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In this paper we present a car detection system prototyped within an experimental project. It analyzes video streams recorded by drones flying over an urban environment. The intended final goal is the automatic provision of helpful information, such as the available parking spaces and the level of congestion of the streets. The system has been tested both in a desktop PC and on an embedded system. The experimental results show a significant accuracy and prove the feasibility of novel on-board services.

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The fifth generation of mobile networks (5G) is rapidly reaching deployment across the globe, promising a series of advances for vertical service providers, both in terms of performance and in terms of operational capabilities. In this context, the 5G-IANA Network Application platform focuses on the rapidly advancing domain of intelligent, data centric, Artificial Intelligence / Machine Learning (AI/ML)-enabled applications, with a particular focus on the automotive domain. In this paper, we present the key functional features designed for the support of such services including the integration of (mobile) far-edge resources, as well as ML-aware orchestration primitives. This includes novel features such as decision support for the optimal distribution of end-to-end ML pipelines, as well as run-time support for client selection in Federated Learning setups, far-edge failure handling and distribution drift aware lifecycle management. Such features come to address a series of limitations associated with legacy 5G management & orchestration systems, such as resource consumption of data centric services and privacy support. In this context, we further discuss the new opportunities arising for service provisioning and corresponding business models in the automotive ecosystem, with a particular emphasis on the implications of the emerging data and/or ML-model sharing schemes.

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