Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments
Ilustraciones, fotografías
- Autores:
-
Arias Correa, Alberto Mauricio
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2024
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86436
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
000 - Ciencias de la computación, información y obras generales::003 - Sistemas
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados
Seguridad vial
Ciclistas - Medidas de seguridad
Accidentes de tránsito - Medidas de seguridad
Redes neurales (Computadores)
Tráfico urbano - Medidas de seguridad
Flujo de tráfico
Transporte - Planificación - Procesamiento de datos
Vulnerable Road User
Intention prediction
Autonomous Driving Environments
Convolutional Neural Networks
Inertial Measurement Unit
LSTM networks
Orientation estimation
Usuarios vulnerables de la vía
Predicción de intención
Redes neuronales convolucionales
Redes LSTM
Estimación de orientación
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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UNACIONAL2 |
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Universidad Nacional de Colombia |
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|
| dc.title.eng.fl_str_mv |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| dc.title.translated.spa.fl_str_mv |
Predicción de intención de ciclista basada en sus ángulos de orientación como usuario vulnerable de la vía en entornos de conducción autónoma |
| title |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| spellingShingle |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados Seguridad vial Ciclistas - Medidas de seguridad Accidentes de tránsito - Medidas de seguridad Redes neurales (Computadores) Tráfico urbano - Medidas de seguridad Flujo de tráfico Transporte - Planificación - Procesamiento de datos Vulnerable Road User Intention prediction Autonomous Driving Environments Convolutional Neural Networks Inertial Measurement Unit LSTM networks Orientation estimation Usuarios vulnerables de la vía Predicción de intención Redes neuronales convolucionales Redes LSTM Estimación de orientación |
| title_short |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| title_full |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| title_fullStr |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| title_full_unstemmed |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| title_sort |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| dc.creator.fl_str_mv |
Arias Correa, Alberto Mauricio |
| dc.contributor.advisor.none.fl_str_mv |
Ballesteros Parra, John Robert Branch Bedoya, John William Madrigal González, Carlos Andrés |
| dc.contributor.author.none.fl_str_mv |
Arias Correa, Alberto Mauricio |
| dc.contributor.researchgroup.spa.fl_str_mv |
Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial |
| dc.contributor.orcid.spa.fl_str_mv |
Arias Correa, Alberto Mauricio [0000-0003-0619-235X] |
| dc.contributor.cvlac.spa.fl_str_mv |
https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000268046 |
| dc.contributor.researchgate.spa.fl_str_mv |
https://www.researchgate.net/profile/Mauricio-Correa-8 |
| dc.contributor.googlescholar.spa.fl_str_mv |
https://scholar.google.com/citations?user=0XMAvosAAAAJ&hl=es&oi=ao |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados |
| topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados Seguridad vial Ciclistas - Medidas de seguridad Accidentes de tránsito - Medidas de seguridad Redes neurales (Computadores) Tráfico urbano - Medidas de seguridad Flujo de tráfico Transporte - Planificación - Procesamiento de datos Vulnerable Road User Intention prediction Autonomous Driving Environments Convolutional Neural Networks Inertial Measurement Unit LSTM networks Orientation estimation Usuarios vulnerables de la vía Predicción de intención Redes neuronales convolucionales Redes LSTM Estimación de orientación |
| dc.subject.lemb.none.fl_str_mv |
Seguridad vial Ciclistas - Medidas de seguridad Accidentes de tránsito - Medidas de seguridad Redes neurales (Computadores) Tráfico urbano - Medidas de seguridad Flujo de tráfico Transporte - Planificación - Procesamiento de datos |
| dc.subject.proposal.eng.fl_str_mv |
Vulnerable Road User Intention prediction Autonomous Driving Environments Convolutional Neural Networks Inertial Measurement Unit LSTM networks Orientation estimation |
| dc.subject.proposal.spa.fl_str_mv |
Usuarios vulnerables de la vía Predicción de intención Redes neuronales convolucionales Redes LSTM Estimación de orientación |
| description |
Ilustraciones, fotografías |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-07-12T13:31:19Z |
| dc.date.available.none.fl_str_mv |
2024-07-12T13:31:19Z |
| dc.date.issued.none.fl_str_mv |
2024-05-16 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TD |
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http://purl.org/coar/resource_type/c_db06 |
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acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/86436 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/86436 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.indexed.spa.fl_str_mv |
LaReferencia |
