Publicación:
Lane Detection based on Multiple Frames Information

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dc.contributor.authorTurrado Blanco, Diego
dc.contributor.directorKoloda, Ján
dc.contributor.directorRincón Zamorano, Mariano
dc.date.accessioned2024-05-20T12:23:19Z
dc.date.available2024-05-20T12:23:19Z
dc.date.issued2021-09-19
dc.description.abstractOne of the fundamental challenges in the field of autonomous driving is the ability to detect dynamic objects, such as vehicles or pedestrians, and statics ones, such as lanes, in the surroundings of the vehicle. The accurate perception of the environment is crucial for a safe decision making and motion planning. In recent years, advanced driver-assistance systems (ADAS) are getting more important, incorporating new features that a few years ago were limited to luxury cars or even not technically possible. In particular, one of this features is the lane keeping assist system which keeps the car centered in the lane. This system is not just a relevant part of actual driving support features, but it is a crucial function of future fully autonomous vehicles (AD). A few years ago, many of these systems relied on traditional computer vision algorithms, based on computational expensive manually calibrated methods. Their lack of robustness under the long tale of driving scenarios makes them not very suitable for scalability. Moreover the limited computational resources of embedded system puts additional requirements on the design of real time capable algorithms. Nowadays, the state-of-the-art object and structure detectors for advanced driverassistance systems are based on machine learning and, in particular, deep learning approaches. However, such approaches still mainly function on single-frame basis and do not exploit the (high) temporal correlation of the signals representing the perceived environment. Single-frame detection networks might work well under circumstances where the lanes are perfectly visible, but show a lack of performance under certain situations, like occlusions, shadows, rain, snow, lane degradation, etc. To address the aforementioned problem, this thesis introduces temporal information for lane binary segmentation, applying convolutional long short-term memory (ConvLSTM) and convolutional neural networks (CNN) to improve substantially the performance of single-frame architecture under challenging and adverse situations.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14107
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.degreeMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordslane detection
dc.subject.keywordsautonomous driving
dc.subject.keywordsconvolutional neuronal networks
dc.subject.keywordsrecurrent neuronal networks
dc.titleLane Detection based on Multiple Frames Informationes
dc.typetesis de maestríaes
dc.typemaster thesisen
dspace.entity.typePublication
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