Currently, the increase of transport demands along with the limited capacity of the road network have increased traffic congestion in urban and highway scenarios. Technologies such as Cooperative Adaptive Cruise Control (CACC) emerge as efficient solutions. However, a higher level of cooperation among multiple vehicle platoons is needed to improve, effectively, the traffic flow. In this paper, a global solution to merge two platoons is presented. This approach combines: (i) a longitudinal controller based on a feed-back/feed-forward architecture focusing on providing CACC capacities and (ii) hybrid trajectory planning to merge platooning on straight paths. Experiments were performed using Tecnalia’s previous basis. These are the AUDRIC modular architecture for automated driving and the highly reliable simulation environment DYNACAR. A simulation test case was conducted using five vehicles, two of them executing the merging and three opening the gap to the upcoming vehicles. The results showed the good performance of both domains, longitudinal and lateral, merging multiple vehicles while ensuring safety and comfort and without propagating speed changes.

Despite the progress made in the Intelligent Transportation Systems (ITS) field, there is still a large amount of scenarios to be solved, such as urban roundabouts. In particular, the entrance must be emphasized due to most of accidents take place in this part. In such way, the article presents a method to solve the roundabout merging considering a nominal trajectory generated through Bézier curves combining with a Model Predictive Control (MPC) to assure safe future states. Tests are made using a real platform to execute the maneuver and a virtual platform to represent the other participant on the scenario. Experimental results demonstrate the good performance of the approach under merging scenarios.

The development of connected and automated vehicles (CAVs) presents a great opportunity to extend the current range of vehicle vision, by gathering information outside of its sensors. Two main sources could be aggregated for this extended perception; vehicles making use of vehicle-to-vehicle communication (V2V), and infrastructure using vehicle-to-infrastructure communication (V2I). In this paper, we focus on the infrastructure side and make the case for low-latency obstacle mapping using V2I communication. A map management framework is proposed, which allows vehicles to broadcast and subscribe to traffic information-related messages using the Message Queuing Telemetry Transport (MQTT) protocol. This framework makes use of our novel candidate/employed map (C/EM) model for the real-time updating of obstacles broadcast by individual vehicles. This solution has been implemented and tested using a scenario that contains real and simulated CAVs tasked with doing lane change and braking maneuvers. As a result, the simulated vehicle can optimize its trajectory planning based on information which could not be observed by its sensor suite but is instead received from the presented map-management module, while remaining capable of performing the maneuvers in an automated manner.

The increase in maritime traffic due to the globalization of trade has led to an exponential growth in logistics operations and port traffic management is becoming increasingly complicated. The need to improve the efficiency, safety, and sustainability of operations is leading to a strong demand for automation in port processes. A significant challenge is the optimization and automation of loading and unloading systems, given their complexity and repetitive nature. Advances in automated systems, through ongoing research and application in various transportation scenarios, are addressing these challenges. However, automating vehicles alone is insufficient; it is also important to have a connected, infrastructure-based collaborative framework to manage the complex logistics of port operations effectively and in a synchronized manner. In this sense, ESTIBA+ 2022, a Spanish-funded project, is addressing this challenge, aiming to develop strategic technologies for Smart-Ports. Its goal is to design and validate a scalable collaborative framework that meets the specifications and functions of port services using Industry 4.0 technologies and advanced wireless communications through Connected Intelligent Transportation Systems (C-ITS). This article presents the proposed architecture and its validation in the comprehensive use case of the project, focusing on communication from the supervision platform to Automated Guided Vehicles (AGVs) to ensure optimal traffic flow and management in ports. Specifically, the scenario involves three different forklifts equipped with an On-Board Unit (OBU) that interact with each other and the infrastructure via Roadside Units (RSU). The outcome of the project shows that the framework can meet the requirements for Smart-Port logistics, showing the feasibility of the implementation with a collaborative maneuver between three forklifts, a supervision station, and connected traffic lights.

En los últimos años los Sistemas Inteligentes de Transporte, ITS (del inglés, Intelligent Transportation System) se han convertido en una realidad dentro de la sociedad, aportando soluciones y beneficios a la conducción. Con el fin de contribuir a su desarrollo, el presente trabajo describe un marco cooperativo híbrido capaz de validar maniobras entre múltiples vehículos (virtuales y reales), con el fin de disminuir los costos, tiempos y riesgos asociados al ajuste de los controladores. Para su validación se presentan 3 casos de estudios. El primero consiste en utilizar dos vehículos virtuales para realizar un Control de Crucero Adaptativo, ACC (del inglés, Adaptive Cruise Control) con seguidor de trayectoria. El segundo, emplea un coche real como seguidor y un coche virtual como líder para la maniobra de Stop & Go. Finalmente, se utilizan dos vehículos reales para el ACC. Los algoritmos de seguimiento empleados para las maniobras cooperativas están basados en controladores de lógica borrosa. Los resultados demuestran la versatilidad del marco propuesto, al poder ejecutar las maniobras correctamente en cada uno de los entornos.