Date of Award:

5-2018

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Advisor/Chair:

Vladimir Kulyukin

Co-Advisor/Chair:

Nicholas Flann

Third Advisor:

Xiaojun Qi

Abstract

A self-driven car is a vehicle that can drive without human intervention by making correct decisions based on the environmental conditions. Since the innovation is in its beginning periods, totally moving beyond the human inclusion is still a long shot. However, rapid technological advancements are being made towards the safety of the driver and the passengers. One such safety feature is a Lane Detection System that empowers vehicle to detect road lane lines in various climate conditions.

This research provides a feasible and economical solution to detect the road lane lines while driving in a sunny, rainy, or snowy weather condition. An algorithm is designed to perform real time road lane line detection on a low voltage computer that can be easily powered in a regular auto vehicle.

The algorithm runs on a RaspberryPi computer placed inside the car. A camera, attached to the vehicle’s windshield, captures the real time images and passes them to the RaspberryPi for processing. The algorithm processes each frame and determines the lane lines. The detected lane lines can be viewed on a 7 inch display screen connected to the Raspberry Pi. The entire system is mounted inside a Jeep Wrangler to conduct the experiments and is powered by the vehicle’s standard charger of 12V-15V power supply. The algorithm provides approximately 97% accurate detection of road lane lines in all weather conditions.

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