In-car advisory system for lane-changing in a connected vehicle environment

This thesis investigates the potential of in-car advisory systems to suggest location and timing where and when lane-changes should be executed, by evaluating traffic flow conditions with data that is available using vehicle-to-vehicle communication. After investigating existing literature regarding car-following and lane-changing models, as well as driving support assistance systems and vehicle communication applications and practice, a new lane-changing model is introduced, with the objective to serve as a basis for the development of the in-car advisory system. In particular, the model accounts for information about position and speed of vehicles that are downstream from the considered vehicle current position, namely, out of the sight of a driver. Based on the proposed model, a decision system to deliver lane-changing advices to the driver is implemented, with the goal of avoiding or reducing traffic congestion. A set of simulations using the microscopic traffic simulator AIMSUN are performed to test the effectiveness of the proposed system

Pavement crack detection and clustering via region-growing algorithm from 3D MLS point clouds

Road condition monitoring plays a critical role in transportation infrastructure maintenance and traffic safety assurance. This research introduces a methodology to detect cracks on pavement point clouds acquired with Mobile Laser Scanning systems, which offer more versatility and comprehensive information about the road environment than other specific surveying systems (i.e., profilometers, 3D cameras). The methodology comprises the following steps: (1) Road segmentation; (2) the detection of candidate crack points in individual scanning lines of the point cloud, based on point elevation; (3) crack point clustering via a region-growing algorithm; and (4) crack geometrical attributes extraction. Both the profile evaluation and the region-growing clustering algorithms have been developed from scratch to detect cracks directly from 3D point clouds instead of using raster data or Geo-Referenced Feature images, offering a quick and effective pre-rating tool for pavement condition assessment. Crack detection is validated with data from damaged roads in Portugal.Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. FJC2018-035550-

Santiago urban dataset SUD: Combination of Handheld and Mobile Laser Scanning point clouds

Santiago Urban Dataset SUD is a real dataset that combines Mobile Laser Scanning (MLS) and Handheld Mobile Laser Scanning (HMLS) point clouds. The data is composed by 2 km of streets, sited in Santiago de Compostela (Spain). Point clouds undergo a manual labelling process supported by both heuristic and Deep Learning methods, resulting in the classification of eight specific classes: road, sidewalk, curb, buildings, vehicles, vegetation, poles, and others. Three PointNet++ models were trained; the first one using MLS point clouds, the second one with HMLS point clouds and the third one with both H&MLS point clouds. In order to ascertain the quality and efficacy of each Deep Learning model, various metrics were employed, including confusion matrices, precision, recall, F1-score, and IoU. The results are consistent with other state-of-the-art works and indicate that SUD is valid for comparing point cloud semantic segmentation works. Furthermore, the survey's extensive coverage and the limited occlusions indicate the potential utility of SUD in urban mobility research.Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Universidade de Vigo/CISU

Explotación de tecnoloxías de mapeado móbil para a inspección automatizada de pavimentos de estradas

The present thesis is focused on the development of methodologies for the exploitation of remote monitoring systems with the aim of obtaining information on land transport infrastructures and their environment, focusing on the segmentation of road assets and pavement analysis for the detection of pathologies. MLS (Mobile Laser Scanner) systems, capable of generating point clouds representing the geometry of the infrastructure, will be mainly used for data collection. The information obtained with other monitoring means, such as photographic images, will be used to increase the knowledge about the scenario to be analyzed. Algorithms in Matlab and Python will be developed as part of heuristic methodologies for the segmentation of point clouds and the detection of pathologies in the road pavement, mainly cracks. The use of Machine Learning tools for these tasks will also be explored. The results achieved will be compared with those obtained using traditional inspection techniques, with the intention of assessing the improvements in working times and safety of the work thanks to the automation of tasks.La presente tesis está enfocada en el desarrollo de metodologías para la explotación de sistemas de mapeado móvil con el objetivo de obtener información de las infraestructuras de transporte terrestres y su entorno, centrándose en la segmentación de activos de las carreteras y el análisis del pavimento para la detección de patologías. Para la recogida de datos se emplearán principalmente sistemas MLS (Mobile Laser Scanner), capaces de generar nubes de puntos que representen la geometría de la infraestructura. La información obtenida con otros medios de monitoreo, como las imágenes fotográficas, se emplearán para incrementar el conocimiento sobre el escenario a analizar. Se desarrollarán algoritmos en Matlab y Python como parte de metodologías heurísticas para la segmentación de las nubes de puntos y la detección de patologías en el pavimento de las carreteras, principalmente grietas. También se explorará el uso de herramientas de aprendizaje automatizado (Machine Learning) para estas tareas. Los resultados alcanzados serán comparados con los obtenidos mediante técnicas de inspección tradicionales, con la intención de valorar las mejoras en los tiempos de trabajo y la seguridad de los mismos gracias a la automatización de tareas.A presente tese está enfocada no desenvolvemento de metodoloxías para a explotación de sistemas de mapeado móbil co obxectivo de obter información das infraestruturas de transporte terrestres e os seus arredores, centrándose na segmentación de activos das estradas e o análise do pavimento para a detección de patoloxías. Para a recollida de datos empregaranse principalmente sistemas MLS (Mobile Laser Scanner), capaces de xerar nubes de puntos que representen a xeometría da infraestrutura. A información obtida con outros medios de monitoreo, como as imaxes fotográficas, empregarase para incrementar o coñecemento sobre o escenario a analizar. Desenvolveranse algoritmos en Matlab e Python como parte de metodoloxías heurísticas para a segmentación das nubes de puntos e detección de patoloxías no pavimento das estradas, principalmente gretas. Tamén se explorará o uso de ferramentas de aprendizaxe automatizado (Machine Learning) para estas tarefas. Os resultados acadados serán comparados cos obtidos mediante técnicas de inspección tradicionais, coa intención de valorar as melloras nos tempos de traballo e a seguridade dos mesmos grazas á automatización de tarefas

Detection of direct sun glare on drivers from point clouds

Sunlight conditions can reduce drivers’ visibility, which is a safety concern on road networks. This research introduces a method to study sun glare incidence in road environments. Sun glare areas during daylight hours are automatically detected from mobile laser scanning (MLS) and aerial laser scanning (ALS) point clouds. The method comprises the following steps. First, the Sun’s position (solar altitude and azimuth) referring to a location is calculated. Second, the incidence of sun glare with the user’s angle of vision is analyzed based on human vision. Third, sun ray intersections with near obstacles (vegetation, building, etc.) are calculated utilizing MLS point clouds. Finally, intersections with distant obstacles (mountains) are calculated utilizing ALS point clouds. MLS and ALS data are processed in order to combine both data types, remove outliers, and optimize computational time for intersection searches (point density reduction and Delaunay triangulation). The method was tested on two real case studies, covering roads with different bearings, slopes, and surroundings. The combination of MLS and ALS data, together with the solar geometry, identify areas of risk for the visibility of drivers. Consequently, the proposed method can be utilized to reduce sun glare, implementing warnings in navigation systems.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158- C4-2-

Review of laser scanning technologies and their applications for road and railway infrastructure monitoring

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.Spanish Ministry of Science, Innovation and Universities | Ref. RTI2018-095893-B-C2