###### Abstract
[PERSON] [15] built a fuzzy inferential system that can be used for parallel parking by organizing the experimental data of parking and analyzing the driver parking data with the help of adaptive neural networks, but designing a fuzzy controller still requires more driver parking data, and it is time-consuming to screen the valid data manually. In addition, there are other shortcomings of fuzzy control for parking trajectory planning: the initial area is unclear, the control accuracy is not high, and the design of rules is tedious and complicated. In summary, the existing intelligent parking algorithms still have many shortcomings, which limit the effectiveness of these methods in practical applications and provide directions for future research to improve and optimize. In recent years, the integration of artificial intelligence and intelligent driving has been deepening, and end-to-end deep learning methods have received much attention in intelligent driving. The end-to-end deep learning-based intelligent driving network model exhibits the following features: it operates in an \"environment perception-control execution\" mode for vehicle motion planning and control, utilizes supervised learning techniques to learn human driving actions and behaviors, and directly maps sensor data to control variables such as an accelerator pedal, steering wheel, and the brake pedal. This approach effectively minimizes system complexity and enhances the actual time performance [16]. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] proposed Convolutional Neural Networks (CNNs) for processing image data to achieve autonomous driving [17]. [PERSON] et al. further proposed a network architecture of Fully Convolutional Network-Long Short-Term Memory (FCN-LSTM) to enhance the model's understanding of environments and achieve the prediction of discrete or continuous vehicle actions [18]. [PERSON] et al. End2 End learning used a surround-view camera [19]. [PERSON] et al. proposed a CNN-LSTM-based car tracking model, which is able to car tracking process of the potential relationship between the car in front and the car behind, and compared with the classic car following models (LSTM model, intelligent driver model), and proved that the accuracy and the ability to learn heterogeneity of the proposed model are better than the other two models [20]. The end-to-end deep learning model directly maps input data to control commands, streamlining the process by avoiding division into multiple sub-tasks. This simplified structure enhances the system's robustness. Deep learning enables robust feature extraction and representation learning, eliminating the need for manual feature engineering and reducing implementation complexity. As a data-driven approach, the model automatically learns control strategies, minimizing human influence and handling complex environments effectively. With its portability across various vehicle types and sensor configurations, the end-to-end model boasts excellent adaptability for diverse applications. However, most of the current research on end-to-end-based intelligent driving control methods is aimed at regular road environments. Its application in intelligent parking environments faces difficulties in many aspects, so the related research and applications have not been reported. The reasons for this are mainly reflected in the following aspects:  
1. The parking environment is more complex than the regular road environment. There are narrow parking spaces and irregular obstacles, so parking requires higher accuracy for close-range obstacle perception. The LIDAR and millimeter wave radar sensors currently that are used in intelligent road driving are unsuitable for close vehicle object detection. Ultrasonic radar, which is widely used in intelligent parking environments, has a strong close object sensing capability. Still, its directionality is poor, and it is not easy to meet the input requirements of end-to-end intelligent driving models. 2.