Lane Detection based on Multiple Frames Information

Turrado Blanco, Diego. (2021). Lane Detection based on Multiple Frames Information Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial

Ficheros (Some files may be inaccessible until you login with your e-spacio credentials)
Nombre Descripción Tipo MIME Size
Turrado_Blanco_Diego_TFM.pdf Turrado_Blanco_Diego_TFM.pdf application/pdf 9.56MB

Título Lane Detection based on Multiple Frames Information
Autor(es) Turrado Blanco, Diego
Abstract One 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.
Notas adicionales Trabajo de Fin de Máster. Máster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones. UNED
Materia(s) Ingeniería Informática
Palabra clave lane detection
autonomous driving
convolutional neuronal networks
recurrent neuronal networks
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
Director/Tutor Koloda, Ján
Rincón Zamorano, Mariano
Fecha 2021-09-19
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-IAA-Dturrado
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

Versión Tipo de filtro
Contador de citas: Google Scholar Search Google Scholar
Estadísticas de acceso: 198 Visitas, 200 Descargas  -  Estadísticas en detalle
Creado: Fri, 30 Sep 2022, 20:46:04 CET