A lane-level road network model with global continuity

Zhang, Tao; Arrigoni, Stefano; Garozzo, M.; Yang, Dian Ge; Cheli, Federico

doi:10.1016/j.trc.2016.07.003

Cited by 26 publications

(16 citation statements)

References 12 publications

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“…These studies were focused on the geometric representation of a lane model. Tao et al defined the lanes of the HD road network with lane arcs, lane attributes, intersections and intersection attributes [21]. These descriptions facilitate the stimulation of lanes in the HD road network.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

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

Zheng

Zhang

et al. 2018

IJGI

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“…Another method involves using only position data. Zhang et al and Zheng et al proposed a road network model for constructing a high-precision road network [28,29]. However, these methods based on point trajectories require a large amount of data collection and calculation.…”

Section: Lane-level Road Network Generation With a Probe Vehiclementioning

confidence: 99%

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

Zheng

Song²,

et al. 2019

IJGI

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With the development of autonomous driving, lane-level maps have attracted significant attention. Since the lane-level road network is an important part of the lane-level map, the efficient, low-cost, and automatic generation of lane-level road networks has become increasingly important. We propose a new method here that generates lane-level road networks using only position information based on an autonomous vehicle and the existing lane-level road networks from the existing road-level professionally surveyed without lane details. This method uses the parallel relationship between the centerline of a lane and the centerline of the corresponding segment. Since the direct point-by-point computation is huge, we propose a method based on a trajectory-similarity-join pruning strategy (TSJ-PS). This method uses a filter-and-verify search framework. First, it performs quick segmentation based on the minimum distance and then uses the similarity of two trajectories to prune the trajectory similarity join. Next, it calculates the centerline trajectory for lanes using the simulation transformation model by the unpruned trajectory points. Finally, we demonstrate the efficiency of the algorithm and generate a lane-level road network via experiments on a real road.

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“…And there have been several studies employing the CHS to describe the shape of lanes , however, the method mentioned in [4] applies CHS to the whole lanes and even virtual lanes in the intersections without distinguishing the control points with different functions.…”

Section: Ictte 2017mentioning

confidence: 99%

“…Thus, the map model is supposed to be an abstract representation of the map database [4]. In terms of map model, there are a nu mber of map standards containing specific exp lanations on the map model which we can refer to.…”

Section: Introductionmentioning

confidence: 99%

“…Besides the standards above, Qing Zhu et al [7] establish a 3-D road network which is composed of three parts: roadway centerline, carriageway, and lane. Tao Zhang et al [4] design a lane-level map model including details on intersections and lanes . Jiang Liu et al [8] generate enhanced intersection model and use circular to illustrate the virtual lane inside the intersection.…”

Section: Introductionmentioning

confidence: 99%

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Design of a Multi-layer Lane-Level Map for Vehicle Route Planning

et al. 2017

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Abstract. With the development of intelligent transportation system, there occurs further demand for high precision localization and route planning, and simultaneously the traditional road-level map fails to meet with this requirement, by which this paper is motivated. In this paper, t he three-layer lane-level map architecture for vehicle path guidance is established, and the mathematical models of road-level layer, intermediate layer and lane-level layer are designed considering efficiency and precision. T he geometric model of the lane-level layer of the map is characterized by Cubic Hermite Spline for continuity. A method of generating the lane geometry with fixed and variable control points is proposed, which can effectively ensure the accuracy with limited num ber of control points. In experimental part, a multi-layer map of an intersection is built to validate the map model, and an example of a local map was generated with the lane-level geometry.

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A lane-level road network model with global continuity

Cited by 26 publications

References 12 publications

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

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

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

Design of a Multi-layer Lane-Level Map for Vehicle Route Planning

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