基于群体感性工学的智能网联汽车功能偏好分析

Analysis of function preference for intelligent connected vehicle based on collective-intelligence Kansei engineering

不少基于智能网联汽车(ICV)的研究均假设用户高度接受并同质使用智能座舱内的智能网联功能,而在汽车“新四化”的不同阶段,用户对ICV的功能需求及使用行为有着显著个体差别特征。忽略这些特征,对智能汽车相关研究及技术发展均有较大影响。本文基于感性工学分析方法及利用大量用户的评论文本大数据,提出群体感性工学理念,旨在建立一套系统科学的ICV功能需求与偏好量化分析体系,为智能网联背景下的驾驶员行为研究及ICV技术发展提供决策支撑。该技术框架包括高效的文本数据挖掘分析方法,所提出的基于汽车领域增量训练的Bert模型能自动化识别用户评论文本中的意图、目标功能及情感强度。最后,通过引入座舱内用户行为数据建立NLP-Kansei模型,能大幅提升模型的预测性能。通过对某新能源汽车品牌两款平台车型85441名车主共138819条用户评论进行模型应用,精细化挖掘不同人群对于智能网联功能偏好的细微差别。对比传统预训练模型,新模型能有效挖掘用户需求偏好并显著提升计算效率,其技术框架及结果能为相关领域研发提供有效支撑。

In recent research on intelligent connected vehicles(ICVs),it was assumed that drivers would homogeneously accept and use all ICV functions in the smart cockpit,which is not true i.e.,individual preferences were not considered.Omitting differences in ICV preference may lead to significant shortcomings of ICV-related research and technology development.This paper proposes the concept of collective-intelligence Kansei engineering with a large number of user-generated comments for developing a systematic evaluation method for quantifying the functionality requirements and preferences.The proposed technology framework includes an efficient text-mining algorithm,and an incremental pretrained Bert model is employed that can automatically identify user purposes,targeted functions,and emotion polarity from user texts.Finally,an NLP-Kansei model is developed with user behaviors from the smart cockpit,and the incorporation of the Bert model and user activities can significantly improve the performance of the classification targets.Data of 85441 drivers and 138819 user comments from two new energy vehicle platforms were used for testing the model.The results indicated that the proposed model outperformed the conventional Bert model.The proposed technology framework is practical and can effectively support ICV-related research and development.