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Scientific progress is an ongoing process and humans always have new ideas for new inventions. Although, scientific process is cumulative, you must invent the tire before inventing the car. Currently, mankind have past that point and looking towards to new challenges. One of the new ideas is cars that drive themselves, in a more formal and general way, the 'autonomous ground vehicles'. Autonomous vehicles in city traffic have been a dream for a long time. However, this great idea comes with its own set of problems. There are many parts of autonomous driving such as scene understanding, path planning. Many methods have been studied and developed to solve its problems. In this thesis, a novel path planning algorithm and results of extensive experimentation on it are presented. The algorithm runs real-time and is an alternative to well known existing algorithms. The experiments are done in a scalable custom environment consisting of a custom-built vehicle, desktop computer, a camera and an open source marker library.