Author

Sara Campbell

Advisor

Smith, Brian K.

Committee Member

Marufuzzaman, Mohammad.

Committee Member

Garrison, Teena.

Date of Degree

1-1-2018

Document Type

Graduate Thesis - Open Access

Major

Industrial and Systems Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

Driving simulators are a main way researchers gather data about on-the-road behavior. However, the quantity of data produced by these simulators forces researchers to rely on algorithms to aid in cleaning and analyzing the data. One example of this is defining whether the vehicle is making a lane change or turning a corner by broadly categorizing the angle of the steering wheel. A more precise method of identifying these driving maneuvers is described. This method involves using self-organizing maps to consider multiple aspects of user input when determining the existence of a lane change or turn. The results show that while steering angle is the most relevant variable to consider, other variables such as throttle pressure can be used to improve the accuracy of the categorization. This indicates a need for further study into the automatic classification of driving simulator data.

URI

https://hdl.handle.net/11668/16558

Share

COinS