Blogs

Building the future of Mobility with Digital Twin Technology

Admin — Tue, 01 Jul 2025 14:54:45 +0000

Building the future of Mobility with Digital Twin Technology Admin Tue, 07/01/2025 - 17:54

The Digital Twin concept, initially introduced in the 1960s has experienced rapid uptake at the beginning of the twenty-first century. Over the last two decades,s is has evolved from a simple virtual replica mostly used in manufacturing to complex, real-time, AI-driven systems able to model cities, critical infrastructures, buildings, and traffic.

Digital Twin and CCAM

In the context of Cooperative, Connected, and Automated Mobility (CCAM), implementing Digital Twin technology is crucial in the domain roadmap. This technology has the potential to improve safety, urban mobility management, and stakeholder collaboration. By simulating various scenarios, a Digital Twin can predict potential hazards and evaluate the benefits of traffic management strategies. Simultaneously, the Digital Twin enables real-time monitoring and analysis of infrastructure and situational data, generating intelligent outcomes that can be shared among multiple stakeholders.

The FRODDO Digital Twin

In FRODDO, the concept of Digital Twin technology is tailored to the needs of Connected, Cooperative, and Automated Mobility. Within the project, the DT is much more than a static digital replica but a dynamic, federated, and AI-assisted platform that integrates real-time data, predictive analytics, and simulation capabilities to support the safe and efficient operation of automated vehicles across diverse environments. This framework has four major objectives:

Aggregate diverse data from vehicles, infrastructure, and environmental sources.
Offer a real-time, multidimensional view of the transport ecosystem.
Simulate, predict, and optimize traffic management strategies by using AI.
Support decision-making through interactive dashboards and AI-based systems.

Built on the modular Crimson Digital Twin platform, the FRODDO Digital Twin framework is designed to be scalable and interoperable, adapting to the specific needs of various pilot projects. This ensures effective integration with CCAM services and other FRODDO modules, whether for road networks or indoor environments, offline simulations, or real-time monitoring.

What’s next?

The FRODDO Digital Twin platform will evolve throughout the project with the clear objective to fulfil the needs of the four project pilots foreseen. More specifically the platform will enable the visualisation of results in a 3D digital twin environment of each pilot

Pilot 1 |LS|Ljubljana, Slovenia|RS| - Trust and comfort levels about Level > 3 of driving automation on various road network segment enabling quick recognition of passenger sentiment.
Pilot 2 |LS|Athens, Greece|RS| – Global and per road segment evaluation of traffic management strategies such as dynamic lane reversal and platooning in various scenarios (typical weekday peaks, planned/unplanned disruptions, special events).
Pilot 3 |LS|Modena, Italy|RS|: Traffic forecasts and real-time infrastructure monitoring with early warnings (environmental, operational efficiency and safety) in an urban environment.
Pilot 4 |LS|Bursa, Turkey|RS|: Autonomous tow truck fleet monitoring and management.

More information about the Crimson Digital Twin in the project here.

Blogs

FRODDO Project Presented at the Artificial Intelligence for Road Safety and Mobility Workshop in Athens

Admin — Tue, 24 Jun 2025 08:52:55 +0000

FRODDO Project Presented at the Artificial Intelligence for Road Safety and Mobility Workshop in Athens Admin Tue, 06/24/2025 - 11:52

On May 15th, 2025, the FRODDO project was prominently featured at the Artificial Intelligence for Road Safety and Mobility Scientific Workshop, held in Athens and online. Dr. Panagiotis Fafoutellis from the National Technical University of Athens (NTUA) delivered a comprehensive presentation of FRODDO, showcasing the project’s innovative work in AI-assisted traffic management and smart mobility.

The workshop, organized by the NTUA Department of Transportation Planning and Engineering, brought together experts, researchers, and stakeholders from across Europe, with more than 20 research projects presented throughout the day. The event provided an excellent platform for sharing cutting-edge research and exchanging knowledge on how artificial intelligence is shaping the future of road safety and mobility.

During his presentation, Dr. Fafoutellis introduced FRODDO’s core objectives and its pioneering approach to managing mixed traffic conditions where autonomous vehicles (AVs) and human-driven vehicles coexist. In particular, he highlighted how FRODDO is developing AI-powered traffic management strategies, such as vehicle platooning and dynamic lane reversals, which are being tested in simulated environments to assess their large-scale impact on traffic flow, emissions, and overall network efficiency.

The Athens pilot, using NTUA’s advanced microscopic simulation model of the city center, was presented as a key component of FRODDO’s research activities. Through detailed scenario-based analysis, the project aims to evaluate how adaptive traffic management can ensure safety, efficiency, and sustainability during the complex transition period towards full vehicle autonomy.

