Quantification of human operator skill in a driving simulator for applications in human adaptive mechatronics

Nowadays, 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 ii 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