Graduation Year

2013

Document Type

Thesis

Degree

M.S.Cp.

Degree Granting Department

Computer Science and Engineering

Major Professor

Srinivas Katkoori

Co-Major Professor

Achilleas Kourtelli

Abstract

According to Motorcycle Industrial Council (MIC), in USA the number of owned

motorcycle increased during last few years and most likely will keep increasing. However, the

number of the deadly crash accidents associated with motorcycles is on the rise. Although MIC

doesn't explain why the accident rate has increased, the unprotected motorcyclist gear can be one

of the reasons. The most recent National Highway Traffic Safety Administration (NHTSA)

annual report stated that its data analyses are based on their experiences and the best judgment is

not based on solid scientific experiment [3]. Thus, building a framework for the data acquisition

about the motorcyclist environment is a first step towards decreasing motorcyclist crashes.

There are a few naturalistic motorcycle studies reported in the literature. The naturalistic

motorcycle study also identifies the behaviors and environmental crash hazards. The primary

objective of this thesis work is to design a highly portable data logging embedded system for

naturalistic motorcycle study with capability of collecting many types of data such as images,

speed, acceleration, time, location, distance approximation, etc. This thesis work is the first

phase (of three phases) of a naturalistic motorcycle study project. The second phase is to

optimize system area, form factor, and power consumption. The third phase will be concerned

with aggressive low power design and energy harvesting. The proposed embedded system design

is based on an Arduino microcontroller. A whole suite of Arduino based prototype boards,

sensor boards, support software, and user forum is available. The system is high portable with

capability to store up to eight (8) hours of text/image data during a one month study period. We

have successfully designed and implemented the system and performed three trial runs. The data

acquired has been validated and found to be accurate.

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