  As the trend of moving away from high-precision maps gradually emerges in the
autonomous driving industry,traditional planning algorithms are gradually
exposing some problems. To address the high real-time, high precision, and high
trajectory quality requirements posed by the automatic parking task under
real-time perceived local maps,this paper proposes an improved automatic
parking planning algorithm based on the A* algorithm, and uses Model Predictive
Control (MPC) as the control module for automatic parking.The algorithm
enhances the planning real-time performance by optimizing heuristic functions,
binary heap optimization, and bidirectional search; it calculates the
passability of narrow areas by dynamically loading obstacles and introduces the
vehicle's own volume during planning; it improves trajectory quality by using
neighborhood expansion and Bezier curve optimization methods to meet the high
trajectory quality requirements of the parking task. After obtaining the output
results of the planning algorithm, a loss function is designed according to the
characteristics of the automatic parking task under local maps, and the MPC
algorithm is used to output control commands to drive the car along the planned
trajectory. This paper uses the perception results of real driving environments
converted into maps as planning inputs to conduct simulation tests and ablation
experiments on the algorithm. Experimental results show that the improved
algorithm proposed in this paper can effectively meet the special requirements
of automatic parking under local maps and complete the automatic parking
planning and control tasks.
