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Context-Aware Forecasting of Mobile Network Quality for Autonomous Vehicle Connectivity

Authors:
Mathias Gerstner
Rudolf Hackenberg

Keywords: autonomous driving; mobile network; connectivity forecasting; signal quality indicators; rsrp

Abstract:
As autonomous driving becomes increasingly feasible, the German government has introduced a legal framework to enable the operation with Level 4 automated driving functionality. A key requirement is the maintenance of a continuous connection between such vehicles and a remote technical supervisor. If this link is lost, the vehicle must transition into a safe state by bringing itself to a controlled stop. To mitigate the risk of connection loss, accurate forecasting of mobile network availability along routes is essential. This paper presents an Exploratory Data Analysis (EDA) based on 38 measurement runs collected over ten months along a rural 64 km route in Germany. The dataset includes passive mobile network signal quality parameters, Global Navigation Satellite System (GNSS) position and precision data, as well as contextual features, such as speed, driving direction, day of the week, weather, and distance to the connected base station. Although mean values capture overall tendencies for areas with consistently good or poor coverage, they fail to capture the variability necessary for reliable prediction on a per-trip basis. Notably, some route segments show high variance in signal quality across different measurement runs. This variability is assumed to result from changing environmental influences, such as weather or traffic conditions at different times. Our analysis reveals weak but statistically relevant correlations between several contextual features (e.g., temperature ≈ -0.2) and network quality indicators. The inclusion of weather parameters or the day of the week has been shown to lower the Mean Absolute Error (MAE) compared to a prediction based only on measurements from the past. These findings underscore the importance of contextual information and localized modeling to predict network availability for safety-critical systems, such as autonomous vehicles.

Pages: 1 to 7

Copyright: Copyright (c) IARIA, 2025

Publication date: October 26, 2025

Published in: conference

ISBN: 978-1-68558-320-0

Location: Barcelona, Spain

Dates: from October 26, 2025 to October 30, 2025