Hash-Based Gaussian Mixture Model (HGMM) for Roadside LiDAR Smart Infrastructure Applications

This paper proposes a point cloud background filtering method and explores applications that integrate LiDAR technology into roadside sensor networks, addressing challenges in handling large volumes of sparse and unstructured 3D data. Traditional methods, which classify background points based on descriptive statistics over many frames, are computationally inefficient for roadside LiDAR surveillance. This study introduces a novel approach using hash-based transformation combined with probabilistic Gaussian Mixture Model (GMM) techniques, enhancing the efficiency of high-dimensional multivariate modeling of LiDAR data. This Hash-based Gaussian Mixture Modeling (HGMM) optimizes feature selection and Gaussian components using AIC and BIC scores, mitigating computational burdens and parameter sensitivity. Unlike Cartesian coordinate-based techniques, HGMM processes LiDAR data in Spherical coordinates, preserving meaningful patterns and structures. For infrastructure LiDAR, object detection only pertains to a small amount of data in a fixed environment, allowing background reduction modeling to significantly enhance data chain efficiency by transitioning only a tiny portion of the foreground LiDAR point clouds. The method was tested on diverse LiDAR datasets, including the New Brunswick DataCity Testbed dataset, Transportation Forecasting Competition (TRANSFOR 24) Dataset, DAIR-V2X, and A9-Dataset, demonstrating its adaptability and efficiency across different scenarios. This approach enhances LiDAR sensors’ utility in supporting AI-enhanced decision-making processes and enriching the knowledge base of incorporating LiDAR sensors into current and prospective traffic management strategies.