Thus, this article proposes a two-stage method based on vehicle lane-changing behavior to model the road’s lateral and longitudinal trajectory distributions simultaneously. In the first stage, lane-changing behaviors are extracted from vehicle trajectories. In the second stage, the lane extraction model is established using the weighted constrained Gaussian mixture model and hidden Markov model to estimate lane parameters (e.g., lane counts and lane centerline) on each road cross section. Then accurate and continuous lane centerlines can be constructed accordingly. The proposed method is verified using vehicle trajectory data collected from the crowdsourced platform named Mapillary. The results show that the proposed method can construct lane-level road information satisfactorily.

Urban Road Lane Number Mining from Low-Frequency Floating Car Data Based on Deep Learning

“…To assess the performance of the deep learning approach presented in this study, we conducted a quantitative comparison with several classification methods, including Kernel Density Estimation [7], Naïve Bayesian, Constraint Gaussian Mixture Model [11], Fuzzy Logic [13], Gradient Lifting Decision Tree [14], The Least Square Estimate to Constrain Gaussian Mixture Model [16], and the Weighted Constrained Gaussian Mixture Model and Hidden Markov Model [17]. The results of the lane number identification comparisons are presented in Table 6.…”

“…Kernel Density Estimation [7] 74.2% Naïve Bayesian [9] 83.7% Constraint Gaussian Mixture Model [11] 85.2% Fuzzy Logic [13] 82.9% Gradient Lifting Decision Tree [14] 83.9% The Least Squares Estimate to Constrain the Gaussian Mixture Model [16] 83.3% The Weighted Constrained Gaussian Mixture Model and Hidden Markov Model [17] 78.6%…”

A Review of Crowdsourcing Update Methods for High-Definition Maps

“…Low-frequency FCD typically originate from public transportation vehicles such as taxis and buses. The sampling frequency for this type of data generally falls within the range of 10 s to 60 s, with positioning accuracy ranging from 10 m to 30 m [24][25][26]. Li et al proposed a method that utilizes FCD data collected from taxis in Wuhan to detect auxiliary lanes at intersections [24].…”