6G edge-networks and multi-UAV knowledge fusion for urban autonomous vehicles

The advent of 6G wireless networks has the potential to unlock diverse applications of scalable autonomy. By advantageously coupling the individual and aggregated attributes of diverse multi-UAV fleets, a range of high-value applications such as logistics, enhanced disaster response, urban navigation, and surveillance can be significantly improved. However, enabling effective communication for knowledge fusion necessitates the intrinsic optimization of performance metrics like energy consumption, resource allocation, latency, and computational overheads to enhance autonomous efficiency. Furthermore, designing robust security features is essential to safeguarding privacy, control, and operational integrity. This paper explores a novel collaborative knowledge-sharing (KS) framework that leverages 6G and edge-computing capabilities to facilitate the cooperative training of decentralized machine learning models among multiple UAVs, without the need to transmit raw data. This framework aims to enhance the learning experience and operational efficiency of autonomous vehicles. The DECKS (distributed edge-based collaborative knowledge-sharing) architecture enables Federated Learning (FL) within UAV networks, allowing local models to be trained and shared among neighboring UAVs for creating global models. This promotes intelligent knowledge aggregation without a central entity, enhancing collaborative capabilities among autonomous vehicles. The DECKS architecture efficiently extracts and distributes collaborative shared experience to ground vehicles through edge and direct inference, reducing energy consumption, latency, and computational overhead. Our simulation analysis demonstrates that the DECKS architecture has the potential to reduce energy consumption by 70% in sensorless vehicles and improve autonomous vehicle learning performance by 15% compared to centralized approaches in a distributed environment. This improvement is achieved by comparing the efficiency of systems with and without aggregated knowledge, as well as with a centralized system.