  In the field of motion simulation, the level of immersion strongly depends on
the motion cueing algorithm (MCA), as it transfers the reference motion of the
simulated vehicle to a motion of the motion simulation platform (MSP). The
challenge for the MCA is to reproduce the motion perception of a real vehicle
driver as accurately as possible without exceeding the limits of the workspace
of the MSP in order to provide a realistic virtual driving experience. In case
of a large discrepancy between the perceived motion signals and the optical
cues, motion sickness may occur with the typical symptoms of nausea, dizziness,
headache and fatigue. Existing approaches either produce non-optimal results,
e.g., due to filtering, linearization, or simplifications, or the required
computational time exceeds the real-time requirements of a closed-loop
application.
  In this work a new solution is presented, where not a human designer
specifies the principles of the MCA but an artificial intelligence (AI) learns
the optimal motion by trial and error in an interaction with the MSP. To
achieve this, deep reinforcement learning (RL) is applied, where an agent
interacts with an environment formulated as a Markov decision process~(MDP).
This allows the agent to directly control a simulated MSP to obtain feedback on
its performance in terms of platform workspace usage and the motion acting on
the simulator user. The RL algorithm used is proximal policy optimization
(PPO), where the value function and the policy corresponding to the control
strategy are learned and both are 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 on a standardized double lane change shows that the RL algorithm is
able to learn the control strategy and improve the quality of...
