Semi-Automated Generation of Road Transition Lines Using Mobile Laser Scanning Data

Ye, Chengming; Li, Jonathan; Han, Jiang; Zhao, He; Ma, Lingfei; Chapman, Michael A.

doi:10.1109/tits.2019.2904735

Cited by 28 publications

(14 citation statements)

References 36 publications

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“…The authors in previous study [63] adopted a gradient value as a multi-threshold to extract lane markings successfully. In previous study [64], with the use of multi-threshold methods, the precision of the lane marking points was 90.80%. With these methods, point clouds need to segment and block, with sizes being uncertain.…”

Section: Point-cloud-based Methodsmentioning

confidence: 92%

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

Zheng

Yang

et al. 2019

Sustainability

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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.

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Section: Point-cloud-based Methodsmentioning

confidence: 92%

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

Zheng

Yang

et al. 2019

Sustainability

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“…A comprehensive summary of these methodological differences is shown in Table 3. As it can be seen, automatic road marking detection and classification using data from LiDAR scanners is more than feasible, and may be a standard data source not only for road marking inspection but for applications such as driving line generation [77,82,83]. Regarding other applications, such as autonomous driving where real-time information is required, road marking recognition is carried out using RGB images analysed by machine learning or deep learning classification models [84][85][86][87].…”

Section: Road Markings and Driving Lanesmentioning

confidence: 99%

Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring

et al. 2019

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Improving the resilience of infrastructures is key to reduce their risk vulnerability and mitigate impact from hazards at different levels (e.g., from increasing extreme events, driven by climate change); or from human-made events such as: accidents, vandalism or terrorist actions. One of the most relevant aspects of resilience is preparation. This is directly related to: (i) the risk prediction capability; (ii) the infrastructure monitoring; and (iii) the systems contributing to anticipate, prevent and prepare the infrastructure for potential damage. This work focuses on those methods and technologies that contribute to more efficient and automated infrastructure monitoring. Therefore, a review that summarizes the state of the art of LiDAR (Light Detection And Ranging)-based data processing is presented, giving a special emphasis to road and railway infrastructure. The most relevant applications related to monitoring and inventory transport infrastructures are discussed. Furthermore, different commercial LiDAR-based terrestrial systems are described and compared to offer a broad scope of the available sensors and tools to remote monitoring infrastructures based on terrestrial systems.

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“…As it can be seen, deep learning frameworks are common on the state-of-the-art for road marking extraction in 3D point clouds. Finally, regarding driving lanes extraction, it is worth mentioning the work from Li et al [25,26], where 3D roadmaps are generated using the information from previously segmented road markings. Then, lane geometries and lane centrelines can be generated, including transition lines.…”

Section: Introductionmentioning

confidence: 99%

3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads

et al. 2020

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Building information modeling (BIM) is a process that has shown great potential in the building industry, but it has not reached the same level of maturity for transportation infrastructure. There is a standardization need for information exchange and management processes in the infrastructure that integrates BIM and Geographic Information Systems (GIS). Currently, the Industry Foundation Classes standard has harmonized different infrastructures under the Industry Foundation Classes (IFC) 4.3 release. Furthermore, the usage of remote sensing technologies such as laser scanning for infrastructure monitoring is becoming more common. This paper presents a semi-automated framework that takes as input a raw point cloud from a mobile mapping system, and outputs an IFC-compliant file that models the alignment and the centreline of each road lane in a highway road. The point cloud processing methodology is validated for two of its key steps, namely road marking processing and alignment and road line extraction, and a UML diagram is designed for the definition of the alignment entity from the point cloud data.

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Semi-Automated Generation of Road Transition Lines Using Mobile Laser Scanning Data

Cited by 28 publications

References 36 publications

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

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

Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring

3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads

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