Automated Generation of Parking Data Sets for Underground Car Parks

The advancement of autonomous driving perception frequently necessitates the aggregation of data, its subsequent annotation, the implementation of training procedures, and other related activities. In contrast, the utilisation of synthetic data obviates the necessity for data collection, annotation, and the generation of accurate and reliable labels. Its incorporation into the development process is anticipated to streamline the entire algorithmic development process. In this study, we propose a novel approach utilising the Blender software to create a virtual representation of an underground car park and develop an automated parking dataset. The utilisation of virtual simulation technology enables the generation of diverse and high-quality training data, thereby addressing the challenge of acquiring data in the actual scene. The experimental results demonstrate that the model trained based on the synthetic dataset exhibits superior performance in the automatic parking task, thereby substantiating the efficacy and practicality of the proposed approach. Furthermore, previous research on synthetic data has often concentrated on the creation of the final perceptual algorithm dataset, without providing access to the material and method used to generate the synthetic data. This study therefore makes the underground car park scene library files available under an open-source licence, in the hope that subsequent developers will be able to generate the required datasets based on these materials with greater freedom.The open-source scene library, code and dataset provided in this paper are as follows: https://github.com/Snyard/parkinglot-generator.