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Evaluation of Motion Cueing Algorithms for a Limited Motion Platform Driver-in-Loop Simulator

Sekar, Rubanraj
http://rave.ohiolink.edu/etdc/view?acc_num=osu1576801483307636

Abstract Details

2020, Master of Science, Ohio State University, Mechanical Engineering.
Four motion cueing algorithms are evaluated for a low cost, driver-in-loop (DiL) simulator with limited platform motion: roll, pitch, and heave. The DiL is augmented with additional non-vestibular cueing. Double lane change and slalom maneuvers are chosen for evaluating the algorithms because they incorporate lateral dynamics. The primary aim of the simulator is for vehicle assessment purposes. The population for conducting the experimental runs is restricted to be drivers with no prior professional or competitive driving background. For this reason, the experiment is designed to have the maneuvers driven under sub-limit conditions. The experiments are conducted in a virtual driving environment designed for conducting isolated experiments. The virtual world was designed to increase the ease of repeatability of the experimental runs. The simulated vehicle run data is gathered and analyzed based on vehicle lateral dynamics, driver input, and driver’s performance. Statistical analysis is conducted to identify the presence of significant differences resulting from the variation of motion cueing algorithms over 24 drivers. Within subjects analysis, paired hypothesis testing, and effect size are computed for analyzing the driving data. The drivers are also monitored for simulator sickness using a subjective questionnaire for each of their experimental driving runs. The results from this study can be used to identify the effect of change of motion cueing on the regular drivers. Furthermore, it can also assist in quantitatively ranking the algorithms evaluated. If any of the algorithms are observed to cause drastic degradation in driver’s performance, it will be eliminated in the continual of the research. Statistical analysis showed that double lane change was better at differentiating the cueing algorithms. For both the maneuvers, driver input changed significantly with similar vehicle performance and driver’s performance. The vehicle motion-based cueing algorithm helps reduce excess steering input (corrections) in both the maneuvers over other algorithms. A subjective questionnaire for simulator sickness assessment revealed that shallow levels of simulator sickness were observed in the population, although no trend could be identified with the algorithms.
Rebecca Dupaix (Advisor)
Stephanie Stockar (Committee Member)
Jeffrey Chrstos (Other)
143 p.
Automotive Engineering; Engineering; Mechanical Engineering
Driver-in-Loop Simulator; DiL; Motion Cueing; Vehicle Dynamics; Motion Cueing Algorithms; 3 DoF simulator

Recommended Citations

Citations

  • Sekar, R. (2020). Evaluation of Motion Cueing Algorithms for a Limited Motion Platform Driver-in-Loop Simulator [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1576801483307636

    APA Style (7th edition)

  • Sekar, Rubanraj. Evaluation of Motion Cueing Algorithms for a Limited Motion Platform Driver-in-Loop Simulator. 2020. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1576801483307636.

    MLA Style (8th edition)

  • Sekar, Rubanraj. "Evaluation of Motion Cueing Algorithms for a Limited Motion Platform Driver-in-Loop Simulator." Master's thesis, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1576801483307636

    Chicago Manual of Style (17th edition)

osu1576801483307636
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This open access ETD is published by The Ohio State University and OhioLINK.
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