Computer Vision and Deep Learning based road monitoring towards a Connected, Cooperative and Automated Mobility
The future of mobility will be connected, cooperative and autonomous. All vehicles on the road will communicate with each other as well as with the infrastructure. Traffic will be mixed and human-driven vehicles will coexist alongside self-driving vehicles of different levels of automation. This...
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| Format: | info:eu-repo/semantics/doctoralThesis |
| Language: | eng |
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Servicio de Publicaciones. Universidad de Navarra.
2022
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| Online Access: | https://hdl.handle.net/10171/64664 |
| _version_ | 1793400535926177792 |
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| author | Iparraguirre-Gil, O. (Olatz) Borro-Yagüez, D. (Diego) Brazález-Guerra, A. (Alfonso) |
| author_facet | Iparraguirre-Gil, O. (Olatz) Borro-Yagüez, D. (Diego) Brazález-Guerra, A. (Alfonso) |
| author_sort | Iparraguirre-Gil, O. (Olatz) |
| collection | DSpace |
| description | The future of mobility will be connected, cooperative and autonomous. All vehicles on
the road will communicate with each other as well as with the infrastructure. Traffic will
be mixed and human-driven vehicles will coexist alongside self-driving vehicles of
different levels of automation. This mobility model will bring greater safety and
efficiency in driving, as well as more sustainable and inclusive transport.
For this future to be possible, vehicular communications, as well as perception systems,
become indispensable. Perception systems are capable of understanding the
environment and adapting driving behaviour to it (following the trajectory, adjusting
speed, overtaking manoeuvres, lane changes, etc.). However, these autonomous systems
have limitations that make their operation not possible in certain circumstances (low
visibility, dense traffic, poor infrastructure conditions, etc.). This unexpected event
would trigger the system to transfer control to the driver, which could become an
important safety weakness. At this point, communication between different elements of
the road network becomes important since the impact of these unexpected events can be
mitigated or even avoided as long as the vehicle has access to dynamic road information.
This information would make it possible to anticipate the disengagement of the
automated system and to adapt the driving task or prepare the control transfer less
abruptly.
In this thesis, we propose to develop a road monitoring system that, installed in vehicles
travelling on the road network, performs automatic auscultation of the status of the
infrastructure and can detect critical events for driving. In the context of this research
work, the aim is to develop three independent modules: 1) a system for detecting fog
and classifying the degree of visibility; 2) a system for recognising traffic signs; 3) a
system for detecting defects in road lines. This solution will make it possible to generate
cooperative services for the communication of critical road events to other road users. It
will also allow the inventory of assets to facilitate the management of maintenance and
investment tasks for infrastructure managers. In addition, it also opens the way for
autonomous driving by being able to better manage transitions of control in critical
situations and by preparing the infrastructure for the reception of self-driving vehicles
with high levels of automation. |
| format | info:eu-repo/semantics/doctoralThesis |
| id | oai:dadun.unav.edu:10171-64664 |
| institution | Universidad de Navarra |
| language | eng |
| publishDate | 2022 |
| publisher | Servicio de Publicaciones. Universidad de Navarra. |
| record_format | dspace |
