At CT, we maintain a strong commitment to technological innovation, process optimization, and the development of solutions that drive more sustainable, autonomous, and efficient mobility. This vision translates into a series of research lines focused on the civil domain, which not only address current challenges in cities and infrastructure but also anticipate future urban and industrial scenarios.

CT develops innovative projects that combine emerging technologies to improve urban mobility. Through the use of digital twins, artificial intelligence, vehicle-to-everything (V2X) communication, and extended reality, solutions are created to optimize traffic, enhance safety, and promote efficient and sustainable mobile platforms and autonomous vehicles.

Traffic Simulation and Advanced Planning for Mobile Platforms
One of the cornerstones of this work is traffic simulation for both manned and unmanned vehicles, such as aerial drones, ground rovers, or marine vessels (including surface and underwater). Digital tools allow precise mission and route planning, identification of specific operational needs, and risk assessment before deploying any platform.

This predictive capability is especially valuable in complex environments or those with space and time constraints, laying the groundwork for safer and more realistic planning. Simulation goes beyond individual vehicles and extends to scenarios where multiple mobile systems interact, which is critical for logistics operations or emergency management — think swarms of robots.

Digital Twins and Urban Traffic Management
Building on this digital foundation, urban traffic models are created as digital twins of road infrastructure and the vehicles that make up today’s urban landscape. These models integrate digital maps, real-time data from sensors and onboard units, and management algorithms to simulate, analyze, and optimize traffic flows.

With this technology, it is possible to anticipate traffic jams, analyze how city changes impact circulation, and design faster, obstacle-free routes. Using historical data and AI algorithms, we can also predict the evolution of future urban traffic.

Data Interoperability and Integrated Virtual Environments
To achieve an accurate and dynamic representation of the urban environment, CT has developed an integration interface that connects real-time data from sensors, infrastructure, and vehicles. This interface feeds traffic models with up-to-date and precise information, facilitating scenario evaluation and informed decision-making.

Additionally, a route planner is incorporated, supported by these digital twins, which allows simulation of specific journeys under various conditions. This tool is useful both for improving urban mobility and for training operators and validating technologies before real-world deployment.

Virtual and Augmented Reality
Research also covers the use of virtual, augmented, and mixed reality, primarily applied to industrial and training environments. The ability to recreate real scenarios in immersive settings allows personnel to be trained safely, faster, more efficiently, and more realistically, reducing risks associated with traditional training methods.

V2X Communication and Decentralized Intelligence
As urban and industrial environments evolve, communication between vehicles, infrastructure, and mobile platforms becomes essential. CT develops V2X (Vehicle-to-Everything) systems that enable vehicles to share information in real time with each other as well as with traffic lights, sensors, drones, and other elements.

This connectivity goes beyond simple data transmission; it combines with artificial intelligence to allow mobile systems to make autonomous decisions, anticipate critical situations, and operate without constant ground control. This opens the door to truly intelligent mobility, where vehicles cooperate to optimize routes, avoid collisions, and adapt to constantly changing environments.

All of this is underpinned by a cybersecurity layer capable of encrypting messages homomorphically, without the need to decrypt them during transmission or reception.

Predictive Maintenance and AI-Driven Asset Management
Another key research line focuses on using artificial intelligence for predictive and prescriptive maintenance of vehicles and machinery. Continuous monitoring through digital twins allows anticipation of failures, prediction of maintenance needs, and optimization of the lifespan of critical components such as batteries, engines, transmissions, brakes, tires, sensors, or electronic units.

AI not only detects problems but also prioritizes and prescribes actions, helping make more efficient decisions and reducing costs.

Natural Language Processing and Advanced Automation
In parallel, CT explores the application of natural language models (LLMs) to enhance interaction with technological systems. These tools allow automation of tasks such as report writing, content generation, translation, or virtual assistance, facilitating work with large volumes of technical information.

Cybersecurity in Connected Environments
Finally, the development of these technologies is accompanied by rigorous attention to cybersecurity, especially in environments where V2X communications are critical. CT works with partners specializing in quantum and post-quantum encryption systems, ensuring data protection is ready for future technological challenges. Ensuring the integrity and confidentiality of communications is essential for smart cities to operate safely and reliably, particularly when autonomous or sensitive systems are involved.

At CT, we continue to work with a clear vision: providing concrete solutions to today’s challenges, while keeping our eyes on a future where technology enables mobility that is more practical, safe, and sustainable.