Fooling the Model, Failing the Road: Benchmarking Inertial Sensor Fidelity in Driving Simulators - Florencio, Renan; Fernandes, Silvio; Costa, Paula D. Paro (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
CARLA and SUMO are widely adopted open-source simulators in autonomous driving and intelligent transportation systems. While neither was initially designed to generate high-fidelity inertial data such as that from an Inertial Measurement Unit (IMU), both have been employed in contexts where such data is critical. In this study, we evaluate the quality of inertial data produced by CARLA and SUMO through direct comparison with the naturalistic UAH-DriveSet dataset. Our analysis reveals that SUMO performs better in qualitative and quantitative assessments. To our knowledge, this is the first systematic comparison of its kind, highlighting limitations in current simulation tools and offering insights for autonomy-related studies using simulated inertial data.