NAITEST

Artículos científicos

• UAVradio: Radio link path loss estimation for UAVs. Aláez Gómez, Daniel; Celaya Echarri, Mikel; Azpilicueta Fernández de las Heras, Leyre; Villadangos Alonso, Jesús. SoftwareX, 25 (2024)
• HIL flight simulator for VTOL-UAV pilot training using X-plane. Aláez Gómez, Daniel, Olaz Moratinos, Xabier, Prieto Míguez, Manuel, Porcellinis Pascau, Pablo de, Villadangos Alonso, Jesús. Information, 2022, 13(12), 1-17 (Q2)
• Digital twin modeling of open category UAV radio communications: A case study. D. Aláez, P. Lopez-Iturri, M. Celaya-Echarri, L. Azpilicueta, F. Falcone, J. Villadangos, J.J. Astrain. Computer Networks (Q1). Volume 242, 2024.
• Regulatory Compliant UAS Navigation Based on a Bitmap Grid Local Path Planning Algorithm. Pablo De Porcellinis Pascau, J. Villadangos, D. Aláez, M. Prieto. 15th annual International Micro Air Vehicle Conference and Competition. 2024, 16-20 Sept. Bristol, United Kingdom
• D. Aláez, M. Prieto, J. Villadangos and J. J. Astrain. On Constructing Efficient UAV Aerodynamic Surrogate Models for Digital Twins. IEEE Transactions on Industrial Informatics, vol. 20, no. 11, pp. 13181-13189, Nov. 2024,

Comunicaciones en congresos

• Integración de IA en la Validación Virtual de ADAS en Entornos Dinámicos. A. Iriondo, O. Del Barrio, J. Sota, N. Garmendia. 25º Congreso Español sobre Sistemas Inteligentes de Transporte (ITS), 2025, 18-20 Feb. Madrid, España.

Problem to solve

Autonomous and connected transport, for both land and air, is one of the biggest technological challenges today. Autonomous vehicles and drones require intelligent systems that can perceive their surroundings, make decisions in real time and behave safely. However, validating and certifying those systems is a complex and costly task that requires controlled, realistic, and repeatable test environments.

The need for methodologies and tools that make it possible to validate the systems efficiently, safely, and in compliance with the regulations in force arises in that context. Furthermore, it is fundamental that the tools be capable of simulating real traffic or flight situations, and that they can detect errors and improve algorithms before they are implemented in real settings.

Project Goal

The NAITEST project intends to be a strategic initiative for developing a test and validation centre for systems associated with autonomous and connected transport. Its primary goal is to design and implement virtual ans physical validation methodologies for systems deployed on vehicles and drones, with a focus on:

• ADAS (advanced driver assistance systems) systems for autonomous vehicles
• DAA (detect and avoid) systems for drones

To achieve those goals developing Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) simulation platforms was proposed, as well as doing field tests that made it possible to validate the behaviour of the algorithms in real conditions.

Results Obtained

The project reached its goals successfully, and created significant advances on several fronts:

• Validation of ADAS systems

An automatic braking algorithm has been developed based on a front camera that can detect dangerous objects and activate the vehicle’s brakes. The tests have shown that the system can identify pedestrians and vehicles and react in an appropriate way.

• Developing DAA systems for drones

Different technologies have been explored for detecting obstacles during flight. On one hand, radar technology gave good results for measuring the distance of the aircraft from the ground. On the other hand, using solutions based on camera vision and neural networks has made it possible to detect obstacles and estimate their GPS position, opening the door to highly complex technological solutions.

• Advanced Simulation Platforms

The SIL and HIL platforms developed have shown to be versatile and effective tools for validating algorithms in controlled environments. Those platforms make it possible to do repeatable, safe and economical tests, and they are applicable to both in-house development and services for third-parties.

• Knowledge Transfer

The project has generated valuable knowledge that is already being using for other technological projects and services. In addition, several scientific articles have been published and the project has been publicised in the media and at conferences.

Impact and Influence

The NAITEST project has significantly contributed to progress in autonomous and connected transport, making it possible to:

• Develop advanced technological capacities for validating intelligent systems
• Generate knowledge that is transferable to companies in the automotive and aeronautics sectors.
• Promote applied research and training high qualified people
• Establish a solid basis for future developments in autonomous vehicles and drones

In addition, the tools and methodologies developed are being provided to companies that incorporate ADAS and DAA systems into land and air vehicles, which strengthens the role of UPNA and NAITEC as leading centres in that field.