Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Li, Lin; Li, Daigang; Xing, Xiaoyan; Yang, Fan; Rong, Wei; Zhu, Haihong

doi:10.3390/ijgi6120403

Cited by 23 publications

(10 citation statements)

References 34 publications

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“…Jia et al used PCA to generate line segments from points after clustering them, finding road segments with fit direction and length from the original points [30]. Similarly, Lin et al applied the PCA method for road-skeleton segmentation using global positioning system (GPS) tracking points, and they further extracted intersections from the road network based on the direction of road segments [31].…”

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confidence: 99%

A High-Definition Road-Network Model for Self-Driving Vehicles

Zheng

Zhang

et al. 2018

IJGI

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High-definition (HD) maps have gained increasing attention in highly automated driving technology and show great significance for self-driving cars. An HD road network (HDRN) is one of the most important parts of an HD map. To date, there have been few studies focusing on road and road-segment extraction in the automatic generation of an HDRN. To improve the precision of an HDRN further and represent the topological relations between road segments and lanes better, in this paper, we propose an HDRN model (HDRNM) for a self-driving car. The HDRNM divides the HDRN into a road-segment network layer and a road-network layer. It includes road segments, attributes and geometric topological relations between lanes, as well as relations between road segments and lanes. We define the place in a road segment where the attribute changes as a linear event point. The road segment serves as a linear benchmark, and the linear event point from the road segment is mapped to its lanes via their relative positions to segment the lanes. Then, the HDRN is automatically generated from road centerlines collected by a mobile mapping vehicle through a multi-directional constraint principal component analysis method. Finally, an experiment proves the effectiveness of this HDRNM.

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confidence: 99%

A High-Definition Road-Network Model for Self-Driving Vehicles

Zheng

Zhang

et al. 2018

IJGI

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“…Because noise exists, the sliced wall should be thinned first. Mean-shift is a mode-seeking algorithm that searches for the maximum densities of local neighborhoods from discrete points [28]. The algorithm is iterated to obtain a stable result, that is, a certain number of iterations are reached or all points move to the location of the local maximum density in the support neighborhood.…”

Section: Proposed Methodsmentioning

confidence: 99%

Automatic Indoor Reconstruction from Point Clouds in Multi-room Environments with Curved Walls

Yang

Zhou

et al. 2019

Sensors

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Recent developments in laser scanning systems have inspired substantial interest in indoor modeling. Semantically rich indoor models are required in many fields. Despite the rapid development of 3D indoor reconstruction methods for building interiors from point clouds, the indoor reconstruction of multi-room environments with curved walls is still not resolved. This study proposed a novel straight and curved line tracking method followed by a straight line test. Robust parameters are used, and a novel straight line regularization method is achieved using constrained least squares. The method constructs a cell complex with both straight lines and curved lines, and the indoor reconstruction is transformed into a labeling problem that is solved based on a novel Markov Random Field formulation. The optimal labeling is found by minimizing an energy function by applying a minimum graph cut approach. Detailed experiments were conducted, and the results indicate that the proposed method is well suited for 3D indoor modeling in multi-room indoor environments with curved walls.

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“…Recently, the total least squares [27] and turning-point detection methods [28] were proposed to segment and group GNSS trajectory data. After the segmentation and grouping, the intersection position and road segment were determined using the phenomena of intersecting and crossing of different groups.…”

Section: Road Geometry Data Updatingmentioning

confidence: 99%

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

Wu³

2019

IJGI

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With the rapid development of cities, the geographic information of urban blocks is also changing rapidly. However, traditional methods of updating road data cannot keep up with this development because they require a high level of professional expertise for operation and are very time-consuming. In this paper, we develop a novel method for extracting missing roadways by reconstructing the topology of the roads from big mobile navigation trajectory data. The three main steps include filtering of original navigation trajectory data, extracting the road centerline from navigation points, and establishing the topology of existing roads. First, data from pedestrians and drivers on existing roads were deleted from the raw data. Second, the centerlines of city block roads were extracted using the RSC (ring-stepping clustering) method proposed herein. Finally, the topologies of missing roads and the connections between missing and existing roads were built. A complex urban block with an area of 5.76 square kilometers was selected as the case study area. The validity of the proposed method was verified using a dataset consisting of five days of mobile navigation trajectory data. The experimental results showed that the average absolute error of the length of the generated centerlines was 1.84 m. Comparative analysis with other existing road extraction methods showed that the F-score performance of the proposed method was much better than previous methods.

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Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Cited by 23 publications

References 34 publications

A High-Definition Road-Network Model for Self-Driving Vehicles

A High-Definition Road-Network Model for Self-Driving Vehicles

Automatic Indoor Reconstruction from Point Clouds in Multi-room Environments with Curved Walls

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

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