Exploration of the Assessment for AVP Algorithm Training in Underground Parking Garages Simulation Scenario
This addresses the need for efficient training of AVP algorithms in complex environments, but it is incremental as it builds on existing simulation methods.
The paper tackles the problem of training autonomous valet parking (AVP) algorithms for complex scenarios by introducing an approach to automatically generate 3D underground garage simulation scenarios from 2D plans, reducing the time and labor required compared to manual construction.
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.