## 3 Dataset Collection
### Actual Car Training Data Collection  
This paper needs to use the actual parking environment data to improve the generalization of the end-to-end intelligent parking model to perform transfer learning retraining on the completed pre-trained model. The acquisition process is shown in Figure 6. In the process of parking data acquisition, the parking video data of the front, rear, and left side of the car are collected in real-time by four fisheye cameras that are installed in the front hood, trunk, and left and right rearview mirrors of the vehicle, respectively, which are collected at a frequency of 20 fps, followed by the use of a script to synthesize and split the surrounding video into a single frame surround-view picture, while using CANoe to record the  
Figure 4: (**a**) Simulated parking environment for capturing intelligent parking driving models. (**b**) Driving simulator. Figure 5: Starting area in the simulated parallel parking slot. steering wheel turn angle, car speed, and ultrasonic radar data through CANalyzer. The CANalyzer 7.5 records the steering wheel angle, car speed, and ultrasonic radar data to ensure that the surround-view image data can correspond with CANoe data one by one. Hence, the CANoe data collection frequency is also 20 Hz; after that, the software generates CAN messages, parses them, and finally collects the synthesized surround-view frame images and CANoe collected data, and uses EXCEL to make dataset. However, when making the actual parking environment dataset, it is necessary to consider the temporal and spatial synchronization of the surround-view parking image sequence and the car control data sequence, and two synchronization methods are chosen in this paper:  
1. To ensure that the length of the sequence of the surround-view images captured by the fisheye camera and the data captured by CANoe are consistent, this paper proposes a comparison of the displacement data of both to achieve sequence length consistency. A 5 m straight line is designed in the actual environment to accomplish this, with ground markings at the start and endpoint. The surround-view system records the ground markings before each parking data acquisition, and the displacement data are obtained using CANoe. 2. To ensure the proper alignment of the starting time for data interception, this paper proposes an initiation of the interception process at the beginning of the movement detected in the surround-view image and simultaneously at the commencement of the speed and displacement data captured by CANoe. This approach helps to synchronize and maintain the consistency of the data sequences from both sources, resulting in a more coherent and seamless integration. In this paper, various datasets of actual parking environments are collected, and an example of the surround-view image in the dataset is shown in Figure 7. The dataset collection environment is mainly selected in the parking environment with obstacles and parking space lines, which ensures the complexity of the dataset collection environment  
Figure 6: Actual car dataset collection process. and thus improves the generalization of the training model. Likewise, this paper also takes into account the impact of various weather conditions on parking performance while collecting the data. Diverse weather conditions, such as sunny, cloudy, rainy, and snowy, can affect visibility and road conditions, resulting in a slow model response and even failure to recognize parking space lines, which poses more challenges for the parking system. By gathering data in these varying environments, this study aims to create a more robust and adaptable end-to-end intelligent parking model that is capable of handling a wide range of actual parking environments. The actual car parking dataset contains parallel parking on the left and right side, strong and low light conditions, and obstacles of different shapes in the front and back, whether the road is inclined, and whether the parking line is fuzzy.