Driving simulators are essential tools to guide automotive research and development. Their motion system requires a motion cueing algorithm (MCA) to keep the simulator motion platform within its physical boundaries, while simultaneously aiming to recreate the sensation of real vehicle motion. While traditional, filter-based approaches are still predominant, optimization-based MCAs have been at the center of MCA research for over a decade due to their ability to systematically improve motion cueing quality through explicit cost function design and constraints handling. However, despite their demonstrated advantages, these optimization-based methods have not yet achieved widespread adoption in driving simulation. This paper therefore provides a comprehensive review of optimization-based MCAs for driving simulation, categorizing and comparing algorithms, describing their key developments and core characteristics. The current limited real-time capability, lack of accurate evaluation methods, challenges in cost function design and its tuning, and the current lack of accurate future reference predictions are identified as key barriers to the practical deployment and widespread use of optimization-based MCAs. These theoretical and practical challenges are further reviewed, providing guidelines to advance the theory and application of optimization-based MCAs. Central in these advancements are a better understanding of which motions constitute a realistic motion experience, a framework allowing to compare the achieved motion fidelity of MCAs across papers, the design of the cost function focusing on human motion perception, and techniques for easing up the tuning process to swiftly reach high quality tunings for different simulators, scenarios, and use cases. We identify the need for improving the real-time capability, and providing high quality motion reference predictions using learning-based approaches on diverse datasets, along with techniques to handle existing uncertainties. Following these guidelines, new foundations for optimization-based algorithms in driving simulation can be achieved, which will significantly impact the research and development of automotive systems. ... Read more

This paper presents a Model Predictive Control framework for driving simulator motion cueing including effective control of the frequency-domain characteristics of the simulator motion. By transforming the predicted output sequence using the Discrete Fourier Transform (DFT) matrix method, the controller can penalize or amplify specific frequency components. The method is first demonstrated using a discrete-frequency multisine reference use-case, followed by a realistic multi degree-of-freedom driving simulation use-case. Although its inclusion adds
additional constraints on the prediction horizon and the simulation sample time, the frequency-aware motion cueing algorithm enhances the frequency response in both use-cases, thus providing an effective control over the frequency characteristics of the simulator motion. This leads to a better integration of frequency-dependent motion characteristics, a more effective use of the simulator motion workspace, and is expected to provide an improved human perception of the motion. ... Read more

The collective goal of the driving simulation community should be to share ideas to improve the motion cueing across driving simulators worldwide. Due to the active research and intensive usage of driving simulators over the last decades, knowledge in the field of motion cueing has been gained from experience gathered by the community, or practical experience by performing dedicated experiments. This paper discusses several points of ‘common knowledge’ in designing and evaluating motion cueing, along with their value for driving simulation. The goal of the discussion in this paper is to compare these points of common knowledge to the experiences and ideas gathered at BMW’s driving simulation center in Munich, which was opened in 2021 and hosts a fleet of fourteen driving simulators. Furthermore, we aim to bring across points of interest and outlines for future research that should be of interest to the driving simulation community. With the common goal of improving motion cueing, this contribution thus aims to improve and extend the discussion between those working and researching in the driving simulator industry. ... Read more