Autonomous Car Parking System through a Cooperative Vehicular Positioning Network

Area of Science:

• Computer Science
• Electrical Engineering
• Transportation Systems

Background:

• The automotive industry is rapidly advancing towards fully autonomous vehicles, necessitating new services within Vehicular Sensor Networks (VSNs).
• Autonomous cars act as mobile nodes in VSNs, exchanging sensor and state information.
• Intelligent parking management is crucial for integrating autonomous cars with traditional vehicles, leveraging their advanced capabilities.

Purpose of the Study:

• To design a novel smart parking system for VSNs that accommodates heterogeneous vehicle types (autonomous and traditional).
• To provide optimal parking guidance for autonomous cars through a collaborative approach.
• To maximize the common good of all vehicles by improving the accessibility rate of free parking spots.

Main Methods:

• Development of a smart parking system architecture integrated with VSNs.
• Implementation of a collaborative approach to determine the best parking spot based on accessibility rate.
• Simulation of a real-world parking lot scenario to evaluate system performance.

Main Results:

• The proposed system effectively guides autonomous cars to suitable parking locations.
• Performance evaluation shows the system's efficiency is close to optimal under various communication ranges and autonomous car penetration rates.
• The collaborative approach successfully balances the needs of both autonomous and traditional vehicles.

Conclusions:

• The designed smart parking system offers a viable solution for managing heterogeneous traffic in intelligent parking environments.
• The system enhances parking efficiency and accessibility for autonomous vehicles within VSNs.
• Future work can further refine the collaborative algorithms for even greater optimization.