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In the rapidly evolving landscape of autonomous vehicle technologies and urban planning, the development of precise motion planning algorithms for autonomous parking has emerged as a critical research endeavor. This paper undertakes a comprehensive exploration of this vital area, with a specific focus on low-speed parking maneuvers. It meticulously analyzes and implements three prominent motion planning algorithms—Hybrid A-star, Reeds and Shepp, and Rapidly-exploring random tree star (RRT)—in known environments. Through a systematic evaluation conducted via simulations, this research assesses the distinct strengths and limitations of each algorithm across a spectrum of parking scenarios. Hybrid A-star's capacity to navigate intricate situations replete with obstacles, the proficiency of Reeds and Shepp in obstacle-free environments, and the efficient parking possibility exploration by RRT are …