Edge-powered Assisted Driving For Connected Cars

TL;DR

This paper presents an edge-based assisted driving system for connected cars that optimizes traffic flow and reduces travel times using a novel algorithm, demonstrating significant improvements over traditional approaches.

Contribution

It introduces a new edge-powered architecture and an efficient optimization algorithm for global traffic decisions in connected vehicle systems.

Findings

Reduces vehicle travel times by 66% for lane change assistance.

Achieves 20% reduction in travel times for navigation services.

Demonstrates effectiveness through realistic simulations.

Abstract

Assisted driving for connected cars is one of the main applications that 5G-and-beyond networks shall support. In this work, we propose an assisted driving system leveraging the synergy between connected vehicles and the edge of the network infrastructure, in order to envision global traffic policies that can effectively drive local decisions. Local decisions concern individual vehicles, e.g., which vehicle should perform a lane-change manoeuvre and when; global decisions, instead, involve whole traffic flows. Such decisions are made at different time scales by different entities, which are integrated within an edge-based architecture and can share information. In particular, we leverage a queuing-based model and formulate an optimization problem to make global decisions on traffic flows. To cope with the problem complexity, we then develop an iterative, linear-time complexity algorithm called Bottleneck Hunting (BH). We show the performance of our solution using a realistic simulation framework, integrating a Python engine with ns-3 and SUMO, and considering two relevant services, namely, lane change assistance and navigation, in a real-world scenario. Results demonstrate that our solution leads to a reduction of the vehicles' travel times by 66 in the case of lane change assistance and by 20 for navigation, compared to traditional, local-coordination approaches.