Browsing by Keyword "Intelligent Transportation Systems"
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Item Connected and intelligent framework for vehicle automation in Smart-Ports(2024) Vaca-Recalde, Myriam E.; Marcano, Mauricio; Matute, Jose; Hidalgo, Carlos; Martinez-Rodriguez, Belen; Bilbao-Arechabala, Sonia; Jorge-Hernandez, Fernando; Murgoitio, Jesus; Perez, Joshue; Santaella, Javier; Camacho, Andres; CCAM; BIGDATA; SWTThe 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.Item Experimental Validation of a Kinematic Bicycle Model Predictive Control with Lateral Acceleration Consideration(2019) Matute, Jose A.; Marcano, Mauricio; Diaz, Sergio; Perez, Joshue; CCAMNowadays, Automated Driving has a growing interest in the scientific and industrial automotive community. The vehicle motion planning is an essential procedure to obtain safe and comfortable trajectories, adapting the longitudinal speed to the road legal limits and mainly to avoid the excessive lateral accelerations along the journey. Typically, the proper speed of the vehicle is intrinsically related to the curvature of the path, requiring a previous approximation of this parameter in the planning stage. In this work, a novel procedure to follow a route trajectory and speed limits considering the lateral acceleration parameter is presented. A lateral jerk equation was developed and introduced into a kinematic bicycle model predictive control formulation. An adaptive speed weight equation that depends on the lateral acceleration is presented to improve the lateral positioning. A vehicle motion control simulation, developed in Dynacar, is validated with some real tests. The results show the capabilities of the proposed approach. An accurate vehicle motion control considers the lateral acceleration to avoid unfeasibility in optimization problem.Item From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability: A prescription of functional requirements for model actionability(2021-02-05) Laña, Ibai; Sanchez-Medina, Javier J.; Vlahogianni, Eleni I.; Ser, Javier Del; IAAdvances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infras-tructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrin-sic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underly-ing the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.Item Guest Editorial Artificial Intelligence and Deep Learning for Intelligent and Sustainable Traffic and Vehicle Management (VANETs)(2022-10-01) Gupta, Brij B.; Agrawal, Dharma P.; Sajjad, Muhammad; Sheng, Michael; Del Ser, Javier; IAIntelligence and sustainability are two essential drivers for the development of current and future Intelligent Transportation Systems. On one hand, the complexity of vehicular ecosystems and the inherently risk-prone circumstances under which pedestrian and vehicles coexist call for the endowment of intelligent functionalities in almost all systems and processes participating in such ecosystems. On the other hand, risk may be the most important objective to be guaranteed by the provision of intelligence in ITS, but it is not certainly the only one: when safety is assured, sustainability comes into play, seeking to convey intelligence to the distinct parts composing the ITS landscape with efficiency, minimum carbon footprint, wastage of resources or any other factor affected by the technological empowerment itself.Item A Speed Planner Approach Based On Bézier Curves Using Vehicle Dynamic Constrains and Passengers Comfort(IEEE, 2018-05) Lattarulo, Ray; Marti, Enrique; Marcano, Mauricio; Matute, Jose; Perez, Joshue; CCAM; Tecnalia Research & InnovationThis paper presents a speed profile generation approach for longitudinal control of automated vehicles, based on quintic Bézier curves. The described method aims to increase comfort level of passengers based on the ISO2631-1 specification, while taking into account vehicle dynamics and traffic rules to keep high safety levels. The algorithm has been tested in an in-house tool for high accuracy vehicle dynamics simulations, called Dynacar. The considered scenario is a closed circuit inside Tecnalia facilities. The resulting profile has better properties (for example, rate of change) than a raw input based on traffic speed limits. When used as reference for the speed controller, it improves both comfort and safety.