Probabilistic Prediction of Longitudinal Driving Behaviour for Driving Simulator Pre-Positioning

Due to the non-deterministic nature of longitudinal human driver behaviour, motion cueing algorithms currently cannot fully utilize the workspace of driving simulators. This paper explores the possibility of using various predictor variables to predict longitudinal driving behaviour. Through the development of a logistic regression model, it is...

Driving Simulator Experiment Stakeholder Perspectives on Motion Cueing Algorithm Quality

In driving simulation, the choice of a simulator, motion cueing algorithm, and associated set of tuning parameters for an experiment is typically made with an exclusive focus on the quality of the motion. In practice, many other metrics could affect this choice as well, such as tuning complexity, algorithm stability, or the financial costs of...

Motion Cueing Quality Comparison of Driving Simulators using Oracle Motion Cueing

BMW’s new driving simulation center operates multiple motion-base simulators – each with a different kinematic configuration – to serve various experiment use-cases and requirements of simulator users. The selection of a simulator for each experiment should ideally be based on their relative strengths and weaknesses. To support this decision...

Quality comparison of motion cueing algorithms for urban driving simulations

When designing driving simulation experiments with motion cueing, it is often necessary to make choices between Motion Cueing Algorithms (MCAs) without being fully able to know how well an MCA will perform during the experiment. Choices between MCAs can therefore be greatly supported by previous measurements or predictions of motion cueing...

Quality comparison of motion cueing algorithms for urban driving simulations

When designing driving simulation experiments with motion cueing, it is often necessary to make choices between Motion Cueing Algorithms (MCAs) without being fully able to know how well an MCA will perform during the experiment. Choices between MCAs can therefore be greatly supported by previous measurements or predictions of motion cueing...

Sensitivity Analysis of an MPC-based Motion Cueing Algorithm for a Curve Driving Scenario

Despite gaining popularity, the use of Motion Cueing Algorithms (MCAs) based on Model Predictive Control (MPC) remains challenging due to the required tuning of a large number of cost function parameters. This paper investigates the effects of two critical MPC cost function parameters, the lateral specific force and roll rate error weights (Way...

Comparison of Quality Metrics between Motion Cueing Algorithms in a Virtual Test Environment

Motion cueing algorithm design often involves a trade-off between priorities due to the limited workspace of the simulator. Such a trade-off requires a detailed understanding of human perception, which we do not yet have. For that reason, objective motion cueing quality metrics, based on the difference between vehicle and simulator signals,...

Simulator Sickness Ratings Reduce with Simulator Motion when Driven Through Urban Environments

A necessity in driving simulation testing is to understand and attenuate simulator sickness, to reduce the number of undesired drop-outs. Especially urban environments, with its many turns and changes in the velocity profile, are a challenge. This paper describes the motion sickness rating results of a between-subjects experiment (n = 63), which...