An Edge-powered Approach to Assisted Driving

TL;DR

This paper proposes an edge-powered system architecture combined with a queue-based model and an efficient algorithm to optimize vehicle flow and significantly reduce travel times in connected vehicle networks.

Contribution

It introduces a novel integrated system architecture and the Bottleneck Hunting algorithm for real-time flow optimization in connected vehicle environments.

Findings

Significantly shorter travel times with the proposed approach.

Effective optimization of vehicle flows in realistic simulations.

Demonstrated applicability to lane change and navigation services.

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.