Browsing by Author "Arizala, Asier"
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Item AUDRIC2: A Modular and Highly Interconnected Automated Driving Framework Focus on Decision Making and Vehicle Control(Institute of Electrical and Electronics Engineers Inc., 2021-09-19) Lattarulo, Ray; Hidalgo, Carlos; Arizala, Asier; Perez, Joshue; CCAMLast years, automated vehicle technologies made considerable progress in academia and the industry. However, there is still a need for more scalable solutions which ensure fast progress between the prototypes and the final deployments. In this sense, this work introduces the updated version of the AUtomated DRIving Core framework (AUDRIC), which aims for robust and reliable vehicle decision-making and control algorithms. The solution is modular and provides MATLAB and ROS interfaces. Also, it has the integration links between the CARLA simulator and Dynacar (in-house simulator). The Autoware. AI framework was integrated to support vehicle perception. This framework targets non-holonomic ground vehicles, such as passenger cars, buses, shuttles, non-holonomic industrial AGV, etc. A simulation around Tecnalia premises was used to verify the provided functionalities (Basque Country, Spain). This framework will continue its development with the support of the European project SHOW. Vehicle cooperation functions are considered as an upcoming feature.Item A Complete Framework for a Behavioral Planner with Automated Vehicles: A Car-Sharing Fleet Relocation Approach(2022-11) Arizala, Asier; Zubizarreta, Asier; Pérez, Joshué; CCAMCurrently, research on automated vehicles is strongly related to technological advances to achieve a safe, more comfortable driving process in different circumstances. The main achievements are focused mainly on highway and interurban scenarios. The urban environment remains a complex scenario due to the number of decisions to be made in a restrictive context. In this context, one of the main challenges is the automation of the relocation process of car-sharing in urban areas, where the management of the platooning and automatic parking and de-parking maneuvers needs a solution from the decision point of view. In this work, a novel behavioral planner framework based on a Finite State Machine (FSM) is proposed for car-sharing applications in urban environments. The approach considers four basic maneuvers: platoon following, parking, de-parking, and platoon joining. In addition, a basic V2V communication protocol is proposed to manage the platoon. Maneuver execution is achieved by implementing both classical (i.e., PID) and Model-based Predictive Control (i.e., MPC) for the longitudinal and lateral control problems. The proposed behavioral planner was implemented in an urban scenario with several vehicles using the Carla Simulator, demonstrating that the proposed planner can be helpful to solve the car-sharing fleet relocation problem in cities.Item A Control Testing Framework for automated driving functionalities using modular architecture with ROS/CARLA environment(Institute of Electrical and Electronics Engineers Inc., 2021) Arizala, Asier; Lattarulo, Ray; Zubizarreta, Asier; Perez, Joshue; Ferariu, Lavinia; Matcovschi, Mihaela-Hanako; Ungureanu, Florina; CCAMInterest in Automated Vehicles (AV) has increased in the last years due to the need of providing more efficient and safe transportation systems. However, the development of AV functionalities is a complex task, as multiple technologies have to be tested and integrated to fulfill the required automation level. Moreover, the number of different scenarios that have to be dealt with Cooperative and Connected Automated Mobility (CCAM) solutions makes traditional track testing a non-optimal approach. Due to this, in recent years interest in the development of simulation-based testing frameworks has arisen, with open-source and commercial solutions trying to fulfill the requirements of AV development. This work introduces an automated vehicle testing framework that combines the widely used open-source simulation environment CARLA with a self-developed modular control framework AUDRIC. The communication between both is made using the ROS environment. The proposed approach provides the advantages of both environments in terms of flexibility and modularity, allowing the development of automated functionalities for the different modules of AV architecture. The validity of the approach is demonstrated by presenting two use cases: a lane following application and an obstacle avoidance scenario.Item Enhancing Motion Prediction by a Cooperative Framework(Institute of Electrical and Electronics Engineers Inc., 2024) Araluce, Javier; Justo, Alberto; Arizala, Asier; González, Leonardo; Díaz, Sergio; CCAMCooperative perception is a technique that enhances the on-board sensing and perception of automated vehicles by fusing data from multiple sources, such as other vehicles, roadside infrastructure, cloud/edge servers, among others. It can improve the performance of automated driving in complex scenarios, like unsignalled roundabouts or intersections where the visibility and awareness of other road users are limited. Motion Prediction (MP) is a key component of cooperative perception, as it enables the estimation and prediction of microscopic traffic states, such as the positions and speeds of all vehicles. It relies on information from other agents and their relationships among them, so the information provided by external sources is valuable because it enhances the understanding of the scene.In this paper, we present improved MP through Vehicle to Vehicle (V2V) communication. We have trained Hierarchical Vector Transformer (HiVT) to be a map-less solution that can be used in road domains. With this model, we have implemented and compared two association methods to evaluate our framework on a real V2V dataset (V2V4Real). Our evaluation concludes that our V2V MP improves performance due to better scene understanding over a single-vehicle MP.Item Fuzzy Logic Based Decision-Making for Urban Platooning on Urban Roundabout Scenarios(Springer Science and Business Media Deutschland GmbH, 2024) Arizala, Asier; Alonso, Gorka; Pérez, Joshué; Zubizarreta, Asier; Marques, Lino; Santos, Cristina; Lima, José Luís; Tardioli, Danilo; Ferre, Manuel; CCAMThis paper proposes a fuzzy-based decision-making framework for urban platooning in roundabout scenarios. By utilizing fuzzy logic to handle uncertainties and imprecise inputs, the framework adapts the behavior of platoon vehicles based on real-time traffic conditions, vehicle dynamics, and safety considerations. In addition, a MPC-based platoon following controller is proposed to execute the actions defined by the decision-making approach. This approach is tested in Carla simulator with successful results, proving the proposal is feasible for platoon handling in urban roundabouts.