Complex & Intelligent Systems, Год журнала: 2024, Номер 11(1)

Опубликована: Дек. 19, 2024

The daily occurrence of traffic accidents has led to the development 3D reconstruction as a key tool for reconstruction, investigation, and insurance claims. This study proposes novel virtual-real-fusion simulation framework that integrates accident generation, unmanned aerial vehicle (UAV)-based image collection, pipeline with advanced computer vision techniques unsupervised point cloud clustering algorithms. Specifically, micro-traffic simulator an autonomous driving are co-simulated generate high-fidelity accidents. Subsequently, deep learning-based method, i.e., Gaussian splatting (3D-GS), is utilized construct digitized scenes from UAV-based datasets collected in environment. While visual rendering by 3D-GS struggles under adverse conditions like nighttime or rain, parameter stochastic optimization model mixed-integer programming Bayesian (MIPBO) algorithm proposed enhance segmentation large-scale clouds. In numerical experiments, produces high-quality, seamless, real-time rendered achieve structural similarity index measure up 0.90 across different towns. Furthermore, MIPDBO exhibits remarkably fast convergence rate, requiring only 3–5 iterations identify well-performing parameters high $${R}^{2}$$ value 0.8 on benchmark cluster problem. Finally, Mixture Model assisted MIPBO accurately separates various elements scenes, demonstrating higher effectiveness compared other classical

Язык: Английский