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- 2016
结合自然语言处理与改进层次分析法的乘用车驾驶舒适性评价
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Abstract:
为了有效和准确地评价乘用车驾驶舒适性, 构建了 “人-机-环境” 系统下的汽车驾驶舒适性本体模型, 并对驾驶舒适性的形成机制进行了阐述。通过用户访谈实验并基于改进的 “词频-逆向文件频率” (TF-IDF)方法对主题词进行提取, 获得影响驾驶舒适性的主要因素并进行分类, 在此基础上建立递阶层次结构; 针对传统层次分析法(AHP)的不足, 以三标度取代九标度以保证精确性, 通过Delphi实验, 构造两两比较判别矩阵后再进行处理, 以计算各评价指标的权重, 并对该指标体系进行一致性检验。以C级乘用车为例, 验证了该评价方法的有效性。该方法为乘用车的驾驶舒适性评价提供了一种可行的技术支持。
Abstract：The driving comfort of drivers in passenger vehicles was evaluated using an ontology model for driving comfort in a human-machine-environment system. The model was then used to evaluate driving comfort with the main driving comfort description extracted and classified via user interviews based on the improved term frequency-inverse document frequency (TF-IDF) method. The words were then ranked in a hierarchical structure. The scale of nine in the traditional analytical hierarchy process (AHP) was replaced by a scale of three to improve the accuracy. The comparative judgment matrix was defined with the weight of each index calculated through a Delphi survey. The index consistency system was also tested. The reliability of this method was validated using C-class passenger vehicles. Thus, this gives an effective approach for evaluating vehicle driving comfort.
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