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AI-Based Traffic Queue Detection for IoV Safety Services in 5G Networks

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Intelligent Technologies for Internet of Vehicles

Part of the book series: Internet of Things ((ITTCC))

Abstract

With the deployment of 5th Generation networks, characterized by a wider bandwidth and an increased computational capability, is now possible to develop more complex services that requires low latency, such as applications for public safety based on artificial intelligence. To this aim, it has been studied a possible use of the so-called Multi-access Edge Computing (MEC) enabled by 5G that reduces the latency thanks to computational resources located closer to the user. This allows to deploy an application that recognizes the formation of traffic queues on the highway through the analysis of video streams, to be used in the context of smart mobility. In order to do so, it is needed to detect the travelling vehicles and to track their movement to understand when a traffic jam is occurring. For the implementation, the Convolutional Neural Network (CNN) paradigm has been leveraged for the detection of the vehicles. Among the several alternatives compared, it has been chosen the third version of You Only Look Once (YOLO) for its trade-off between accuracy and real-time computation. Then, the detections tracked through the Simple Online and Realtime Tracking (SORT) algorithm are exploited to identify the direction of the traffic flows and then to calculate when the vehicles are slowing down or stop**, by either measuring the number of stationary vehicles or the travelling time of the vehicles within a region of interest. The service has been developed with the objective of being employed through the parallel computation offered by 5G MEC servers equipped with modern GPUs, in order to obtain real-time performances.

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Notes

  1. 1.

    IoU is an index that measures the closeness between the anchor box and the ground truth box through the division of the area obtained by intersecting the two boxes with the area obtained by the union of them.

  2. 2.

    Common Objects in COntext (COCO): https://cocodataset.org/.

  3. 3.

    Imagenet: http://www.image-net.org/.

  4. 4.

    Pascal VOC 2007: http://host.robots.ox.ac.uk/pascal/VOC/voc2007/.

  5. 5.

    For the sake of simplicity we will refer to them as Video 1 and Video 2, but the original title are: M6 Motorway Traffic uploaded by the channel “DriveCamUK” (https://youtu.be/PNCJQkvALVc) for Video 1, and 4K Road traffic video for object detection and tracking uploaded by Karol Majek (https://youtu.be/MNn9qKG2UFI). Due to copyright reasons, in this section will be shown frames extracted by the second video that have been published under the Creative Commons license.

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Authors and Affiliations

  1. Cefriel, Milan, Italy

    Simone Grilli & Gianmarco Panza

Authors
  1. Simone Grilli
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  2. Gianmarco Panza
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Correspondence to Simone Grilli .

Editor information

Editors and Affiliations

  1. LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal

    Naercio Magaia

  2. Department of Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, Lakonia, Greece

    George Mastorakis

  3. Department Computer Science, University of Nicosia, Nicosia, Cyprus

    Constandinos Mavromoustakis

  4. Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece

    Evangelos Pallis

  5. Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece

    Evangelos K. Markakis

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Grilli, S., Panza, G. (2021). AI-Based Traffic Queue Detection for IoV Safety Services in 5G Networks. In: Magaia, N., Mastorakis, G., Mavromoustakis, C., Pallis, E., Markakis, E.K. (eds) Intelligent Technologies for Internet of Vehicles. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-76493-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-76493-7_3

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