Subjective Evaluation of Filter- and Optimization-Based Motion Cueing Algorithms for a Hybrid Kinematics Driving Simulator

Abstract

Interactive driving simulation has become a key technology to support the development and optimization process of modern vehicle components and driver assistance systems both in academic research and in the automotive industry. However, the validity of the results obtained within the virtual environment depends essentially on the adequate reproduction of the simulated vehicle movements and the corresponding immersion of the driver. For that reason, specific motion platform control strategies, so-called Motion Cueing Algorithms (MCA), are used to replicate the simulated accelerations and angular velocities within the physical limitations of the driving simulator best possible. In this paper, we present the design and evaluation of a subjective comparison of three different filter- and optimization-based MCA resulting from previous research. For that purpose, a Human-in-the-Loop experiment was conducted with 27 participants in four typical driving situations, using a hybrid kinematics motion system as an application example. The statistical analysis of the study proves that the optimization-based algorithm is preferred by the subjects regardless of the presentation sequence and the respective driving maneuver, while the filter-based approaches differ only insignificantly in their ranking. Results thus correlate with the findings of an objective evaluation of the control quality and identify further potentials for improving the driving experience.