Hackable Motion Cueing for the 21st Century
Hackable Motion Cueing for the 21st Century
Today, the majority of simulators are underutilised. That is to say, are running in a sub-optimal motion cueing configuration. This reduces driver satisfaction, reducing the quantity and quality of insights from driver-in-the-loop (DIL) activities.
This is not necessary, at least from a technical sense. Currently, the cost of re-adjusting motion cueing to be optimal on a case by case basis is too time consuming, requiring expert knowledge and professional driver time.
Fortunately, there is now a cost-effective and easy solution to extract the maximum performance from your simulator hardware. That solution is ProCue.
Master even the trickiest motion cueing optimisation problems with our derivative-free, local optima resistant solver.
Whether a manual parameter sweep or a large scale optimisation, ProCue will make full use of all your CPU cores.
Plot and compare an arbitrary number of motion cueing algorithms together, in terms of workspace usage, driver perception and more (including custom outputs).
Incorporate layered velocity, acceleration and jerk limits - fully hackable.
Deploy ProCue's alternative Motion Cueing Algorithms (MCA's) to your simulator, shipped as compilable Simulink models.
Manage the many permutations of scenario(s), MCA('s) and driver(s). Git-like", plain-text traceability.
Incorporate and share your own motion cueing algorithms. Simple compilation process from Simulink leading to completely stand-alone MCA at run-time. IP is protected!
Incorporate and share your own perception models. Build bespoke pereception models to represent driver "quirks".
Write arbitrary workspaces as a simple Python module. Carry out simulator studies before a simulator is even purchased/built.
Customise your optimisation. Separate constants from tunable variables. Link axes for symmetrical cueing and save computing time. Or leave everything up to the optimiser!
Minimise time wasted on tuning cueing systems and safety stops due to bad tuning.
Reduce time wasted on shakedowns by getting cueing 90% right offline. Increase driver satisfaction.
With cueing quality comes better driver/pilot insights, leading to better engineering and set-up decisions, as well as training outcomes.
R&D is a costly and risky activity for any company, but especially simulator teams. Let us do this work for you!
Brown, C., 2024. Reduced Cross-Axis Distortion Motion Cueing . In: A. Kemeny, J.-R. Chardonnet, F. Colombet and S. Espie (eds.), Proceedings of the Driving Simulation Conference 2024, Strasbourg, France
Brown, C., 2023. A Nonlinear Extension to Classical Filters for Washout Miscue Prevention . In: A. Kemeny, J.-R. Chardonnet, and F. Colombet (eds.), Proceedings of the Driving Simulation Conference 2023, Antibes, France
Brown, C., 2020. Motion Cueing Washout Tuning based on Step Responses. In: A. Kemeny, J.-R. Chardonnet, and F. Colombet (eds.), Product Solutions Book of the Driving Simulation Conference 2020, Antibes, France
Brown, C., Jin, Y., Leach, M. et al., 2016, μJADE: adaptive differential evolution with a small population. Soft Comput 20, 4111–4120
Brown, C., Jin, Y., Leach, M. et al., 2016, Towards Generic-Optimal Domestic Heating Control, Thesis, University of Surrey
BrownSim is committed to academic research. Be part of the BrownSim community or help improve your favorite cueing software.
Request Academic License