Publication
The Impact of Positional Accuracy in 6G Networks on Urban Traffic Participant Classification
Florian Langenstein; Christof Rauber; Christoph Fischer; Hans Dieter Schotten (Hrsg.)
International Conference on 6G Networking (6GNet-2024), Proceedings of the 3st International Conference on 6G Networking, Paris, France, IEEE Xplore, 2024.
Abstract
The advent of 6G mobile networks promises significant advancements in wireless communications, particularly in
positioning accuracy, which is expected to transform intelligent
transportation systems and smart city infrastructure. This study
explores the potential of using high-precision positioning data to
classify various traffic participants within urban environments,
such as pedestrians, bikes, buses, motorcycles, trucks, and cars.
A neural network-based model was developed and evaluated
using synthetic traffic data generated by the Simulation of
Urban MObility (SUMO) tool. The impact of positional accuracy
on classification performance was systematically analyzed, with
environments modeled at different accuracy levels, including
Urban-Macro (UMa) with 10 meters accuracy and Urban-Micro
(UMi) with 1 meter accuracy. The results highlight the critical
role of precise positioning in enhancing classification accuracy,
particularly for vehicles with similar velocity profiles, such as
cars and trucks.