In order to alleviate the problems of short supply of parking spaces and traffic congestion, intelligent driving solutions have emerged. Automatic parking has now become the first application scenario for driverless driving due to the more fixed scenario and lower traveling speed. In this study, the traditional A* algorithm is improved using the cost function, and the hybrid algorithm of parking space path search and planning is designed by combining the improved A* algorithm with the Reeds-Shepp curve, and then combined with the collision constraints to improve the algorithm’s path planning performance. The results of simulation experiments and in-loop test experiments show that the maximum lateral error and heading error are low in parallel and perpendicular parking scenarios, and it is found that the average lateral error during the whole parking process is only 0.177m in the in loop test, which is a good tracking effect for vehicles. The path search and planning algorithm designed in this paper can better realize the autonomous parking function and has high tracking accuracy and stability in the simulation scenario.