| spelling | oai:dadun.unav.edu:10171-646642022-11-22T09:40:10Z Computer Vision and Deep Learning based road monitoring towards a Connected, Cooperative and Automated Mobility Iparraguirre-Gil, O. (Olatz) Borro-Yagüez, D. (Diego) Brazález-Guerra, A. (Alfonso) Inteligencia artificial Road monitoring Deep learning Future mobility CCAM Automated driving The future of mobility will be connected, cooperative and autonomous. All vehicles on the road will communicate with each other as well as with the infrastructure. Traffic will be mixed and human-driven vehicles will coexist alongside self-driving vehicles of different levels of automation. This mobility model will bring greater safety and efficiency in driving, as well as more sustainable and inclusive transport. For this future to be possible, vehicular communications, as well as perception systems, become indispensable. Perception systems are capable of understanding the environment and adapting driving behaviour to it (following the trajectory, adjusting speed, overtaking manoeuvres, lane changes, etc.). However, these autonomous systems have limitations that make their operation not possible in certain circumstances (low visibility, dense traffic, poor infrastructure conditions, etc.). This unexpected event would trigger the system to transfer control to the driver, which could become an important safety weakness. At this point, communication between different elements of the road network becomes important since the impact of these unexpected events can be mitigated or even avoided as long as the vehicle has access to dynamic road information. This information would make it possible to anticipate the disengagement of the automated system and to adapt the driving task or prepare the control transfer less abruptly. In this thesis, we propose to develop a road monitoring system that, installed in vehicles travelling on the road network, performs automatic auscultation of the status of the infrastructure and can detect critical events for driving. In the context of this research work, the aim is to develop three independent modules: 1) a system for detecting fog and classifying the degree of visibility; 2) a system for recognising traffic signs; 3) a system for detecting defects in road lines. This solution will make it possible to generate cooperative services for the communication of critical road events to other road users. It will also allow the inventory of assets to facilitate the management of maintenance and investment tasks for infrastructure managers. In addition, it also opens the way for autonomous driving by being able to better manage transitions of control in critical situations and by preparing the infrastructure for the reception of self-driving vehicles with high levels of automation. El futuro de la movilidad será conectada, cooperativa y autónoma. Todos los vehículos de la carretera estarán conectados entre sí, así como con la infraestructura. El tráfico será mixto y vehículos tripulados por humanos convivirán junto con vehículos de diferentes niveles de automatización. Este modelo de movilidad traerá consigo una mayor seguridad y eficiencia en la conducción, así como un transporte más sostenible e inclusivo. Para que este futuro sea posible, las comunicaciones vehiculares, así como los sistemas de percepción, se vuelven imprescindibles. Los sistemas de percepción son capaces de entender el entorno y adaptar la conducción al mismo (seguir la trayectoria, adecuar la velocidad, maniobras de adelantamiento, cambio de carril etc.). Sin embargo, estos sistemas autónomos tienen limitaciones que hacen que en ciertas circunstancias su funcionamiento no sea posible (baja visibilidad, tráfico denso, infraestructura en malas condiciones etc.). Este imprevisto haría que el sistema transfiera el control al conductor, lo que puede convertirse en un problema de seguridad vial. En este punto, la comunicación entre los distintos elementos de la red de carreteras cobra especial importancia, ya que el impacto de estos imprevistos puede mitigarse o incluso evitarse si el vehículo tiene acceso a información dinámica de la carretera. Esta información permitiría anticipar la desconexión del sistema automatizado y adaptar la tarea de conducción o preparar la transferencia de control de forma menos brusca. En esta tesis, se propone desarrollar un sistema de monitorización de la carretera que, instalado en vehículos que recorran la red viaria, realice una auscultación automática del estado de la infraestructura y pueda detectar a su vez eventos críticos para la conducción. En el contexto de este trabajo de investigación se pretende desarrollar tres módulos independientes: 1) un sistema de detección de niebla y clasificación del grado de visibilidad; 2) un sistema de reconocimiento de señales de tráfico; 3) un sistema de detección de defectos en las líneas de la carretera. Esta solución permitirá generar servicios cooperativos para la comunicación de