Automotive services for connected vehicles are one of the main fields of application for new-generation mobile networks as well as for the edge computing paradigm. In this paper, we investigate a system architecture that integrates the distributed vehicular network with the network edge, with the aim to optimize the vehicle travel times. We then present a queue-based system model that permits the optimization of the vehicle flows, and we show its applicability to two relevant services, namely, lane change/merge (representative of cooperative assisted driving) and navigation. Furthermore, we introduce an efficient algorithm called Bottleneck Hunting (BH), able to formulate high-quality flow policies in linear time. We assess the performance of the proposed system architecture and of BH through a comprehensive and realistic simulation framework, combining ns-3 and SUMO. The results, derived under real-world scenarios, show that our solution provides much shorter travel times than when decisions are made by individual vehicles.

An Edge-powered Approach to Assisted Driving / Malandrino, Francesco; Chiasserini, Carla Fabiana; Michele Dell'Aera, Gian. - STAMPA. - (2020). (Intervento presentato al convegno IEEE GLOBECOM 2020 tenutosi a Taipei nel December 2020) [10.1109/GLOBECOM42002.2020.9348235].

An Edge-powered Approach to Assisted Driving

Carla Fabiana Chiasserini;
2020

Abstract

Automotive services for connected vehicles are one of the main fields of application for new-generation mobile networks as well as for the edge computing paradigm. In this paper, we investigate a system architecture that integrates the distributed vehicular network with the network edge, with the aim to optimize the vehicle travel times. We then present a queue-based system model that permits the optimization of the vehicle flows, and we show its applicability to two relevant services, namely, lane change/merge (representative of cooperative assisted driving) and navigation. Furthermore, we introduce an efficient algorithm called Bottleneck Hunting (BH), able to formulate high-quality flow policies in linear time. We assess the performance of the proposed system architecture and of BH through a comprehensive and realistic simulation framework, combining ns-3 and SUMO. The results, derived under real-world scenarios, show that our solution provides much shorter travel times than when decisions are made by individual vehicles.
2020
978-1-7281-8298-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2859182