Road Pothole Detection Method Using Built-in Sensors In Smartphone

In this thesis, the smartphone is installed on vehicle as a tool to detect potholes on the road. The data collected and pre-processed by smartphone is then analysed to obtain the result. The history and status of pothole detection study is firstly discussed. The traditional pothole detection method has two disadvantages: 1, low efficiency; 2, limited detection area. To work out a new method, the suspension models and tyre models are studied. Also the axis-correction and de-noise in signal process discussed for data analysis. Based on these, the requirement of detection system is analysed, and the features of embedded systems are discussed, which leads to the conclusion that smartphone is the best hardware for pothole detection. Then the smartphone (3 mobiles are chosen: Samsung Note 1, Nexus 7 and iPhone 5) is fixed on vehicle (Nissan Micra K11 and Saab 93) as experiment platform. The Tay Road Bridge is chosen as experiment road. The software Sensor Insider Pro and Sensor Data are chosen to collect data. Simulations about vehicle and tyre are done by MATLAB Simulink. From the simulation the relationship between the bouncing height and the speed of vehicle is obtained, and is used as threshold in pothole detection. The experimental data from acceleration sensor and gyroscope is processed by MATLAB using axis-correction and wavelet transform. Together with velocity data gathered by GPS and threshold (calculated from speed), the position of pothole is obtained. Comparing with road, the detection has good accuracy, which proves the feasibility of this pothole detection method. At the end of thesis, other potential field of application of smartphone is discussed.

Name dc:type.qualificationname

Doctor of Philosophy

Level dc:type.qualificationlevel

Doctoral Thesis

Grantor dc:publisher.institution

University of Dundee

Year dc:date.issued

2017

Author dc:creator

• Zhang, Dalong

Language dc:language

eng

Identifier

oai:discovery.dundee.ac.uk:studenttheses/ae02e999-acbc-41fc-a092-7bd88d362f20

OAI identifier oai:identifier

oai:discovery.dundee.ac.uk:studenttheses/ae02e999-acbc-41fc-a092-7bd88d362f20