Realistic Global Navigation Satellite System (GNSS) synthetic data is essential for the research and development of vehicular applications, such as Advanced Driver Assistance Systems (ADAS), autonomous driving, and solutions or scenarios that are difficult and expensive to test in the real world, such as vehicular cooperative positioning. However, generating GNSS synthetic data is complex due to satellite dynamics, signal characteristics, and various noise and error sources. This complexity increases in automotive contexts by vehicle movement and environmental factors influencing signal propagation, with multipath effects being particularly challenging to simulate accurately. This paper introduces a novel pipeline that leverages a 3D virtual environment to produce more realistic GNSS synthetic data for automotive applications. The pipeline integrates the CARLA Simulator and GPSoft's SatNav Toolbox for Matlab, with custom-developed modules that generate raw GNSS measurements incorporating environment- and location-specific multipath effects. Our contributions include a tailored simulation pipeline for automotive applications, with integration of GNSS satellite orbits within CARLA, a dynamic multipath model reflecting obstacles in the simulated environment, and a synthetic dataset generated by this approach available to the community. Evaluation on CARLA's Town03 map showed that while standard multipath models result in unrealistic uniform effects, our dynamic model produces effects that correlate with the vehicle's surroundings, accurately reflecting real-world conditions such as increased errors in urban areas and lack of signals in tunnels. This approach can support the research, development, and validation of GNSS positioning algorithms and Artificial Intelligence (AI) model training, with potential applications extending also beyond the automotive context.
