Construction process of a three-dimensional roadway geometry map for autonomous driving

Zhang

et al. 2018

IJGI

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.

Section: Literature Reviewmentioning

confidence: 99%

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

Zhang

et al. 2018

IJGI

“…In previous study [90], the authors proposed a progressive correction algorithm to reduce the number of control parameters in the B-spline curve to accurately represent the lane geometry. Based on this research, the authors in previous study [95] proposed an adaptive curve refinement method based on a dominant point that used a B-spline mathematical curve model to describe the 3D road geometry information, which took into account the road shape factors (the curvature, arc, etc.). This method reduced the number of nodes and control points of the B-spline road model while ensuring the accuracy of the road network.…”

Section: Cubic Spline Curvesmentioning

confidence: 99%

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Yang

et al. 2019

Sustainability

Autonomous driving is experiencing rapid development. A lane-level map is essential for autonomous driving, and a lane-level road network is a fundamental part of a lane-level map. A large amount of research has been performed on lane-level road network generation based on various on-board systems. However, there is a lack of analysis and summaries with regards to previous work. This paper presents an overview of lane-level road network generation techniques for the lane-level maps of autonomous vehicles with on-board systems, including the representation and generation of lane-level road networks. First, sensors for lane-level road network data collection are discussed. Then, an overview of the lane-level road geometry extraction methods and mathematical modeling of a lane-level road network is presented. The methodologies, advantages, limitations, and summaries of the two parts are analyzed individually. Next, the classic logic formats of a lane-level road network are discussed. Finally, the survey summarizes the results of the review.

“…Virtual lanes are often used to express the traffic details of intersections in lane-level road maps [37]. Popular functions for describing lanes include the circular arc curve [38], clothoid curve [39], cubic Hermite curve [40], and B-spline curve [41]. We first determined the road sections that are included in the intersection and pass through a point adjacent to the road terminal.…”

Section: Intersection Graph Constructionmentioning

confidence: 99%

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

Song²,

et al. 2019

IJGI

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.