Last modified on February 12, 2024

Innovation tools towards a safer and more effective society

4 January 2024

The constant increase in urban mobility over the past decades has affected the overall quality of life around the world. About 70 % of Europe’s population lives in urban areas, and this figure will increase to around 80 % by 2050. Traffic congestion, collisions, and pollution are therefore growing daily problems for many people in the EU and the world, and efficient mobility management is a key issue for urban areas. Consequently, continuous efforts are required to optimise the traffic flow, reduce travel time, decrease fuel consumption, and prevent collisions.

A challenge to contribute to traffic flow optimisation in critical areas, such as intersections, is being recently given by the advent of Connected, Cooperative and Automated Mobility (CCAM). The capability of Connected, Cooperative and Automated Vehicles (CCAV) to exchange data by a Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) makes it possible to implement different classes of optimisation tasks and therefore plan routes and schedule crossing times based on priorities, thus preventing collisions and ultimately improving traffic streams.

In the near future, however, the reality will be a combination of automated and regular vehicles, therefore mixed traffic scenarios have been recently receiving more attention. The IN2CCAM project, aligning with the vision of the Horizon Europe framework programme for 2021-2027, aims to expedite the deployment of pioneering CCAM technologies and systems for passengers and goods. It intends to develop, implement, and demonstrate innovative services catering to connected and automated vehicles, infrastructures, and users.

The goal is to provide benefits to all citizens by implementing a full integration of CCAM services in the transport system by guaranteeing safety (i.e., decreasing the number of road accidents caused by human error), reducing transport emissions and congestion by smoothening the traffic flow and avoiding unnecessary trips, ensuring inclusive mobility and good access for all. To this aim the IN2CCAM approach is based on the development and implementation of innovative tools for enhanced connectivity and service effectiveness.

On one hand, telecommunication, data services and digital media sectors support in-vehicle connectivity (V2V and V2I) and upcoming 5G networks support the exchange of massive amounts of data generated by a CCAV.

On the other hand, Artificial Intelligence (AI), Digital Twin (DT), Simulation and High-Definition (HD) Maps are the most challenging enablers for IN2CCAM services. Especially, AI approaches such as Machine Learning and Deep Reinforcement Learning are used for designing optimised services based on real-time data and simulation models that will be employed to train the Neural Networks for reinforcement learning. The enhanced simulation models utilise information from CCAVs and are integrated with the overall traffic scale; in addition, the simulation tools can enhance the CCAM by integrating both real CCAVs and simulated CCAV fleets. The DTs are used for reacting to the real-time system, implementing remote monitoring and controlling and assessing the traffic risk. Moreover, HD Maps can cover the whole road and transport infrastructure, systems, and services. An example is the integration of dedicated lanes with their characteristics, priorities at intersections, traffic lights and signs, variable message signs, and physical structures on-above-and-beside the roads accompanied by up-to-date traffic situational awareness.

Professor Maria Pia Fanti

IN2CCAM innovation tools represent a contribution towards a safer and more effective society in which vehicles will not be driven by humans. The consortium is designing such tools that will be implemented next year in the IN2CCAM leaving labs.

Written by Prof. Maria Pia Fanti, Full professor of Control Systems Engineering at the Polytechnic University of Bari