In order to improve the efficiency and quality of parking path planning, a study on intelligent driving parking path planning algorithms has been conducted. By reviewing the parking planning methods from recent domestic and international academic research, based on their characteristics the parking path planning algorithms are categorized into 5 types: graph search algorithms, sampling-based algorithms, intelligent algorithms, curve interpolation algorithms, and optimal control algorithms. The advantages and disadvantages of these 5 types of algorithms are analyzed, and the application of fusion algorithms in specific environments is explored. The rationality and effectiveness of existing solutions are evaluated in terms of planning efficiency and path curvature. Furthermore, future development trends in parking path planning algorithms are discussed. The study concludes the following: (1) Graph search algorithms offer the advantages of globally optimal paths and good real-time performance but suffer from poor path continuity and high complexity in high-dimensional spaces. (2) Sampling-based algorithms have the advantages of probabilistic completeness and high search efficiency in high-dimensional spaces but have large memory consumption, significant randomness, and cannot guarantee path curvature. (3) Intelligent algorithms have strong learning capabilities based on samples and strong iteration capabilities but have high training costs, poor dynamic adaptability, and poor real-time performance. (4) Curve interpolation algorithms based on optimization are easy to calculate but cannot guarantee the continuity of curvature. (5) Optimal control algorithms based on optimization can handle complex optimization problems but cannot guarantee timeliness and are prone to falling into local minima. Fusion algorithms through complementary advantages can better adapt to vehicle constraints and environmental constraints, achieving efficient and safe parking path planning.