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|Title: ||Intelligent automotive safety systems: the third age challenge|
|Authors: ||Amin, Imran|
|Keywords: ||Vehicle safety|
Artificial neural network
|Issue Date: ||2006|
|Publisher: ||© Imran Amin|
|Abstract: ||Over 300,000 individual are injured every year by vehicle related accident in the United Kingdom
alone. Government and the vehicle manufacturers are not only bringing new legislation but are also
investing in vehicle safety research to bring this figure down.
A private self-driven car is an important factor in maintaining the independence and quality of life of
the third age individuals. However, since older people brings deterioration of cognitive, physical and
visual abilities, resulting in slower reaction times and lapses while driving. The third age individuals
are involved in more vehicle related accidents than middle aged individuals. This scenario is corrected
by the fact that the number of third age individuals is increasing, especially in developed countries. It is
expected that the percentage of third age individuals in the United Kingdom will increase to 20% of the
total population by 2010.
Several safety systems have been developed by the automotive industry including intelligent airbags,
Electronic Stability Control (ESC) and pre-tensioned seat belts, but nothing has been specifically
developed for the third age related problems.
This thesis proposes a driver posture identification system using low resolution infrared imaging. The
use of a low resolution thermal imager provides a reliable non-contact based posture identification
system at a relatively low cost and is shown to provide robust performance over a wide range of
conditions. The low resolution also protects the privacy of the driver.
In order to develop the proposed safety system an Artificial Intelligent Thermal Imaging algorithm
(AITl) is created in MatLAB. Experimentation is conducted in real and simulated environment, with
human subjects, to evaluate the results of the algorithm.
The result shows that the safety system is able to identify eighteen different driving postures. The
system also provides other valuable information about the driver such as driver physical built, fatigue,
smoking, mobile phone usage, eye-height and trunk stability. It is clear that in incorporating this safety
system in the overall automotive central strategy, better safety for third age individual can be achieved.
This thesis provides various contributions to knowledge including a novel neural network design, a
safety system using low resolution infrared imager and an algorithm that can identify driver posture.|
|Description: ||A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.|
|Appears in Collections:||PhD Theses (Mechanical, Electrical and Manufacturing Engineering)|
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