Browsing by Author "Asua, Estibaliz"
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Item Intelligent Longitudinal Merging Maneuver at Roundabouts Based on Hybrid Planning Approach(Springer, 2020) Hidalgo, Carlos; Lattarulo, Ray; Pérez, Joshué; Asua, Estibaliz; Moreno-Díaz, Roberto; Quesada-Arencibia, Alexis; Pichler, Franz; CCAMRoundabout intersections promote a continuous traffic flow, with less congestion and more safety than standard intersections. However, there are several problems related to its entrance. In this way, the article presents a method to solve the roundabout merging combining a nominal trajectory generated with Bézier curves with a Model Predictive Control (MPC) to assure safe maneuvers. Simulation results using Dynacar are shown and the good performance of the approach under merging maneuvers is demonstrated.Item Trajectory Planning of Automated Vehicles Using Real-Time Map Updates(2023) Szántó, Mátyás; Hidalgo, Carlos; González, Leonardo; Rastelli, Joshué Pérez; Asua, Estibaliz; Vajta, László; CCAMThe 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.