Computer Vision and Deep Learning based road monitoring towards a Connected, Cooperative and Automated Mobility

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dc.contributor.authorIparraguirre, Olatz
dc.date.accessioned2024-01-10T15:45:01Z
dc.date.available2024-01-10T15:45:01Z
dc.date.issued2022-11-11
dc.description.abstractThe future of mobility will be connected, cooperative and autonomous. All vehicles on the road will be connected to each other as well as to 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 automationen_US
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3543930
dc.language.isoenen_US
dc.subjectCCAMen_US
dc.subjectComputer visionen_US
dc.subjectRoad monitoringen_US
dc.subjectfuture mobilityen_US
dc.titleComputer Vision and Deep Learning based road monitoring towards a Connected, Cooperative and Automated Mobilityen_US
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