The Impact of Positional Accuracy in 6G Networks on Urban Traffic Participant Classification

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.