Iparraguirre-Gil, O. (Olatz)
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- An adjusted propagation model for ITS-G5 communications for improving the location of RSUs in real V2I deployments(Elsevier, 2024-02) Iparraguirre-Gil, O. (Olatz); Mendizabal-Samper, J. (Jaizki); Iturbe-Olleta, N. (Nagore); Bilbao, J. (Jon); Brazález-Guerra, A. (Alfonso)The future of mobility is cooperative, connected, and autonomous leading to new technological challenges in the development of Cooperative Intelligent Transport Systems (C-ITS). Therefore, Vehicle to Everything (V2X) and, more specifically, Vehicle to Infrastructure (V2I) deployments are key to enabling these features around the highways as well as along the cities. The communication range of the RoadSide Units (RSUs) is one of the most important aspects when implementing Vehicle-to-Infrastructure (V2I) communications as it has a direct impact on efficiency and the economy of the installation. The aim is to maximise the communication range with the minimum number of RSUs and to optimise the deployments, thus having a realistic simulation tool is key. To be realistic, simulations rely on adequate propagation models, which ideally would adapt to the environment without a high computational need. Therefore, an appropriate characterisation of the different V2X environments as well as a simple and versatile propagation model is an important instrument for deciding the location of the RSUs. In this paper, we characterise different environments for ITS-G5 communications and provide an adjusted propagation model with an α parameter that depends on the environment. Thus, eradicating the need to model the environment and the obstacles in it. For that purpose, a methodology for the modeling and characterisation of the ITS-G5 propagation model is proposed,after that the methodology is applied and the results validated. The methodology is presented and the characterisation of the ITS-G5 environments is made. Later, tests were carried out in different environments to measure how the signal power decreases with the distance. After that, the propagation model for ITS-G5 communications, specifically V2I communications, is presented along with the methodology applied to obtain it. Then, an α value is assigned to each environment. Finally, the validation is made by comparing our adjusted propagation model with other propagation models and applying the adjusted propagation model to a new RSU installation.
- Computer Vision and Deep Learning based road monitoring towards a Connected, Cooperative and Automated Mobility(Servicio de Publicaciones. Universidad de Navarra., 2022-11) Iparraguirre-Gil, O. (Olatz); Borro-Yagüez, D. (Diego); Brazález-Guerra, A. (Alfonso)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.
- Sensors on the move: onboard camera-based real-time traffic alerts paving the way for cooperative roads(MDPI, 2021) Iparraguirre-Gil, O. (Olatz); Amundarain-Irizar, A. (Aiert); Brazález-Guerra, A. (Alfonso); Borro-Yagüez, D. (Diego)European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.