发表机构
Karlsruhe Institute of Technology (KIT); FZI Research Center for Information Technology（卡尔斯鲁厄理工学院; FZI信息技术研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对AVs用户接受度研究的场景局限，通过144名参与者的真实交通实地研究，验证多模态传感在乘客情感推断中的实用性，提供相关研究工具，为自主系统用户中心设计奠定基础。
AI 中文摘要
自动驾驶汽车（AVs）有望实现更安全、更清洁、更具包容性的移动，但大规模推广的阻碍来自用户接受度而非技术挑战。此前关于接受度与用户体验的研究多依赖调查、模拟器或“绿野仙踪”（Wizard-of-Oz）设置，往往过度代表技术热情参与者，且聚焦驾驶员而非乘客。本研究通过在真实交通环境中开展AVs实地研究，共招募144名参与者以填补这一空白。我们采用多模态传感技术，结合车辆数据评估脑电（EEG）、心跳、呼吸、摄像头及语音信号用于情感推断的效果。研究结果显示，呼吸、摄像头及语音测量在自然乘客场景中可靠且实用。我们还贡献了经验证的研究方案、用于人机交互期间实时评估的自我评估应用程序，以及用于捕捉参与者对AVs态度的定制问卷。通过将用户体验（UX）评估建立在真实场景基础上，本研究为自主移动系统及通用机器人的用户中心设计奠定了基础，同时填补了情感计算与AVs技术实现之间的鸿沟。
英文摘要
Autonomous vehicles (AVs) promise safer, cleaner, and more inclusive mobility, yet large-scale adoption is hindered by user acceptance rather than by technical challenges. Prior studies on acceptance and user experience largely rely on surveys, simulators or Wizard-of-Oz setups, often over-representing technologically enthusiastic participants and focusing on drivers instead of passengers. We address this gap with real-world field studies with AVs in real traffic, totaling 144 participants. Using multi-modal sensing, we evaluated EGG, heartbeat, breathing, camera and voice signals for affect inference in combination with vehicle data. Our results show that breathing, camera and voice measurements are reliable and pratical in naturalistic passenger contexts. We further contribute a validated study protocol, a self-assessment app for real-time assessment during human-machine interaction, and a tailored questionnaire to capture participant attitudes towards AVs. By grounding UX evaluation in real-world contexts, this work lays a foundation for user-centered design of autonomous mobility systems and robotics in general. Our work bridges the gap between affective computing and technical implementation of autonomous vehicles.
CommentsAccepted for publication at ABAW Workshop at ECCV2026
