Authors:
Dongcheng Qiu, Fabien De-oliveira, Florent Colombet, Matthieu Sommervogel, Stéphane Régnier, Zhou Fang
Keywords:
AI-based predictive MPC, driver behavior prediction, motion cueing algorithm, ROADS
Abstract:
Fang Z.; De-oliveira F.; Qiu D.; Colombet F.; Régnier S. and Sommervogel M. Enhancing Longitudinal Motion Cueing via AIBased Driver Behavior Prediction In: Proceedings of the Driving Simulation Conference 2026 Europe XR, Driving Simulation Association, Antibes, France, 2026, pp. 113 - 118
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@inproceedings{Fang2026,
title = {Enhancing Longitudinal Motion Cueing via AIBased Driver Behavior Prediction},
author = {Zhou Fang and Fabien De-oliveira and Dongcheng Qiu and Florent Colombet and Stéphane Régnier and Matthieu Sommervogel},
editor = {Andras Kemeny and Jean-Rémy Chardonnet and Florent Colombet and Stéphane Espié},
doi = {https://doi.org/10.82157/dsa/2026/15},
isbn = {978-2-9573777-9-4},
year = {2026},
date = {2026-09-16},
urldate = {2026-09-16},
booktitle = {Proceedings of the Driving Simulation Conference 2026 Europe XR},
volume = {11},
pages = {113 - 118},
address = {Antibes, France},
organization = {Driving Simulation Association},
abstract = {Renault’s high-performance driving simulator, ROADS, is an advanced tool for vehicle development that utilizes virtual proving grounds to evaluate dynamic performance. While lateral motion perception has reached high fidelity through an internal Model Predictive Control-based Motion Cueing Algorithm (MPC-MCA), longitudinal performance remains limited by the unpredictable nature of driver behavior. This paper investigates various AI architectures, specifically LSTM, Embedding LSTM, and Informer, to enhance longitudinal predictive performance within the ROADS real-time platform. By forecasting future driver actions, the MPC-MCA optimizes platform prepositioning and workspace management. Preliminary numerical results demonstrate a 20% increase in motion restitution amplitude of longitudinal acceleration, significantly improved workspace utilization, and lower required tilt velocity.},
keywords = {},
}
Download .bib file
TY - CONF
TI - Enhancing Longitudinal Motion Cueing via AIBased Driver Behavior Prediction
AU - Fang, Zhou
AU - De-oliveira, Fabien
AU - Qiu, Dongcheng
AU - Colombet, Florent
AU - Régnier, Stéphane
AU - Sommervogel, Matthieu
C1 - Antibes, France
C3 - Proceedings of the Driving Simulation Conference 2026 Europe XR
DA - 2026/09/16
PY - 2026
SP - 113
EP - 118
LA - en-US
PB - Driving Simulation Association
SN - 978-2-9573777-9-4
L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2026/enhancing-longitudinal-motion-cueing-via-ai-based-driver-behavior-prediction
ER -
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