Utilizing AI for Real-Time Autonomous Parking Systems: Developing Deep Learning Models for Parking Space Detection, Path Planning, and Parking Maneuver Execution in Smart Cities

Abstract

The integration of artificial intelligence (AI) into real-time autonomous parking systems represents a significant advancement in smart city infrastructure, addressing the growing need for efficient urban mobility solutions. This paper explores the development and implementation of deep learning models designed to enhance the functionality and performance of autonomous parking systems. The primary focus is on three core components: parking space detection, path planning, and parking maneuver execution. Each component is crucial for achieving seamless and reliable autonomous parking in diverse environments, including on-street, multi-level, and underground parking facilities.

Parking space detection involves the use of advanced deep learning techniques to identify and classify available parking spaces in real-time. Convolutional neural networks (CNNs) and other state-of-the-art object detection algorithms are employed to analyze visual data captured by cameras installed in vehicles or surrounding infrastructure. The paper discusses various approaches for enhancing detection accuracy and robustness, including the use of synthetic data augmentation, transfer learning, and multi-sensor fusion. These methods aim to overcome challenges such as varying lighting conditions, occlusions, and diverse parking environments.

Path planning is another critical aspect addressed in this research. The paper examines the application of deep reinforcement learning (DRL) and other algorithmic approaches to develop efficient path planning strategies. These strategies are designed to optimize the trajectory of autonomous vehicles as they navigate from the point of entry to the designated parking space. Emphasis is placed on developing algorithms that can adapt to dynamic and complex environments, ensuring that the planned paths are not only optimal in terms of distance and time but also safe and compliant with traffic regulations.

The execution of parking maneuvers involves translating the planned paths into precise vehicle control actions. This paper explores the integration of control algorithms with deep learning models to achieve accurate and smooth parking maneuvers. Techniques such as model predictive control (MPC) and end-to-end learning approaches are discussed, highlighting their effectiveness in handling various parking scenarios, including parallel parking, perpendicular parking, and tight parking spots. The challenges associated with real-time control and the need for high-resolution feedback systems are also addressed.

In addition to these technical components, the paper considers the broader implications of implementing AI-driven autonomous parking systems in smart cities. It examines the potential benefits, such as reduced traffic congestion, improved parking efficiency, and enhanced safety for both drivers and pedestrians. The research also addresses potential challenges and limitations, including the need for robust sensor calibration, the integration of AI systems with existing infrastructure, and the ethical considerations related to data privacy and security.

The findings presented in this paper contribute to the growing body of knowledge in the field of autonomous driving and smart city technologies. By leveraging deep learning models for real-time parking space detection, path planning, and maneuver execution, this research aims to advance the state of the art in autonomous parking systems, providing a foundation for future innovations and deployments in urban environments.