In this paper, we present an enhanced Collision Avoidance (eCA) service that leverages vehicle connectivity through a cellular network to avoid vehicle collisions and increase road safety at intersections. The eCA service is assumed to be deployed at the edge of the network, thus curbing the latency incurred by the communication process. The core of the eCA service is composed of a Collision Avoidance Algorithm (CAA), and a Collision Avoidance Strategy (CAS). The former predicts the vehicle's future trajectory through the positional information advertised by periodic beacons and detects if two vehicles are on a collision course. The latter decides which of the vehicles potentially involved in a collision should yield. The vehicles are then notified of both the impending danger and of the actions needed to avoid it. We have simulated our solution using SUMO (Simulation of Urban MObility) and ns-3 (network simulator 3) with the LENA (LTE-EPC Network simulAtor) framework on a Manhattan-grid road topology, and observed its good performance in terms of avoided collisions percentage as a function of vehicle speed and different vehicles densities. ; Grant numbers : 5G-REFINE - Resource EfFIcient 5G NEtworks project (code: TEC2017-88373-R), 5Growth - 5G-enabled Growth in Vertical Industries project (code: 856709) and SGR - Suport als Grups de Recerca, "Xarxes de Comunicacions" @ 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


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    Title :

    An Edge-based Framework for Enhanced Road Safety of Connected Cars



    Publication date :

    2020-03-01


    Remarks:

    oai:zenodo.org:3982918
    IEEE Access 8 58018-58031



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




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