System Configuration and Validation of Hybrid-Based Six Degrees of Freedom Motion Platform for Vehicle Dynamic Testing

Scenario-based testing has become one of the important elements to evaluate the performance of automated vehicle systems before deploying on actual road. There are several approaches that can be used to conduct scenario-based testing via simulation approach. One of the important aspects in scenario-based safety testing is the driver-in-the-loop (DiL) simulation where it involves integration of hardware and human interaction. Therefore, motion platform-based vehicle driving simulators are commonly used for the DiL simulation for scenario-based testing. Generally, a high degree of freedom driving simulator is used for scenario-based testing such as 6 degrees of freedom (DoF) to achieve high accuracy to represent an actual vehicle response. Moreover, most of the motion platforms are designed using hexapod configuration, which also contributes to 6-DoF. However, this type of design requires large space to conduct the testing because the field of motion (FoM) is high in three axes and high possibility of interference of individual kinematic chains. Besides, previous works did not emphasize on the validity of the dynamic behavior of the motion platform model based on SAE testing standards. This is mainly to ensure the accuracy of the developed motion platform model with actual behaviors of the motion platform. Therefore, a novel approach using combination of 6-DoF motion platform and vehicle simulation software called IPG CarMaker namely Automated Vehicle Engineering System (AVES) Motion Driving Simulator (AMoDS) was developed using vertical and horizontal linear actuator configuration. To understand the dynamic response of the motion platform, 6-DoF equations using Euler–Lagrange theory were derived. Validation was carried out with the data collected from actual motion platform vehicles for comparison with IPG CarMaker simulation outputs. Results showed that the response of the dynamic model closely follows the response of AMoDS and predicts adequately with an error percentage of less than 10% with high correlation.