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Demo Object detection under 5G-edge mob
Ref: CISTER-TR-230610       Publication Date: 15, Jun, 2023

Abstract:
In the mid-term future, vehicles will generate large amounts of data for both standalone usage (e.g., to recognize road features and external elements such as lanes, signs, and pedestrians) and cooperative usage (e.g., lane merging). However, processing the captured video and image data results comes with significant computational requirements (e.g., GPUs). Computer vision tasks, such as feature extraction, are unfeasible from a business perspective if performed directly in the User Equipment (UE), as automotive manufacturers are unwilling to increase the end-product’s costs. Thus, the logical solution is to collect and upload this data to be processed elsewhere. Nonetheless, processing the data as close to the vehicle is important due to latency constraints, thus calling for the use of Mobile Edge Computing (MEC). An additional benefit of this scenario, in which 5G connectivity enables data to be offloaded to the edge, is that the data from our car is not processed alone. Data from several sources, e.g., multiple vehicles and fixed cameras, can be offloaded to the edge node and processed together, enhancing its quality as more sources of data enhance the prediction output of machine-learning models. This demo showcases a video recording from a vehicle uploaded to an edge node via 5G software-defined-radio FPGA devices. There, a YOLO application to detect objects processes the video and communicates this information to the vehicle, ensuring QoS metrics even when the UE performs handover to a different cell or geographical area.

Marco Araújo

,

João Silva

,

Pedro Miguel Santos

,

Himanshu Singh

,

Deepak Gunjal

,

João Fonseca

,

Paulo Duarte

,

Bruno Mendes

,

Raul Barbosa

,

Peter Steenkiste

,

Saeid Sabamoniri

,

Luis Puente Lam

,

João Pereira

,

Harrison Kurunathan

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