Thesis-2011-Ishak.pdf (2.29 MB)
Quantification of human operator skill in a driving simulator for applications in human adaptive mechatronics
thesis
posted on 2012-07-12, 15:42 authored by Mohamad H.B. IshakNowadays, the Human Machine System (HMS) is considered to be a proven technology, and now plays an important role in various human activities. However,
this system requires that only a human has an in-depth understanding of the machine
operation, and is thus a one-way relationship. Therefore, researchers have recently
developed Human Adaptive Mechatronics (HAM) to overcome this problem and
balance the roles of the human and machine in any HMS. HAM is different compared
to ordinary HMS in terms of its ability to adapt to changes in its surroundings and the
changing skill level of humans. Nonetheless, the main problem with HAM is in
quantifying the human skill level in machine manipulation as part of human
recognition. Therefore, this thesis deals with a proposed formula to quantify and
classify the skill of the human operator in driving a car as an example application
between humans and machines. The formula is evaluated using the logical conditions
and the definition of skill in HAM in terms of time and error. The skill indices are
classified into five levels: Very Highly Skilled, Highly Skilled, Medium Skilled, Low
Skilled and Very Low Skilled.
Driving was selected because it is considered to be a complex mechanical task that
involves skill, a human and a machine. However, as the safety of the human subjects
when performing the required tasks in various situations must be considered, a driving
simulator was used. The simulator was designed using Microsoft Visual Studio,
controlled using a USB steering wheel and pedals, as was able to record the human
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path and include the desired effects on the road. Thus, two experiments involving the
driving simulator were performed; 20 human subjects with a varying numbers of
years experience in driving and gaming were used in the experiments. In the first
experiment, the subjects were asked to drive in Expected and Guided Conditions
(EGC). Five guided tracks were used to show the variety of driving skill: straight,
circular, elliptical, square and triangular. The results of this experiment indicate that
the tracking error is inversely proportional to the elapsed time. In second experiment,
the subjects experienced Sudden Transitory Conditions (STC). Two types of
unexpected situations in driving were used: tyre puncture and slippery surface. This
experiment demonstrated that the tracking error is not directly proportional to the
elapsed time. Both experiments also included the correlation between experience and
skill. For the first time, a new skill index formula is proposed based on the logical
conditions and the definition of skill in HAM.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Publisher
© Mohamad Hafis Izran B IshakPublication date
2011Notes
A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.EThOS Persistent ID
uk.bl.ethos.587884Language
- en