FRODDO’s participation in the workshop not only showcased the project’s ongoing progress but also strengthened its connections within the broader scientific community, fostering opportunities for future collaborations and knowledge exchange.

Blogs

Navigating the Transition: AI-Driven Traffic Management for Mixed AV and Human-Driven Vehicle Ecosystems

Admin — Thu, 05 Dec 2024 15:43:55 +0000

Navigating the Transition: AI-Driven Traffic Management for Mixed AV and Human-Driven Vehicle Ecosystems Admin Thu, 12/05/2024 - 17:43

In the transport system of the future, and before the full integration of autonomous vehicles (AVs), mixed traffic conditions involving both AVs and human-driven vehicles will coexist. In these transitional phases, traffic management strategies should be able to adapt to all users’ requirements and their effectiveness will be essential to maintain traffic flow and safety. Such TMS may refer to vehicle platooning—where AVs and human-driven vehicles travel in close, coordinated formations— or dynamic lane reversal strategies – where a specific part of the road changes its use based on the dynamic demand. This need is particularly crucial during non-recurrent events on the network, such as roadworks, accidents, and temporary lane closures.

In such scenarios, adaptive traffic control mechanisms will help manage diverse vehicle behaviors, ensuring that both AVs and human-driven vehicles can navigate safely and seamlessly through unpredictable road conditions. In FRODDO AI-assisted traffic management strategies will be developed and tested in simulation environments to estimate large-scale impacts on the network.

More specifically, in the Athens pilot several scenarios will be tested using the up to date microscopic, simulated network of the Athens’ city center owned and maintained by the NTUA Laboratory of Traffic Engineering. Within the simulation traffic management strategies will be evaluated under various mixed traffic compositions, enabling the estimation of large-scale impacts on traffic, emissions and network efficiency. By modeling scenarios with diverse mixes of autonomous vehicles and traditional vehicles, simulation allows for safe, cost-effective experimentation across a wide range of traffic densities and behavioral patterns.

Blogs

FRODDO will develop new approaches improving security V2V communications

Admin — Wed, 09 Oct 2024 09:00:00 +0000

FRODDO will develop new approaches improving security V2V communications Admin Wed, 10/09/2024 - 12:00

Vehicle-to-vehicle (V2V) communications can be extremely useful in improving the safety of all road users. Connected, Cooperative and Automated Mobility approaches, such as those promoted by the CCAM consortium, allow the data received from on-board vehicle sensors to be complemented with information from vehicles outside the range of the sensors, thus improving the perception of ADAS systems and their ability to react promptly to dangerous conditions and prevent damage.

All main standards for V2V communications (WAVE/DSRC, ETSI-ITS, C-V2X) require vehicles to transmit the current position, speed, acceleration, and the possible occurrence of emergency maneuvers (such as sudden braking or loss of grip). This information can be actively used by ADAS systems to slow down when a vehicle in front of us initiates emergency braking, even if this vehicle is out of range of our sensors or masked by other vehicles or obstacles that prevent direct perception.

This sounds great, but it also exposes ADAS systems to possible cyberattacks where malicious actors send false messages. What would happen if an attacker on the side of a highway used Software Defined Radio to send fake messages about non-existent vehicles pretending to be in front of us and initiating emergency braking? Would it be enough to block traffic on a major road artery? Or, could an attacker convince vehicles around him to “lead the way” simply by sending modified messages claiming to be an emergency vehicle proceeding with sirens blaring?

Some of these attack scenarios are mitigated by security countermeasures already included in the standards, which include the possibility of digitally signing messages, certifying their authenticity. However, an attacker could abuse a TCU (telecommunication unit) equipped with valid digital certificates to send V2V messages with incorrect information to nearby vehicles. For this reason it is important to envisage the use of systems for the analysis of V2V messages capable of identifying anomalies and potentially incorrect behavior.

Traditional detection approaches, such as the one proposed by the Framework For Misbehavior Detection (F2MD)1, rely on the analysis of received V2V messages. However, these systems can be deceived by sophisticated attackers, who create "plausible" message sequences, explicitly designed not to activate the mechanisms for detecting anomalies and impossible situations2. Such attacks can only be accurately detected using innovative approaches, which fuse and compare the data received from V2V communications, the data collected by on-vehicle sensors, and the data collected by sensors at the infrastructure level. The FRODDO project will take all these aspects into account, resulting in the design of safer and more secure V2V communications.

Blogs

Blogs

Building the future of Mobility with Digital Twin Technology

Leave a Reply

FRODDO Project Presented at the Artificial Intelligence for Road Safety and Mobility Workshop in Athens

Leave a Reply

Navigating the Transition: AI-Driven Traffic Management for Mixed AV and Human-Driven Vehicle Ecosystems

Leave a Reply

FRODDO will develop new approaches improving security V2V communications

Leave a Reply