Evaluation of the path tracking performance of autonomous vehicles using the universal motion simulator

© 2018 IEEE. Autonomous vehicles (AVs) are considered one of the most promising solutions for enhancing road safety, saving individuals' time, and reducing energy consumption. Autonomous vehicles are still in their early stage to be publicly accepted and gain a high level of trust. They need to be comprehensively and continuously evaluated and improved through road tests which are risky, costly, and time-consuming. Motion simulators are capable of contributing to these tests by providing an immersive virtual environment and high fidelity ride experiences for subjective and objective evaluations of AVs' performance. This paper provides a simulation study on the capability of a serial motion platform, known as the Universal Motion Simulator (UMS), for emulation of an AVs' path tracking capabilities. For this purpose, a versatile path tracking model of an AV is initially introduced. The computational Multi-Body System (MBS) approach is then used to implement inverse kinematics and dynamics analyses of the UMS. The UMS is also equipped with an optimal Motion Cueing Algorithm (MCA) to emulate the motion sensation of the AV performing different manoeuvres. The results show that the UMS is an efficient tool for regenerating a realistic and high-fidelity AV ride experience when (1) curvature of the trajectory is not large and the AV does not experience large turning angles when it negotiates the path, and (2) the human motion sensation (vestibular system mathematical model) is taken into consideration in developing the MCA of the motion simulator.