Technology Category
Intelligent Robots: Localization, Mapping, and NavigationPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMachine Learning: Learning on the Edge & Model Compression
Location as a service with a MEC architecture
🤖 AI Summary
This work addresses the demand for lane-level high-precision positioning in autonomous driving and advanced driver-assistance systems by proposing a collaborative localization architecture based on mobile edge computing (MEC). The approach integrates GNSS observations from multiple vehicles with high-definition maps and probabilistic filtering algorithms—such as Kalman filtering—to achieve real-time lane-level positioning at the network edge. Leveraging the MEC platform for multi-user data fusion enables significant improvements in both positioning accuracy and system robustness. The proposed solution offers a scalable and low-latency framework for wide-area deployment of high-precision localization services, effectively balancing computational efficiency with stringent performance requirements in dynamic vehicular environments.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
localization
automated driving
ADAS
GNSS
lane-precise positioning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Mobile Edge Computing
cooperative localization
lane-precise positioning
probabilistic filtering
HD maps
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2024-10-03arXiv.orgCitations: 0
