# A Deep Reinforcement Learning Based Motion Cueing Algorithm for Vehicle Driving Simulation

arXiv: https://arxiv.org/abs/2304.07600 · HTML: https://mlsanity.com/paper/2304.07600/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 15 Apr 2023.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Learning to Perform Physics Experiments via Deep Reinforcement Learning](https://mlsanity.com/paper/1611.01843/) (2016)
- [Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control](https://mlsanity.com/paper/1910.12047/) (2019)

## Built on by (most cited first, 3 of 3)

- [A nonlinear real time capable motion cueing algorithm based on deep reinforcement learning](https://mlsanity.com/paper/2503.10419/) (2025)
- [Learning-Based Approximate Nonlinear Model Predictive Control Motion Cueing](https://mlsanity.com/paper/2504.00469/) (2025)
- [Towards Infant Sleep-Optimized Driving: Synergizing Wearable and Vehicle Sensing in Intelligent Cruise Control](https://mlsanity.com/paper/2506.06459/) (2025)

## Abstract (arXiv)

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.