从黑盒到透明度:多层次可视化对用户对自动驾驶的信任的影响
Mengniu Li1, Ming Zhou1, Yajun Li1
1School of Design Art & Media, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
自主系统中的可解释的可视化增强了用户的信任,并降低了认知负载. 增强的可视化显著改善了感知的有用性和功能信任,证明了有意义的透明度的价值.
科学领域:
- 人与计算机的互动.
- 自主系统 自主系统
- 视觉化的可视化
背景情况:
- 这是一个很棒的节目,这是一个很棒的节目.
- 一个黑盒子.
- 自主系统的性质阻碍了用户的信任和采用.
- 可解释的可视化对于增强人机协作至关重要.
研究的目的:
- 调查可解释的可视化对用户信任和自动驾驶中的认知状态的影响.
- 将不同级别的系统透明度的用户体验进行比较.
主要方法:
- 一项对29名参与者进行高保真驾驶任务的内科研究.
- 评估了三个透明度条件:黑盒,标准和增强可视化.
- 多模式数据分析,包括主观评分和生理测量 (瞳孔直径,固定时间).
主要成果:
- 增强的可视化显著增加了感知到的有用性 (28.5%) 并改善了功能信任.
- 平均瞳孔直径在增强可视化时减少了15.3%,这表明认知负载较低.
- 黑盒子条件导致了最小的参与度和最低的信任度.
结论:
- 精心设计的可视化促进了平衡的信任校准和认知效率.
- "有意义的透明度"是自动驾驶汽车接口的关键设计原则.
- 透明度提高了自主系统中的用户体验和系统性能.
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