Automatización en la extracción del trazado y el inventario geométrico de carreteras mediante sistemas de cartografiado móvil

[ES] En esta Tesis Doctoral se plantea el uso de los sistemas de cartografiado móvil (MMS-Mapping Mobile System) tanto para la obtención de inventarios geométricos de carreteras como para la automatización del trazado asociado a las mismas. Una de las mayores dificultades que tienen estos sistemas es la falta de software que permita automatizar la extracción de información semántica de interés, como son las marcas viales o la plataforma de la carretera. Y a partir de esta información poder obtener la delineación de la vía, su inventario geométrico (p. ej.: nº carriles, ancho plataforma, ancho arcenes, ancho carriles), así como el trazado geométrico tanto horizontal (p. ej.: rectas, curvas circulares, clotoides) como vertical (p. ej.: rasantes, acuerdos verticales, peraltes). Con este tipo de desarrollos será posible realizar un control exhaustivo y rápido de las carreteras por parte de las administraciones gestoras, debido a que en la información derivada puedan identificarse problemas relacionados con el estado de las infraestructuras viarias, su inventariado geométrico e incluso la mejora de la seguridad vial. No obstante, uno de los mayores problemas a los que se hace frente en esta Tesis Doctoral es el tratamiento de toda la información capturada; más concretamente a los procesos de segmentación y modelización de objetos o entidades, debido al carácter desorganizado y sin semántica de las nubes de puntos. Por ello planteamos una metodología automática a partir de la cual obtener los diferentes elementos de la carretera mediante el uso de sistemas LiDAR móvil (MLS), en el que un sistema láser activo de medida permite la documentación de las vías mediante nubes de puntos tridimensionales. La primera fase que se llevará a cabo es obtener de manera automática tanto la plataforma de la carretera como las marcas viales. Debido a la gran cantidad de datos capturados por el MLS se ha desarrollado una metodología basada en procesos de segmentación y clasificación a través de la cual detectar tanto la plataforma del vial como reconocer semánticamente la marcas viales (p. ej.: marca vial izquierda/derecha y diferentes marcas centrales), para así poder llevar a cabo la delineación de la infraestructura viaria en formato CAD. Realizado el proceso de segmentación y clasificación estamos ya en disposición de obtener los diferentes elementos geométricos contenidos en la vía. En primer lugar comenzaremos estimando las características geométricas del trazado horizontal, que cumplen con los requisitos de la instrucción de carreteras (Norma 3.1- IC. Trazado). Dicho proceso se realizará mediante técnicas de parametrización y filtrado de la marca principal de la vía, ya que esta actuará de eje de la carretera y con ella podremos estimar los elementos geométricos que componen su trazado horizontal (p. ej.: líneas rectas, curvas circulares y clotoides). En segundo lugar estimaremos las características geométricas del trazado vertical. Este proceso se llevará a cabo mediante técnicas de regresión ortogonal, a través del análisis de componentes principales (PCA), así como una parametrización y filtrado de la calzada con la que determinar los elementos geométricos que mejor definen su trazado en alzado (p. ej.: rasantes y acuerdos verticales), así como en su sección transversal (p. ej.: peraltes). La última fase de la metodología será obtener los perfiles transversales de la carretera, a partir de la segmentación de la vía como de la clasificación de las marcas viales, con las que poder obtener el inventario geométrico de la sección transversal (p. ej.: nº carriles, ancho plataforma, ancho arcenes, ancho carriles). Esta Tesis Doctoral es el resultado de un compendio de tres artículos científicos publicados en revistas internacionales de alto impacto (ver Apéndice A)

Building demolition estimation in urban road widening projects using as-is BIM models

Building demolition caused by urban road widening projects can lead to engineering, economic, and environmental issues and should be planned at the design stage. Based on as-is BIM, this paper proposes a method to estimate the building demolition caused by urban road widening using online map data and statistics on government websites. The as-is BIM models of the existing old road and its surrounding buildings are created, and the BIM models of the newly widened road are built based on the as-is BIM models considering road components in accordance with road engineering expressions to assist building demolition estimation using clash detection. This paper presents a cost-effective building demolition estimation in urban road widening projects without field surveys. It was tested on the M4 Motorway project in London. It has been proved to be a very practical approach to facilitate urban road planning and decision making

