多意象驱动下的无人驾驶汽车座椅造型设计
摘 要：为满足用户在无人驾驶座椅使用场景中的多维度情感需求，文章提出一套以多意象目标为核心的造型设计流程。首先，基于感性工学构建样本与意象的评价矩阵，获取用户对各方案的情感反馈；其次，采用层次分析法确定各意象权重，并结合意象评分计算蛛网灰靶模型的综合决策数值，以评定样本的多维度情感表现；再次，运用形态分析法搭建造型元素组合空间，建立元素组合与决策值之间的关联矩阵，明确不同造型特性对综合表现的影响趋势；最后，将造型组合输入BP神经网络，筛选出最优设计要素搭配，并据此完成一款满足用户多维度情感需求的无人驾驶汽车座椅设计。结果表明，该方法具有可行性与有效性。
关键词：工业设计；多意象；无人驾驶；汽车座椅；造型设计；感性工学
中图分类号：TB472 文献标识码：A 文章编号：1672-7053(2026)08-0134-05
Abstract：To meet users' multi-dimensional emotional needs in the context of driverless seat usage, this article proposes a styling design process centered around multi-image objectives. Firstly, based on Kansei Engineering, an evaluation matrix of samples and images is constructed to obtain users' emotional feedback on various schemes. Secondly, the Analytic Hierarchy Process (AHP) is employed to determine the weight of each image, and the comprehensive decision-making value of the cobweb grey target model is calculated in combination with image scores to evaluate the multi-dimensional emotional expression of samples. Thirdly, morphological analysis is utilized to build a combination space of styling elements, establish a correlation matrix between element combinations and decision values, and clarify the influence trend of different styling characteristics on comprehensive expression. Finally, the styling combinations are input into a BP neural network to screen out the optimal design element combinations, and based on this, a driverless car seat design that meets users' multi-dimensional emotional needs is completed. The results show that this method is feasible and effective.
Key Words：Industrial Design; Multiple Imagery; Autonomous Driving; Car Seats; Form Design; Kansei Engineering
