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