We present a fully interpretable and flexible statistical method for background subtraction in roadside LiDAR data, aimed at enhancing infrastructure-based perception in automated driving. Our approach introduces both a Gaussian distribution grid (GDG), which models the spatial statistics of the background using background-only scans, and a filtering algorithm that uses this representation to classify LiDAR points as foreground or background. The method supports diverse LiDAR types, including multiline 360 degree and micro-electro-mechanical systems (MEMS) sensors, and adapts to various configurations. Evaluated on the publicly available RCooper dataset, it outperforms state-of-the-art techniques in accuracy and flexibility, even with minimal background data. Its efficient implementation ensures reliable performance on low-resource hardware, enabling scalable real-world deployment.

翻译：我们提出了一种完全可解释且灵活的统计方法，用于路侧激光雷达数据的背景减除，旨在增强自动驾驶中基于基础设施的感知能力。我们的方法引入了高斯分布网格（GDG），该网格利用纯背景扫描对背景的空间统计特性进行建模，以及一种利用此表示将激光雷达点分类为前景或背景的滤波算法。该方法支持多种激光雷达类型，包括多线360度扫描和微机电系统（MEMS）传感器，并能适应不同的配置方案。在公开可用的RCooper数据集上的评估表明，即使在背景数据极少的情况下，该方法在准确性和灵活性方面也优于现有先进技术。其高效实现确保了在低资源硬件上的可靠性能，从而支持可扩展的实际部署。