Last decades the autonomous driving research field has shown exponential growth. The social benefits, which include increased safety, mobility and productivity, are the main factor that drive this growth. One of the most difficult problems that vehicle engineers must solve to develop autonomous vehicles is the motion planning problem. They must solve the motion planning problem for environments ranging from unstructured to structured, such as parallel parking up to high-speed highway driving. Current literature presents many implementations that solve either the structured or unstructured planning environment or a small range of environments. Yet, the generic implementation of a single motion planning method, that can plan in the full range of environments, is still an open question. The aim of this thesis is to address the identified gap in the literature, by realizing a real-time implementation of a single motion planning method, that shows human-like and safe driving behavior, and can deal with any environment it encounters.
In this thesis, a method is proposed that solves the planning problem by enhancing the Closed-Loop Rapidly-exploring Random Tree (CL-RRT) algorithm for planning on curved structured roads. The planner is aware of the road curvature and deforms the motion plan, so it follows the shape of the road. Extensive simulations have demonstrated that the proposed method can improve the path quality on curved highway roads when compared with the standard RRT and CL-RRT. Although the method can plan in any environment it encounters, it demonstrated limitations in its capability of dealing with complex dynamic environments.