Selection Method of a Driving Simulator Motion System

Bruck, Lucas; Veldhuis, Stephen C.; Emadi, Ali

doi:10.1109/itec.2019.8790465

Cited by 2 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When building a simulator, the definition of the motion system is key, and should be done based on the intended application. The work in [77] develops an objective approach to be used when selecting between a 2-DOF, 3-DOF, or 6-DOF motion systems. The work also proposes a method to optimize the motion system geometry based on the future application of the driving simulator.…”

Section: H Motion Systemmentioning

confidence: 99%

A Review of Driving Simulation Technology and Applications

Bruck

Haycock

Emadi

2021

IEEE Open J. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

Driving simulation has become a very useful tool for vehicle design and research in industry and educational institutes. This paper provides a review of driving simulator components, including the vehicle dynamics model, the motion system, and the virtual environment, and how they interact with the human perceptual system in order to create the illusion of the driving. In addition, a sample of current state-of-the-art vehicle simulators and algorithms are described. Finally, current applications are discussed, such as driver-centered studies, chassis and powertrain design, and autonomous systems development.

show abstract

Section: H Motion Systemmentioning

confidence: 99%

A Review of Driving Simulation Technology and Applications

Bruck

Haycock

Emadi

2021

IEEE Open J. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Error minimization was studied with adaptive linear quadratic regulated motion cueing algorithm (MCA) in order to create a realistic driving feeling on 8 DoF driving simulators [24]. It has been studied to improve the technical arguments by using genetic algorithm for the optimization of 3 different motion systems on 7 DoF vehicle simulators [25]. Sliding mode control and model predictive control were studied on how to drive the motion cueing algorithm (MCA) to the system on the 4 DoF driving platform.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and NN-Based Enhanced Test Maneuver Deployment for 2 DoF Vehicle Simulator

et al. 2023

View full text Add to dashboard Cite

This paper presents a deployment method of various test maneuver scenarios for 2 degree of freedom (2 DoF) vehicle simulator by using feature extraction and neural networks (NN). A prototype version has been set up for the 2 DoF vehicle simulator. Then, a hardware in the loop (HIL) model with 2 inputs (torque, τ 1 -τ 2 ) and 3 outputs (acceleration, a x -a y -a z ) is created. System identification is performed to obtain the training data of NNs to be used for the deployment of test maneuvers. In the system identification process, 2 arbitrary sinusoidal torque signals (τ 1 -τ 2 ) are generated by using the actuator specs of the 2 DoF vehicle simulator. By applying the generated torque signals to the actuators, acceleration (a x -a y -a z ) data are collected from the inertial measurement sensor (IMU) on the 2 DoF vehicle simulator. It is determined to create 3 different NN models for the obtained data. The 1st NN model is trained with 3 inputs (a x -a y -a z ) and 2 targets (τ 1 -τ 2 ) training data. The 2nd NN model is trained with 6 inputs (amplitudes and phases of a x -a y -a z ) and 2 targets (τ 1 -τ 2 ) training data. The input data features for the 2nd NN model is extracted by using the Fast Fourier Transform (FFT). The 3rd NN model is trained with 6 inputs (amplitudes and phases of a x -a y -a z ) and 4 targets (amplitudes and phases of τ 1 -τ 2 ) training data. For the 3rd NN model, the features of input and target data are extracted by using the FFT. The NN training process continues until acceptable performance criteria are reached. Then, 3 NN models are run and analyzed under various test scenarios such as Double Lane Change, Constant Radius, Increase Steer, Fish Hook, Sine with Dwell and Swept Sine. Only for the 3rd NN, the actuator signals (τ 1 -τ 2 ) are recomposed by applying an inverse FFT process to the 4 targets (amplitudes and phases of τ 1 -τ 2 ). Finally, the reference trajectory tracking performances are evaluated by comparing the NN models that are run under the test scenarios.

show abstract

Selection Method of a Driving Simulator Motion System

Cited by 2 publications

References 16 publications

A Review of Driving Simulation Technology and Applications

A Review of Driving Simulation Technology and Applications

Feature Extraction and NN-Based Enhanced Test Maneuver Deployment for 2 DoF Vehicle Simulator

Contact Info

Product

Resources

About