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.
In the process of addressing, when people use words to express indistinctly, they often draw simple sketches to assist expression, which helps people to form a simple spatial scene in the brain and correspond to the actual scene one by one, and finally locate and find the target address. How to establish an one-to-one mapping relationship between the spatial objects in the hand-drawn sketch and in the vector map is the key to the realization of map addressing and location, and this process is also the process of map matching. This paper aims to address difficult problems associated with the features of hand-drawn sketches and vector map matching in order to improve the use of all potential matching points designed for application in hand-drawn sketches and spatial relation matrix structures of vector maps. To accomplish this, we use the N-queen problem solving process and improve the tabu search algorithm. In the matching process under the constraint of a single spatial relationship, and the hierarchical matching process under the constraint of multiple spatial relations, this study verifies the quality of the spatial relationship and the feasibility and effectiveness of the matching method of hand-drawn sketches and vector maps using the improved tabu search algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.