A Comparative Study of Speculative Retrieval for Multi-Modal Data Trails: Towards User-Friendly Human-Vehicle Interactions

Yaohua Wang, Zhengtao Huang, Rongze Li, Xinyu Yin, Min Luo, Zheng Zhang, Xu Sun
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引用次数: 2

Abstract

In the era of growing developments in Autonomous Vehicles, the importance of Human-Vehicle Interaction has become apparent. However, the requirements of retrieving in-vehicle drivers' multi-modal data trails, by utilizing embedded sensors, have been consid- ered user unfriendly and impractical. Hence, speculative designs, for in-vehicle multi-modal data retrieval, has been demanded for future personalized and intelligent Human-Vehicle Interaction. In this paper, we explore the feasibility to utilize facial recog- nition techniques to build in-vehicle multi-modal data retrieval. We first perform a comprehensive user study to collect relevant data and extra trails through sensors, cameras and questionnaire. Then, we build the whole pipeline through Convolution Neural Net- works to predict multi-model values of three particular categories of data, which are Heart Rate, Skin Conductance and Vehicle Speed, by solely taking facial expressions as input. We further evaluate and validate its effectiveness within the data set, which suggest the promising future of Speculative Designs for Multi-modal Data Retrieval through this approach.
多模态数据路径推测检索的比较研究:面向用户友好的人机交互
在自动驾驶汽车日益发展的时代,人车交互的重要性已经变得明显。然而,利用嵌入式传感器检索车内驾驶员的多模态数据轨迹的要求被认为是用户不友好和不切实际的。因此,为了实现未来个性化和智能化的人机交互,需要对车载多模态数据检索进行推测性设计。本文探讨了利用人脸识别技术构建车载多模态数据检索的可行性。我们首先进行全面的用户研究,通过传感器、摄像头和问卷收集相关数据和额外的轨迹。然后,我们通过卷积神经网络构建整个管道,以单独的面部表情作为输入,预测心率、皮肤电导和车速三种特定类别数据的多模型值。我们进一步评估和验证了其在数据集中的有效性,这表明通过这种方法进行多模态数据检索的推测设计具有广阔的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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