Modeling data acquisition systems (DASs) can support the vehicle industry in the development and design of sophisticated driver assistance systems. Modeling DASs on the basis of multiple criteria is considered as a multicriteria decision-making (MCDM) problem. Although literature reviews have provided models for DASs, the issue of imprecise, unclear, and ambiguous information remains unresolved. Compared with existing MCDM methods, the robustness of the fuzzy decision by opinion score method II (FDOSM II) and fuzzy weighted with zero inconsistency II (FWZIC II) is demonstrated for modeling the DASs. However, these methods are implemented in an intuitionistic fuzzy set environment that restricts the ability of experts to provide membership and nonmembership degrees freely, simulate real-world ambiguity efficiently, utilize a narrow fuzzy number space, and deal with interval data. Thus, this study used a more efficient fuzzy environment interval-valued linear Diophantine fuzzy set (IVLDF) with FWZIC II for criterion weighting and IVLDF with FDOSM for DAS modeling to address the issues and support industrial community characteristics in the design and implementation of advanced driver assistance systems in vehicles. The proposed methodology comprises two consecutive phases. The first phase involves adapting a decision matrix that intersects DAS alternatives and criteria. The second phase (development phase) proposes a decision modeling approach based on formulation of IVLD-FWZIC II and IVLD-FDOSM II to model DASs. A total of 14 DASs were modeled on the basis of 15 DAS criteria, including seven subcriteria for “comprehensive complexity assessment” and eight subcriteria for “design and implementation,” which had a remarkable effect on the DAS design when implemented by industrial communities. Systematic ranking, sensitivity analysis, and modeling checklists were conducted to demonstrate that the modeling results were subject to systematic ranking, as indicated by the high correlations across all described scenarios of changing criterion weight values, supporting the most important research points, and proposing a value-adding process in modeling the most desirable DAS.

建模数据采集系统 (DAS) 可以支持汽车行业开发和设计复杂的驾驶员辅助系统。基于多个标准的 DAS 建模被视为多标准决策 (MCDM) 问题。尽管文献综述为 DAS 提供了模型，但信息不精确、不清楚和模糊的问题仍未得到解决。与现有的MCDM方法相比，意见评分法II（FDOSM II）和零不一致模糊加权II（FWZIC II）的模糊决策在DAS建模中的鲁棒性得到了证明。然而，这些方法是在直观的模糊集环境中实现的，这限制了专家自由提供隶属度和非隶属度、有效模拟现实世界模糊性的能力，利用狭窄的模糊数空间，处理区间数据。因此，本研究使用更高效的模糊环境区间值线性丢番图模糊集 (IVLDF) 与 FWZIC II 进行标准加权，并使用 IVLDF 与 FDOSM 进行 DAS 建模，以解决高级驱动程序设计和实现中的问题并支持工业界特征车辆中的辅助系统。拟议的方法包括两个连续的阶段。第一阶段涉及调整与 DAS 替代方案和标准相交的决策矩阵。第二阶段（开发阶段）提出了一种基于 IVLD-FWZIC II 和 IVLD-FDOSM II 的决策建模方法来对 DAS 进行建模。根据 15 个 DAS 标准对总共 14 个 DAS 进行了建模，其中包括“综合复杂性评估”的七个子标准和“设计与实施”的八个子标准，这些子标准在工业界实施时对DAS设计产生了显着的影响。进行了系统排名、敏感性分析和建模检查表，以证明建模结果受到系统排名的影响，正如所有描述的改变标准权重值、支持最重要的研究点和提出值的场景之间的高度相关性所表明的那样-添加最理想的 DAS 建模过程。