Location as a service with a MEC architecture

TL;DR

This paper presents a cooperative localization system for automated driving that leverages Mobile Edge Computing to enhance GPS accuracy using probabilistic filters and HD maps, enabling lane-precise positioning for multiple vehicles.

Contribution

It introduces a novel cooperative localization approach utilizing MEC and probabilistic filtering to improve GPS accuracy in automated driving scenarios.

Findings

Enhanced lane-precise positioning accuracy

Effective use of MEC for real-time data processing

Improved GPS reliability through cooperative data sharing

Abstract

In recent years, automated driving has become viable, and advanced driver assistance systems (ADAS) are now part of modern cars. These systems require highly precise positioning. In this paper, a cooperative approach to localization is presented. The GPS information from several road users is collected in a Mobile Edge Computing cloud, and the characteristics of GNSS positioning are used to provide lane-precise positioning for all participants by applying probabilistic filters and HD maps.