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Lane detection system for autonomous vehicle using image processing techniques

By Shahizul Eza Mohd Kiblee


A completely autonomous vehicle is one in which a computer performs all the tasks that the human driver normally would. This would mean, to go to a specific destination, a driver will just has to key in the desired destination and the system will be enabled automatically by the computer. From there, the car would take over and drive to destination with no human input. The car would be able to sense its environment and change maneuver and speed when necessary. A system for road marking detection has been set up during the course of this master's thesis project. In the development of the software, images acquired from a front looking video camera mounted inside the vehicle were used. The problem of using computer vision to develop lane detection system for autonomous vehicle is road marking characteristic. Since the strongest characteristic of a road marking image are the edges, the road marking detection step is based on edge detection. For the detection of the straight edge lines, a Radon based method was chosen. Due to peak spreading in Radon space, the difficulty of detecting the correct peak in Radon space was encountered. A Radon peak detection algorithm was developed based on two values, Rand O. These values make the system robust to the different types of road marking such as continuous road marking, discontinuous road marking and road with shadow. The performance of the road marking detection algorithm was investigated over several different short image sequences. The different sequences included normal countly road driving, a number of different road marking configurations, such as continuous, intermittent and combinations of and images with shadows. The system performs well during the experiments within the difference road condition state above. The work done in this thesis can be used as a starting point in the development of for example a lane departure warning system. The potential of such a system is further increased by merging information retrieved from images with information from the vehicle such as vehicle speed, steering angle and acceleration

Topics: TL Motor vehicles. Aeronautics. Astronautics
Year: 2005
OAI identifier:

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