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The autonomous valet parking (AVP) functionality in self-driving vehicles iscurrently capable of handling most simple parking tasks. However, furthertraining is necessary to enable the AVP algorithm to adapt to complex scenariosand complete parking tasks in any given situation. Training algorithms withreal-world data is time-consuming and labour-intensive, and the current stateof constructing simulation environments is predominantly manual. This paperintroduces an approach to automatically generate 3D underground garagesimulation scenarios of varying difficulty levels based on pre-input 2Dunderground parking structure plans.

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