Subjective Evaluation of Motion Cueing Algorithms: A Comparative Study on Real-World Expectations in a Robot-Based Driving Simulator

The growing prevalence of interactive driving simulators as affective instruments in various fields, including driver training, vehicle dynamics design, and advanced driving assistance system testing, highlights the significance of precise vehicle motion replication and driver immersion. Motion cueing algorithms (MCAs) play a pivotal role in generating realistic motion feedback based on driver interactions within simulated environments. These algorithms are designed to regulate motion platforms, thereby emulating accelerations and angular velocities, and thus replicating a vehicles physical dynamics. A taxonomy of MCA methodologies is available, primarily categorized into filter-based and optimization-based approaches. However, a consensus on the relative effectiveness of these methodologies remains elusive. recent efforts have centered on the development of both subjective and objective evaluation criteria to facilitate the comparison of diverse MCAs. While earlier studies have primarily focused on hexapod-based systems, thereby contributing to a more profound understanding of motion cueing effectiveness across disparate simulation platforms. Contrary to other studies, participants evaluate an interactive ride against their real-world expectations.