###### Abstract  
To address the challenges of inefficient intelligent parking performance and reduced efficiency in complex environments, this study presents an end-to-end intelligent parking control model based on a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture incorporating multi-source sensory information fusion to improve decision-making and adaptability. The model can produce real-time intelligent parking control decisions by extracting spatiotemporal features, including comprehensive 360-degree panoramic images and ultrasonic sensor distance measurements. To enhance the coverage of real-world environments in the dataset, a data collection platform was developed, leveraging the PreScan simulation platform in conjunction with actual parking environments. Consequently, a comprehensive parking environment dataset comprising various types was constructed. A deep learning model was devised to ameliorate horizontal and vertical control in intelligent parking systems, integrating Convolutional Neural Networks and Long Short-Term Memory in a parallel configuration. By meticulously accounting for parking environment characteristics, sliding window parameters were optimized, and transfer learning was employed for secondary model training to fortify the prediction accuracy. To ascertain the system's robustness, simulation tests were performed. The ultimate results from the actual environment experiment revealed that the end-to-end intelligent parking model substantially surpassed the existing approaches, bolstering parking efficiency and effectiveness in complex contexts. intelligent parking model; end-to-end deep learning; multi-source sensory information fusion; parking efficiency; complex environments +
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method to design online paths that combine circular arcs and straight lines, resulting in a fast computational speed. However, the method requires highly flexible vehicles and can be challenging to control in confined spaces. [PERSON] et al. [6] proposed to design suitable and collision-free trajectories specifically for parking in tight spaces, and this method can continuously optimize the paths to achieve parking planning. However, when the parking space is small, the method is computationally intensive, which leads to a time-consuming and tedious planning process. The literature [7] summarizes various parking path-tracking control methods, such as the sliding film control. Still, it is easy to park failure in the more complex environment of the slot, and it cannot draw on the experience of skilled drivers. Artificial intelligence methods make the controller simulate the driver's operation behavior in the parking process through intelligent algorithms [8], and domestic and foreign scholars have carried out research on intelligent parking path planning and tracking control through intelligent algorithms, such as ant colony algorithm, reinforcement learning, and fuzzy control, respectively [9; 10; 11; 12]. The literature [13] uses an ant colony algorithm that reasonably uses the pheromone heuristic and expectation factors, which improves the pheromone volatility factor. This algorithm has the characteristics of positive feedback, strong robustness, and better adaptability. Still, this algorithm's convergence speed is slow, and the obtained paths are more tortuous and do not easily satisfy the vehicle's kinematic constraints. The reinforcement learning (RL) algorithm in machine learning is also a path planning method in which an intelligent body is trained to explore the possible paths in the environment in a trial-and-error manner to learn without a large amount of driver experience data, and the reward or penalty of the reward function is used to judge the good or bad state of the intelligent body in the environment to obtain the corresponding target path [14].