为自动驾驶汽车的用户提供以人为中心的解释:系统性审查
Zishuo Zhu1, Xiaomeng Li1, Patricia Delhomme2
1Queensland University of Technology, Centre for Accident Research and Road Safety - Queensland (CARRS-Q), 130 Victoria Road, Kelvin Grove 4059, Australia.
Accident; analysis and prevention
|July 2, 2025
概括
关于自动驾驶汽车 (AV) 的解释应该澄清推理,而不仅仅是行动. 根据驾驶环境量身定制解释,并在采取行动之前提供它们,可以提高用户的信任和接受度,提高安全性.
科学领域:
- 人与计算机的交互
- 自动化车辆系统 自动化车辆系统
- 值得信赖的AI 值得信赖的AI
背景情况:
- 自动化汽车 (AV) 决策可以减少用户的信任和接受.
- 以人为中心的解释对于透明度,信任和安全的人与车辆的互动至关重要.
- 关于解释类型如何影响驾驶员以及如何评估驾驶员的研究有限.
研究的目的:
- 在自动化车辆环境中系统地审查以人为中心的解释.
- 确定什么,什么时候以及如何提供解释的最佳实践.
- 解决解释对用户信任,接受和情境意识的影响.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 从2000年到2024年4月,搜索了五个主要的数据库 (Scopus,科学网络,IEEE Xplore,TRID,语义学者).
- 在266个已识别的物品中,包括59个物品.
主要成果:
- 澄清推理的解释比描述行动的解释更有效.
- 建议在采取行动之前进行解释,但最佳时机需要进一步研究.
- 多模式解释是最好的,当不同的;否则,仅视觉是最好的. 叙事视角对信任的影响不同.
结论:
- 以人为中心的解释必须根据特定的驾驶环境量身定制.
- 未来的研究应该专注于解释的长度,时间,模式和现实世界的验证.
- 优化解释对于更安全,更值得信赖的人类-自动驾驶交互至关重要.
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