Abstract

The C-IPAC4AA project aims to develop a new Integrated Sensing and Communication (ISAC) paradigm to enhance the cognitive capabilities of autonomous vehicles and connected infrastructure in 6G environments. The objective is to bridge the gap between perception and communication by integrating data acquisition, multi-sensor fusion, learning, decision-making and the secure exchange of information between autonomous agents within a data-driven, Bayesian framework.

The research will address two complementary scenarios. In the first, radio signals will be used as a perceptual source to improve environmental awareness, particularly when conventional sensors prove unreliable. In the second, data from LiDAR and cameras will be combined with GPS and IMU information to dynamically adapt communication parameters and support collective perception and the coordination of manoeuvres.

The proposed architecture will comprise interconnected modules for data acquisition, compact knowledge representation, multimodal perception and real-time execution. The communication link will ensure adaptive and secure transmissions, whilst memory, actuation and online learning components will enable models to be updated, anomalies to be detected and efficient decisions to be made. Particular attention will be paid to multi-agent coordination, knowledge transfer, model robustness, incremental learning and security.

The expected results include generative models for perception, secure communication protocols, anomaly detection mechanisms and methods for collaborative decision-making. The solutions will be evaluated from the definition of requirements through to experimental performance verification. By integrating bio-inspired learning and advanced communication technologies, C-IPAC4AA will contribute to the development of intelligent, cooperative and reliable autonomous systems for the mobility of the future.