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

arXiv: https://arxiv.org/abs/2311.08410 · HTML: https://mlsanity.com/paper/2311.08410/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [A Large-Scale Car Dataset for Fine-Grained Categorization and Verification](https://mlsanity.com/paper/1506.08959/) (2015)
- [Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm](https://mlsanity.com/paper/2005.04166/) (2020)
- [Vision-Based Human Pose Estimation via Deep Learning: A Survey](https://mlsanity.com/paper/2308.13872/) (2023)
- [A Semantic Web Technology Index](https://mlsanity.com/paper/2201.07034/) (2022)
- [Class Binarization to NeuroEvolution for Multiclass Classification](https://mlsanity.com/paper/2308.13876/) (2023)

## Built on by (most cited first, 2 of 2)

- [Automatic parking planning control method based on improved A* algorithm](https://mlsanity.com/paper/2406.15429/) (2024)
- [Automated Parking Planning with Vision-Based BEV Approach](https://mlsanity.com/paper/2406.15430/) (2024)

## Abstract (arXiv)

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.