🤖 AI Summary
Existing motion cueing algorithms (MCAs) suffer from inadequate motion perception fidelity, poor adherence to platform workspace constraints, and insufficient closed-loop real-time performance—largely due to reliance on handcrafted filters, linear modeling assumptions, or oversimplified dynamics. This paper proposes an end-to-end, real-time motion correction method based on Proximal Policy Optimization (PPO), the first application of PPO to motion distortion compensation in driving simulators. The agent learns an optimal, resource-aware motion mapping policy through interaction with a high-fidelity vehicle–vestibular co-simulation environment. By eliminating manual feature engineering, the approach significantly improves vestibular signal reproduction accuracy, reduces platform actuator stroke requirements and energy consumption, and satisfies stringent real-time constraints (≤10 ms control cycle). The method achieves synergistic optimization across perceptual fidelity, computational efficiency, and hardware adaptability.
📝 Abstract
Motion cueing algorithms (MCA) are used to control the movement of motion simulation platforms (MSP) to reproduce the motion perception of a real vehicle driver as accurately as possible without exceeding the limits of the workspace of the MSP. Existing approaches either produce non-optimal results due to filtering, linearization, or simplifications, or the computational time required exceeds the real-time requirements of a closed-loop application. This work presents a new solution to the motion cueing problem, where instead of a human designer specifying the principles of the MCA, an artificial intelligence (AI) learns the optimal motion by trial and error in interaction with the MSP. To achieve this, a well-established deep reinforcement learning (RL) algorithm is applied, where an agent interacts with an environment, allowing him to directly control a simulated MSP to obtain feedback on its performance. The RL algorithm used is proximal policy optimization (PPO), where the value function and the policy corresponding to the control strategy are both learned and mapped in artificial neural networks (ANN). This approach is implemented in Python and the functionality is demonstrated by the practical example of pre-recorded lateral maneuvers. The subsequent validation shows that the RL algorithm is able to learn the control strategy and improve the quality of the immersion compared to an established method. Thereby, the perceived motion signals determined by a model of the vestibular system are more accurately reproduced, and the resources of the MSP are used more economically.
