Thesis (MEng)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Road accidents are a growing concern for governments and is rising to become one of the
leading causes of death in developing countries. Aggressive driving is one of the major
causes of road accidents, and it is therefore important to investigate ways to improve
people's driving habits. The ubiquitous presence of smartphones provides a new platform
on which to implement sensor networks in vehicles, and therefore this thesis focuses
on the use of smartphones to monitor a person's driving behaviour. The framework for
a smartphone-based system that can detect and classify various driving manoeuvres is
researched. As a proof of concept, a system is developed that specifically detects lateral
driving manoeuvres and that classifies them as aggressive or not, using a supervised
learning classification algorithm. Existing solutions found in research literature are investigated
and presented. The best existing solution, a dynamic time warping classification
approach, is also implemented and tested. We use an aggressive driving model that is
based on the angle of a turn, the lateral force exerted on the vehicle and its speed through
the turn. The tests and results of the implemented manoeuvre detection and classifcation
algorithms are presented, and thoroughly discussed. The performance of each classifer is
tested using the same data set, and a quantitative comparison are made between them.
Ultimately, a lateral driving manoeuvre detection and recognition system was successfully
developed, and its potential to be implemented on a smartphone was substantiated. The
suitability of supervised learning classi ers for classifying aggressive driving, in comparison
to dynamic time warping classifcation, was successfully demonstrated and used to
validate our aggressive driving model. Conceivably, this work can be employed in the future
to develop an holistic smartphone-based driver behaviour monitoring system, which
can be easily deployed on a large scale to help make the public drive better. This would
make our roads safer, reducing the occurrence of road accidents and fatalities. / AFRIKAANSE OPSOMMING: Padongelukkige is 'n groeiende bekommernis vir regerings en is een van die hoof oorsake
van sterftes in ontwikkelende lande. Aggressiewe bestuur is een van die grootste oorsake
van padongelukke, en dit is dus belangrik om ondersoek in te stel oor hoe mense se
bestuurgewoontes verbeter kan word. Die alomteenwoordigheid van slimfone bied 'n nuwe
platform waarop sensor netwerke geïmplementeer kan word in voertuie. Daarom fokus
hierdie tesis op die gebruik van slimfone om 'n persoon se bestuurgedrag te moniteer. Die
raamwerk vir 'n slimfoon-gebaseerde stelsel wat verskeie bestuurbewegings kan opspoor
en klassifiseer is nagevors. As 'n bewys van die konsep, is 'n stelsel ontwikkel wat spesifiek
laterale bestuurbewegings opspoor en dan klassifiseer of dit aggressief is of nie, met behulp
van 'n klassifikasie algoritme wat onder toesig geleer is. Bestaande oplossings gevind
in navorsingsliteratuur word ondersoek en aangebied. Die beste bestaande oplossing,
'n dinamiese tyd buiging klassifikasie benadering, word ook geïmplementeer en getoets.
Ons gebruik 'n aggressiewe bestuurmodel wat gebaseer is op die hoek van 'n draai, die
laterale krag wat uitgeofen is op die voertuig en sy spoed deur die draai. Die toetse
en die resultate van die geïmplementeer beweging opsporing en klassifisering algoritmes
word aangebied, en deeglik bespreek. Die prestasie van elke klassifiseerder is getoets met
behulp van dieselfde stel data, en 'n kwantitatiewe vergelyking is tussen beide gemaak.
Oplaas is 'n laterale bestuurbeweging bemerking en herkenning stelsel suksesvol ontwikkel
en sy potensiaal om geïmplementeer te word op 'n slimfoon is gestaaf. Die geskiktheid
van die onder-toesig-geleerde klassifiseerders vir die klassifikasie van aggressiewe bestuur,
in vergelyking met dinamiese tyd buiging klassifikasie, was suksesvol gedemonstreer en
gebruik om ons aggressiewe bestuurmodel te bewys. Hierdie werk kan in die toekoms
gebruik word in 'n holistiese slimfoon-gebaseerde bestuurdergedrag monitering stelsel,
wat maklik op groot skaal ontplooi kan word om te help verseker dat die publiek beter
bestuur. Dit sal ons paaie veiliger maak, en die voorkoms van padongelukke en sterftes
verminder.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/96597 |
| Date | 03 1900 |
| Creators | Engelbrecht, Jarrett |
| Contributors | Booysen, M. J., Van Rooyen, G-J., Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 69 pages : illustrations |
| Rights | Stellenbosch University |
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