Exploration of the Assessment for AVP Algorithm Training in Underground Parking Garages Simulation Scenario

Abdullah's study, as described in [1], compared the space Simulation test scenarios are an important part of helping utilization efficiency of diagonal, parallel, and perpendicular autonomous driving algorithms improve, but current simulation parking methods. The research findings concluded that scenarios are still limited to manual approaches. The perpendicular parking methods yield the highest number of ultimate goal of this project is to generate Autonomous parking spaces. This conclusion was drawn using a university Valet Parking (AVP) simulation test scenarios in underground as a specific example. The study summarized in [2] focuses garages with differentiated difficulty levels through reinforcement on smart parking solutions, emphasizing their significance learning, which will challenge the vehicle-side AVP in the context of urban growth and traffic congestion. This algorithms and ultimately improve the algorithmic test metrics.