| dc.relation.references.spa.fl_str_mv |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ballesteros Parra, John Robert8b10cddb5010474c877da0b0b9ef76faBranch Bedoya, John Williamf42af09c4155d70bd93c48fba945c3b0Madrigal González, Carlos Andrés1e7e58a58f46fa6d897830622b797e8fArias Correa, Alberto Mauricioae316c2dbbd6cac0d8fb533f6b50768eGidia: Grupo de Investigación YyDesarrollo en Inteligencia ArtificialArias Correa, Alberto Mauricio [0000-0003-0619-235X]https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000268046https://www.researchgate.net/profile/Mauricio-Correa-8https://scholar.google.com/citations?user=0XMAvosAAAAJ&hl=es&oi=ao2024-07-12T13:31:19Z2024-07-12T13:31:19Z2024-05-16https://repositorio.unal.edu.co/handle/unal/86436Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones, fotografíasTraffic accidents are currently the eighth leading cause of death, according to the World Health Organization (WHO). Of these deaths, 71% are vulnerable road users (VRUs), with cyclists accounting for 3%. In an environment where autonomous vehicles (AVs) are the most prominent non-vulnerable road actors, VRUS must be effectively and swiftly detected by these AVs. This task remains an open challenge, as cyclists exhibit highly complex movement patterns, and occlusions and lighting issues in urban roads hinder their detection. In this doctoral thesis, we propose predicting the intentions of cyclists on urban roads by estimating both their orientation and inclination during movement near AVs. Due to the lack of hardware and software for the data acquisition systems, containing images associated with cyclists' orientation angles, a system was designed, and a dataset called Cyclops was compiled. This dataset was then used to train an effective cyclist detector using the YOLOv8 architecture. A refined subset of the dataset enabled the training of a model based on modified VGG16 for angular regression and another with similar features based on EfficientNetV2-s. Both models showed better cyclist orientation estimation results than those currently found in the state-of-the-art. Finally, we trained an LSTM network to predict two subsequent periods of angular change (for orientation and inclination) from six previous states, maintaining a prediction sequence and achieving the proposed objective. (Tomado de la fuente)Las muertes por accidentes de tránsito son actualmente la octava causa de muerte según la Organización Mundial de la Salud (OMS). El 71% de esas muertes corresponde a usuarios vulnerables de la vía (VRU), en particular el 3% son ciclistas. En un entorno en el cual los vehículos autónomos (AV) son los actores viales no vulnerables de mayor presencia, será de gran importancia que los VRU sean detectados por dichos AV de forma efectiva y en el menor tiempo posible. Esta tarea aún es un desafío abierto, debido a que los ciclistas tienen patrones de movimiento altamente complejos y su detección se ve afectada por oclusiones y problemas asociados a la iluminación cuando se desplazan sobre vías urbana. En esta tesis doctoral, se propone predecir la intención de los ciclistas en vías urbanas a partir de la estimación tanto de su orientación como de su inclinación durante el movimiento en cercanías de AVs. Debido a la falta de datasets que contengan imágenes asociadas a ángulos de orientación de ciclistas, se diseñó un sistema y se construyó un dataset denominado Cyclops. Posteriormente el dataset fue utilizado para entrenar un detector de ciclistas efectivo utilizando la arquitectura YOLOv8. Un subconjunto depurado del dataset permitió entrenar un modelo basado en VGG16 modificado para regresión angular y otro con las mismas características, pero basado en EfficientNetV2-s. Ambos modelos presentaron resultados de estimación de orientación de ciclistas mejores a los actualmente encontrados en el estado del arte. Finalmente se entrenó una red LSTM que permitía para predecir dos periodos posteriores de cambio angular (para orientación e inclinación) a partir de seis estados anteriores y mantener una secuencia de predicción, logrando así el objetivo propuesto.DoctoradoDoctor en IngenieríaIngeniería De Sistemas E Informática.Sede Medellín83 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - SistemasFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería000 - Ciencias de la computación, información y obras generales::003 - Sistemas000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionadosSeguridad vialCiclistas - Medidas de seguridadAccidentes de tránsito - Medidas de seguridadRedes neurales (Computadores)Tráfico urbano - Medidas de seguridadFlujo de tráficoTransporte - Planificación - Procesamiento de datosVulnerable Road UserIntention predictionAutonomous Driving EnvironmentsConvolutional Neural NetworksInertial Measurement UnitLSTM networksOrientation estimationUsuarios vulnerables de la víaPredicción de intenciónRedes neuronales convolucionalesRedes LSTMEstimación de orientaciónIntention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environmentsPredicción de intención de ciclista basada en sus ángulos de orientación como usuario vulnerable de la vía en entornos de conducción autónomaTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDLaReferenciaAbadi, A. 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IEEE.EstudiantesInvestigadoresMaestrosPúblico generalResponsables políticosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86436/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL71726659.2024.pdf71726659.2024.pdfTesis de Doctorado en Ingeniería - Sistemasapplication/pdf5436770https://repositorio.unal.edu.co/bitstream/unal/86436/2/71726659.2024.pdfbbe0815df79097bd56e55827b4d611e0MD52THUMBNAIL71726659.2024.pdf.jpg71726659.2024.pdf.jpgGenerated Thumbnailimage/jpeg4392https://repositorio.unal.edu.co/bitstream/unal/86436/3/71726659.2024.pdf.jpgeb8a7ccf20a7e17e47b04152392b7d81MD53unal/86436oai:repositorio.unal.edu.co:unal/864362024-08-26 23:10:54.843Repositorio Institucional Universidad Nacional de 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