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

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

Eppink, J.M. (TU Delft Control & Simulation; BMW Group)
Kolff, M.J.C. (TU Delft Control & Simulation; BMW Group)
Venrooij, Joost (BMW Group)
Pool, D.M. (TU Delft Control & Simulation)
Mulder, Max (TU Delft Control & Simulation)

2023

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 shown that a combination of the current vehicle velocity, the speed limit eight seconds ahead and the accelerator pedal deflection yields the most accurate estimate of the probabilities that drivers will accelerate or decelerate. Based on these probabilities, a driving simulator was linearly pre-positioned in combination with a classical washout algorithm. The perceived motion incongruence was subjectively evaluated by the drivers (N = 34), testing: (i) no pre-positioning, (ii) pre-positioning, and (iii) pre-positioning with an increased longitudinal classical washout gain enabled by the pre-positioning. Results show that the pre-positioning improves the margins with respect to the longitudinal workspace limits (better workspace management), without affecting the motion incongruence ratings. When using the increased margins to increase the longitudinal gain, however, no significant reduction in motion incongruence ratings was observed. This is likely due to the small motion space of the hexapod motion system used in the current study. However, this paper shows that longitudinal driving behaviour can be accurately predicted and can enable improved workspace utilization for driving simulators.

Motion cueing
behaviour prediction
pre-positioning
workspace management

http://resolver.tudelft.nl/uuid:61588ae9-bbba-4afe-85ab-a6e277f1148f

2024-03-01

Proceedings of the 22nd driving Simulation & Virtual Reality Confrence

22nd driving Simulation & Virtual Reality Confrence, 2023-09-06 → 2023-09-08, Antibes, France

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 J.M. Eppink, M.J.C. Kolff, Joost Venrooij, D.M. Pool, Max Mulder