Business Finland 6G Visible project’s objective is the development of 6G-era service and architecture solutions utilizing autonomous and semi-autonomous driving, with both physical and logical computational elements and with use cases for real-life verification and validation. Finnish Meteorological Institute is focusing especially on weather- and safety-related services for autonomous vehicles. We are tailoring our road weather services for the special needs of autonomous driving, keeping in mind that autonomous vehicles are more sensitive to the harsh winter weather conditions and benefit from more accurate weather information considering the sensor systems of each vehicle. Employing weather radar-based nowcasting of more accurate short-term precipitation forecasting benefits autonomous traffic, especially in cases of heavy local precipitation by re-routing/route planning and avoiding heaviest precipitation. Evaluation of autonomous vehicles’ sensor systems’ sensitivity to harsh weather conditions allows for weather forecasting based on the real vulnerability of each vehicle.

Accidents involving heavy road vehicles are often destructive, causing operational losses, human casualties, infrastructure losses, and negative environmental impacts. The risk is especially high in wintertime traffic. The Eureka Xecs SafeTrucks project (Heavy traffic safety improvements by advanced dynamics and road weather services) develops real-time vehicle-specific weather and safety services tailored to each vehicle, based on the vehicle’s own sensor-based observations combined with data from weather service systems and an analysis of the vehicle’s own dynamics. The services will also be analyzed by Digital Twin modeling in Hardware-in-the-Loop (HIL) and Driver-In-the-Loop (DIL) scenarios, in order to evaluate and refine them in a controlled environment. This paper focuses on operative fleet piloting, while the Digital Twin approach will be presented in future work. The pilot services are ultimately tested in a pilot system within operative heavy traffic. This paper presents the concept and architecture of the platform, with preliminary results of pilot services operation, alternative communication analysis, and system evaluation.

Autonomous driving can be challenging especially in winter conditions when road surface is covered by icy and snow or visibility is low due to precipitation, fog or blowing snow. These harsh weather and road conditions set up very important requirements for the guidance systems of autonomous cars. In the normal conditions autonomous cars can drive without limitations but otherwise the speed must be reduced, and the safety distances increased to ensure safety on the roads. Autonomous driving needs very precise and real-time weather and road condition information. Data can be collected from different sources, like (road) weather models, fixed road weather station network, weather radars and vehicle sensors (for example Lidars, radars and dashboard cameras). By combining the all relevant weather and road condition information a weather-based autonomous driving mode system is developed to help and guide autonomous driving. The driving mode system is dividing the driving conditions from perfect conditions to very poor conditions. In between there are several steps with slightly alternate driving modes depending for example snow intensity and friction. In the most challenging weather conditions, automatic driving must be stopped because the sensors guiding the driving are disturbed by for example heavy snowfall or icy road.Finnish Meteorological Institute is testing autonomous driving in the Arctic vehicular test track in Sodankylä, Northern Finland. The test track is equipped with road weather observation system network including road weather stations, IoT sensors measuring air temperature and humidity along with various communication systems. Also, tailored road weather services are produced to the test track, like precise road weather model calculations and very accurate radar precipitation observations and nowcasting. The developed weather-based autonomous driving system is tested on Sodankylä test track among other arctic autonomous driving testing.This study presents the Sodankylä Arctic vehicular test track environment and weather-based autonomous driving mode system that is developed at the Finnish Meteorological Institute.

Business Finland 6G Visible project’s objective is the development of 6G-era service and architecture solutions utilizing autonomous and semi-autonomous driving, with both physical and logical computational elements and with the use cases for real-life verification and validation. Finnish Meteorological Institute is focusing especially on weather and safety related services for autonomous vehicles. We are tailoring our road weather services for the special needs of autonomous driving, keeping in mind that autonomous vehicle is more sensitive to the harsh winter weather conditions and benefit from more accurate weather information considering the sensor systems of each vehicle. Employing weather radar-based Nowcasting of more accurate short-term precipitation forecasting benefits autonomous traffic especially in cases of heavy local precipitation by re-routing/route-planning and avoiding heaviest precipitation. Evaluation of autonomous vehicles’ sensor systems sensitivity to harsh weather conditions allows the weather forecasting based on real vulnerability of each vehicle.

A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development.

Accidents involving heavy road vehicles are often destructive, causing operational losses, human casualties, infrastructure losses, and negative environmental impacts. The risk is especially high in wintertime traffic. Safetrucks project targeted to establish a basis for enhancing heavy vehicle safety by delivering warnings and data directly to drivers of such vehicles. The services will also be analyzed by Digital Twin modelling in Hard-ware-in-the-Loop (HIL) and Driver-In-the-Loop (DIL) scenarios, to evaluate and refine them in a controlled environment. This paper focuses on the operative fleet piloting within operative heavy traffic, Digital Twin approach representing simulations providing parallel evaluation approach employed in more detail in our future publica-tions. This paper presents the concept and architecture of the platform, with results of pilot services operation, communication alternatives analysis, preliminary evaluation of braking distance and tyre measurements as the sources of vehicle dynamics data in pilots and general system evaluation.