eventos críticos de la carretera al resto de usuarios. Del mismo modo permitirá realizar el inventariado de activos para facilitar la gestión de tareas de mantenimiento e inversiones a los gestores de la infraestructura. Además, abre camino también a la conducción autónoma pudiendo gestionar mejor las transiciones de control en situaciones críticas y poniendo a punto la infraestructura para la acogida de vehículos con niveles de automatización elevados. Etorkizuneko mugikortasuna konektatua, kooperatiboa eta autonomoa izango da. Errepideko ibilgailu guztiak elkarren artean konektatuta egongo dira, baita azpiegiturarekin ere. Trafikoa mistoa izango da, eta gizakiek gidatutako ibilgailuak hainbat automatizazio-mailatako ibilgailuekin batera biziko dira. Mugikortasun-eredu horrek segurtasun eta eraginkortasun handiagoa ekarriko du gidatzean, bai eta garraio jasangarriagoa eta inklusiboagoa ere. Etorkizun hori posible izan dadin, ibilgailu-komunikazioak eta pertzepzio-sistemak ezinbestekoak dira. Pertzepzio-sistemak gai dira ingurunea ulertzeko eta gidatzeko modua horretara egokitzeko (ibilbideari jarraitzea, abiadura egokitzea, aurreratzeko maniobrak, errei-aldaketa, etab.). Hala ere, sistema autonomo horiek mugak dituzte, eta, horren ondorioz, zenbait egoeratan ezin dute funtzionatu (ikuspen urria, trafiko handia, baldintza txarreko azpiegitura, etab.). Ezusteko horren ondorioz, sistemak kontrola gidariari transferituko lioke, eta hori bide-segurtasuneko arazo bihur daiteke. Puntu horretan, errepide-sareko elementuen arteko komunikazioa bereziki garrantzitsua da, ezusteko horien eragina arindu edo saihestu egin baitaiteke ibilgailuak errepideari buruzko informazio dinamikoa eskura badu. Informazio horri esker, sistema automatizatuaren deskonexioa aurreikusi ahal izango litzateke, eta gidatze-lana egokitu edo kontrol-transferentzia hain zakarra izan gabe prestatu. Tesi honetan, errepidea monitorizatzeko sistema bat garatzea proposatzen da. Sistema horrek, ibilgailuetan instalatuta, bide-sarea zeharkatzen du, azpiegituraren egoeraren auskultazio automatikoa eginez, eta, aldi berean, gidatzeko kritikoak diren gertaerak hautemanez. Ikerketa-lan honen kontextuan, hiru modulu ezberdin garatu nahi dira: 1) lainoa detektatzeko eta ikuspen-maila sailkatzeko sistema bat; 2) trafiko-seinaleak ezagutzeko sistema bat; 3) errepide-lerroetan akatsak detektatzeko sistema bat. Konponbide horri esker, zerbitzu kooperatiboak sortu ahal izango dira errepideko gertaera kritikoak gainerako erabiltzaileei jakinarazteko. Era berean, aktiboen inbentarioa egiteko aukera emango du, azpiegituraren kudeatzaileei mantentze-lanen eta inbertsioen kudeaketa errazteko. Gainera, bide ematen dio gidatze autonomoari, egoera kritikoetan kontrol-trantsizioak hobeto kudea ditzan eta automatizazio-maila altuak dituzten ibilgailuak hartzeko azpiegitura prest jar dezan. 2022-11-16T16:50:21Z 2022-11-16T16:50:21Z 2022-11 2022-11-14 info:eu-repo/semantics/doctoralThesis https://hdl.handle.net/10171/64664 eng info:eu-repo/semantics/embargoedAccess application/pdf Servicio de Publicaciones. Universidad de Navarra. |
| spellingShingle | Inteligencia artificial Road monitoring Deep learning Future mobility CCAM Automated driving Iparraguirre-Gil, O. (Olatz) Borro-Yagüez, D. (Diego) Brazález-Guerra, A. (Alfonso) Computer Vision and Deep Learning based road monitoring towards a Connected, Cooperative and Automated Mobility |
| title | Computer Vision and Deep Learning based road monitoring towards a Connected, Cooperative and Automated Mobility |
| title_full | Computer Vision and Deep Learning based road monitoring towards a Connected, Cooperative and Automated Mobility |
| title_fullStr | Computer Vision and Deep Learning based road monitoring towards a Connected, Cooperative and Automated Mobility |
| title_full_unstemmed | Computer Vision and Deep Learning based road monitoring towards a Connected, Cooperative and Automated Mobility |
| title_short | Computer Vision and Deep Learning based road monitoring towards a Connected, Cooperative and Automated Mobility |
| title_sort | computer vision and deep learning based road monitoring towards a connected, cooperative and automated mobility |
| topic | Inteligencia artificial Road monitoring Deep learning Future mobility CCAM Automated driving |
| url | https://hdl.handle.net/10171/64664 |
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