Objective Evaluation of a Novel Filter-Based Motion Cueing Algorithm in Comparison to Optimization-Based Control in Interactive Driving Simulation

摘要

Dynamic driving simulators have become a key technology to support the development and optimization process of modern vehicle systems both in academic research and in the automotive industry. However, the validity of the results obtained in simulator tests depends significantly on the adequate reproduction of the simulated vehicle movements and the associated immersion of the driver. Therefore, specific motion platform control strategies, so-called Motion Cueing Algorithms (MCA), are used to replicate the acting accelerations and angular velocities within the physical limitations of the driving simulator best possible. In this paper, we present a novel filter-based control approach for this task, using a hybrid kinematics motion system as an application example. Based on introduced quality criteria, an objective comparison of the proposed control strategy and a real-time capable Model Predictive Control (MPC) algorithm is performed using various standard driving scenarios. These include longitudinal as well as lateral dynamic maneuvers in order to estimate the overall improvements of both Motion Cueing Algorithms for interactive driving simulation.