User Experience in Human-Machine Interaction: Insights from Field Studies in Autonomous Mobility

2026-08-31

AI Summary

通过真实交通环境下的实地研究，使用多模态感知技术评估乘客体验，解决了自动驾驶车辆用户接受度问题。

Abstract

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.

Problem

• User Experience
• Human-Machine Interaction
• Autonomous Vehicles
• Field Studies

Innovation

• multi-modal sensing
• affect inference
• real-world field studies
• user-centered design
• self-assessment app