Random processes play a crucial role in scientific research, often characterized by distribution functions or probability density functions (PDFs). These PDFs serve as essential approximations of the actual and frequently undisclosed distribution governing the random process under examination. Diverse methodologies exist for estimating PDFs, each offering distinct advantages in specific contexts. This publication presents a novel approach that centers on estimating probability density functions by leveraging histograms and B-spline curves, with a particular focus on analyzing vehicle-related time series data. The proposed method outlines a comprehensive framework for estimating PDFs tailored specifically to the study of vehicle-related phenomena. By effectively combining the strengths of histograms and B-spline curves, researchers gain a powerful toolset to obtain precise and reliable estimations of PDFs, thereby enabling advanced analysis and comprehension of vehicle-related random processes in scientific investigations.

翻译：随机过程在科学研究中扮演着关键角色，通常通过分布函数或概率密度函数（PDFs）来表征。这些PDF作为所研究随机过程中实际且往往未公开分布的重要近似。存在多种估计PDF的方法，每种方法在特定情境下各有优势。本文提出了一种新方法，专注于利用直方图和B样条曲线估计概率密度函数，特别聚焦于分析车辆相关时间序列数据。该方法为车辆相关现象的研究量身定制了一套完整的PDF估计框架。通过有效结合直方图与B样条曲线的优势，研究人员得以获得精确可靠的PDF估计工具集，从而在科学调查中实现对车辆相关随机过程的高级分析与理解。