A Motion Cueing Algorithm Based on Model Predictive Control Using Terminal Conditions in Urban Driving Scenario

Abstract

The motion cueing algorithm (MCA) is in charge of the real vehicle motion feeling regeneration for the driver of the simulation-based motion platform (SBMP) with respect to its limitations. The model predictive control (MPC) has newly employed in developing MCAs to calculate the optimal input signals for delivering the best motion feeling to the SBMP's drivers while respecting the boundaries of the platform. The stability of the MCA based on MPC has become one of the main issues for some scenarios such as urban driving scenario which involves sudden decelerations/accelerations (stop and start moving), sharp and large turn, slalom movement. The urban driving scenario destabilises the current MCA based on MPC and leads undesired motion fluctuations which creates unpleasant motion artefact for the SBMP drivers. Therefore, the displacement of the SBMP should be penalised conservatively to respect the workspace boundaries for all driving scenarios. This will make the motion conservative and can cause some motion feeling error. In this paper, the concept of terminal conditions (weights and states) are employed for the first time to design and develop a new generation of MCA based on MPC to enhance the performance of the model for different scenarios such as the heavy-traffic scenario in urban areas. Also, the stability of the MPC by considering of the terminal conditions is investigated in MCA domain. Then, the MCA based on MPC by considering of terminal conditions is developed using MATLAB software with presentation of urban motion scenario. The outcomes demonstrate the effectiveness of the designed model with common MCA based on MPC without consideration of the terminal conditions.

Objective Evaluation of Motion Cueing Algorithms for Vehicle Driving Simulator Based on Criteria Importance through Intercriteria Correlation (CRITIC) Weight Method Combined with Gray Correlation Analysis

“…For instance, Mohammad [22,23] and his team compared P0 and P1 by calculating the root mean square error (RMSE) and correlation coefficient (CC) between them. Pham [19] proposed an evaluation index based on the standard error formula.…”

“…Casas [23] analyzed the correlation between NAAD, NPC, and ED and participants' perception in the subjective experiment by combining subjective evaluation with objective evaluation and calculated the correlation coefficient. The higher the correlation between an indicator and participants' perception, the more the indicator can reflect the performance of the MCA in the objective evaluation, and the greater the weight value allocated in the indicator system.…”

A Prediction of Time Series Driving Motion Scenarios Using LSTM and ESN

“…As a result, the motion cueing algorithm (MCA) [21,22] is devised to re-generate the motion sensations for the simulator user to experience the same sensation as if in a vehicle, within the limited working area. MCA is divided to classical [23], adaptive [24][25][26][27][28][29], optimal [30][31][32][33] and model predictive [34][35][36][37][38][39][40][41] methods. In this regard, false motion cues lead to motion sickness in simulator users, which is the main disadvantage of the MCA.…”