## 2 End-to-End Intelligent Parking Model Architecture
### CNN-LSTM Neural Network Action Layers  
The CNN-LSTM neural network model constructed in this paper mainly consists of a convolutional layer, a pooling layer, an LSTM layer, a feature fusion layer, an activation layer, a fully connected layer, and a dropout layer. The convolutional layer is used to extract image features, and the pooling layer is used to effectively reduce the size of the node matrix, reduce the number of feature dimensions, and compress the number of data and parameters. The function of the LSTM layer is to identify and capture the relationships between the temporal features of the vehicle control data sequence and the ultrasonic sensor data sequence. The role of the feature fusion layer is mainly to fuse two or more feature vectors and generate new semantic features. The primary purpose of the activation layer is to enhance the representation and learning ability of the neural network through nonlinear transformations. 1. Spatial feature extraction sub-network: a convolutional layer and its network structure are shown in Table 1. The feature extraction network uses a Convolutional Neural Network to extract the high-level semantic features in the input single-frame surround-view image, including features such as the slot line, obstacles, and the location information of the intelligent parking vehicle and the surrounding environment. The input image size of the spatial feature extraction sub-network is 224 \(\times\) 224 \(\times\) 3, and the Convolutional Neural Network used is ResNet50. The initial convolutional layer employs 64 kernels, each with a 7 \(\times\) 7 size and a stride of two. The output size is 112 \(\times\) 112, followed by a 3 \(\times\) 3 max pooling operation with a stride of two. This is succeeded by four distinct residual modules, each from ResNet50, which extract  
Figure 1: The architecture of CNN-LSTM intelligent parking algorithm. features and generate a 1000-dimensional feature vector. A three-layer fully connected layer reduces this to 128 dimensions before feeding it into the multi-task prediction sub-network. 2. Temporal feature extraction sub-network: vehicle control commands can be generated directly from the camera information via CNNs, but their mapping process does not exactly match the whole process of a human driving a vehicle, i.e., the process of a human driving a vehicle not only needs to make a driving intention judgment based on the forward view observation but it also needs to consider the historical state of the car. The driving decision of intelligent parking is continuous in the time dimension, so it is necessary to introduce recurrent neural networks. Recurrent neural networks have powerful information mining and deep characterization capabilities [21; 22]. The temporal feature extraction sub-network can capture the temporal dependencies existing between the steering wheel angle data; the value of w represents the number of discrete time steps considered, which allows the sub-network to effectively analyze and process the time-varying relationships among the vehicle speed, steering wheel angle, and ultrasonic distances after first going through one LSTM layer with 128 dimensions and then further feature extraction through two fully connected layers. The outputs are all 128-dimensional time series features for the multi-task prediction sub-network and the feature extraction sub-network. 3. Multi-task prediction sub-network: in end-to-end intelligent driving control decision-making methods, the steering wheel angle and vehicle speed control decisions belong to multi-task learning. Multi-task learning belongs to the recommendation task, which improves the generalization ability by sharing representation information among related tasks to enable the network to better generalize the initial task [23]. The vehicle speed and steering angle prediction information is output by fusing the spatial and temporal features to achieve horizontal and vertical vehicle control. The proposed multi-task prediction sub-network is shown in Figure 2.