为满足自动驾驶产品准入的实际需求，推动自动驾驶仿真可信度评估的标准化，基于UN R171等相关标准法规，提出了一套基于“管理-分析-验证-确认”框架的仿真可信度评估体系。首先，结合标准法规要求，构建标准化的自动驾驶系统仿真可信度评估作业流程。其次，建立系统化的方法体系：在管理层面，明确文档记录要点；在分析层面，构建仿真关键性分析方法；在验证层面，依托数值分析、软件质量保证与误差分析，提出代码验证、计算验证及敏感性分析方法；在确认层面，构建仿真可信度的量化评价指标。结合实际工程案例，为生产企业构建一套可落地的仿真可信度评估体系，并验证其工程适用性。案例分析表明：①不同评价指标在量纲和物理含义上存在差异，各自适用于不同误差尺度下的可信度表征，因此指标的选取应具备多样性，以降低评价偏倚；②关键敏感输入参数的不确定性需控制在预期范围内，以避免误差的传播与扩大；③仿真模型的数值平滑作用会在某些工况引起较大的局部误差，因此需明确仿真模型的适用范围。研究结论可为自动驾驶仿真可信度评估的标准化与产业化应用提供参考。

To satisfy the practical requirements of the autonomous driving product type approval, and promote the standardization of simulation credibility assessment, this paper proposes a simulation credibility assessment system based on the "Management-Analysis-Verification-Validation" paradigm, aligned with relevant standards such as UN R171. First, by integrating regulatory requirements, a standardized workflow for autonomous driving system simulation credibility assessment is established. Subsequently, a systematic methodological solution is developed: at the management level, documentation essentials are determined; at the analysis level, a simulation criticality analysis method is constructed; at the verification level, based on numerical analysis, software quality assurance, and error analysis, methods for code verification, calculation verification, and sensitivity analysis are proposed; and at the validation level, quantitative metrics for credibility assessment are established. Through a practical project case, an implementable credibility assessment system is developed for manufacturers, and its applicability is verified. The case study reveals that: ① due to differences in dimensions and physical interpretations among various metrics, each metric is suitable for credibility characterization under different error scales; therefore, the selection of metrics should be diversified to reduce evaluation bias; ② uncertainty in key sensitive input parameters must be controlled within expected bounds to prevent error propagation and amplification; ③ the numerical smoothing of models may cause significant local errors under certain driving cycles, thus the application scope of the simulation model should be clearly defined. The research conclusion can provide a reference for the standardization and industrial application of autonomous driving simulation credibility assessment.