A Lane Merge Coordination Model for a V2X Scenario

TL;DR

This paper proposes a centralized lane merge coordination system for connected vehicles using 5G connectivity, employing machine learning to predict successful merges and optimize trajectory recommendations for autonomous driving safety.

Contribution

It introduces a novel centralized application leveraging machine learning for lane merge coordination in V2X scenarios, enhancing safety and efficiency.

Findings

Performance evaluation of multiple algorithms

Parameter selection to prevent over-fitting

Effective trajectory recommendations for safe merges

Abstract

Cooperative driving using connectivity services has been a promising avenue for autonomous vehicles, with the low latency and further reliability support provided by 5th Generation Mobile Network (5G). In this paper, we present an application for lane merge coordination based on a centralised system, for connected cars. This application delivers trajectory recommendations to the connected vehicles on the road. The application comprises of a Traffic Orchestrator as the main component. We apply machine learning and data analysis to predict whether a connected vehicle can successfully complete the cooperative manoeuvre of a lane merge. Furthermore, the acceleration and heading parameters that are necessary for the completion of a safe merge are elaborated. The results demonstrate the performance of several existing algorithms and how their main parameters were selected to avoid over-fitting.