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

Recent advances in autonomous vehicles (AVs) are exponential. Prominent car manufacturers, academic institutions, and corresponding governmental departments around the world are taking active roles in the AV industry. Although the attempts to integrate AV technology into smart roads and smart cities have been in the works for more than half a century, the High Definition Road Maps (HDRMs) that assists full self-driving autonomous vehicles did not yet exist. Mobile Laser Scanning (MLS) has enormous potential in the construction of HDRMs due to its flexibility in collecting wide coverage of street scenes and 3D information on scanned targets. However, without proper and efficient execution, it is difficult to generate HDRMs from MLS point clouds. This study recognizes the research gaps and difficulties in generating transition lines (the paths that pass through a road intersection) in road intersections from MLS point clouds. The proposed method contains three modules: road surface detection, lane marking extraction, and transition line generation. Firstly, the points covering road surface are extracted using the voxel- based upward-growing and the improved region growing. Then, lane markings are extracted and identified according to the multi-thresholding and the geometric filtering. Finally, transition lines are generated through a combination of the lane node structure generation algorithm and the cubic Catmull-Rom spline algorithm. The experimental results demonstrate that transition lines can be successfully generated for both T- and cross-intersections with promising accuracy. In the validation of lane marking extraction using the manually interpreted lane marking points, the method can achieve 90.80% precision, 92.07% recall, and 91.43% F1-score, respectively. The success rate of transition line generation is 96.5%. Furthermore, the Buffer-overlay-statistics (BOS) method validates that the proposed method can generate lane centerlines and transition lines within 20 cm-level localization accuracy from MLS point clouds. In addition, a comparative study is conducted to indicate the better performance of the proposed road marking extraction method than that of three other existing methods. In conclusion, this study makes a considerable contribution to the research on generating transition lines for HDRMs, which further contributes to the research of AVs

Road Information Extraction from Mobile LiDAR Point Clouds using Deep Neural Networks

Urban roads, as one of the essential transportation infrastructures, provide considerable motivations for rapid urban sprawl and bring notable economic and social benefits. Accurate and efficient extraction of road information plays a significant role in the development of autonomous vehicles (AVs) and high-definition (HD) maps. Mobile laser scanning (MLS) systems have been widely used for many transportation-related studies and applications in road inventory, including road object detection, pavement inspection, road marking segmentation and classification, and road boundary extraction, benefiting from their large-scale data coverage, high surveying flexibility, high measurement accuracy, and reduced weather sensitivity. Road information from MLS point clouds is significant for road infrastructure planning and maintenance, and have an important impact on transportation-related policymaking, driving behaviour regulation, and traffic efficiency enhancement. Compared to the existing threshold-based and rule-based road information extraction methods, deep learning methods have demonstrated superior performance in 3D road object segmentation and classification tasks. However, three main challenges remain that impede deep learning methods for precisely and robustly extracting road information from MLS point clouds. (1) Point clouds obtained from MLS systems are always in large-volume and irregular formats, which has presented significant challenges for managing and processing such massive unstructured points. (2) Variations in point density and intensity are inevitable because of the profiling scanning mechanism of MLS systems. (3) Due to occlusions and the limited scanning range of onboard sensors, some road objects are incomplete, which considerably degrades the performance of threshold-based methods to extract road information. To deal with these challenges, this doctoral thesis proposes several deep neural networks that encode inherent point cloud features and extract road information. These novel deep learning models have been tested by several datasets to deliver robust and accurate road information extraction results compared to state-of-the-art deep learning methods in complex urban environments. First, an end-to-end feature extraction framework for 3D point cloud segmentation is proposed using dynamic point-wise convolutional operations at multiple scales. This framework is less sensitive to data distribution and computational power. Second, a capsule-based deep learning framework to extract and classify road markings is developed to update road information and support HD maps. It demonstrates the practical application of combining capsule networks with hierarchical feature encodings of georeferenced feature images. Third, a novel deep learning framework for road boundary completion is developed using MLS point clouds and satellite imagery, based on the U-shaped network and the conditional deep convolutional generative adversarial network (c-DCGAN). Empirical evidence obtained from experiments compared with state-of-the-art methods demonstrates the superior performance of the proposed models in road object semantic segmentation, road marking extraction and classification, and road boundary completion tasks