对自动驾驶的多式模式解释对驾驶性能,认知负载,专业知识,信心和信任的影响
Robert Kaufman1, Jean Costa2, Everlyne Kimani2
1University of California - San Diego, La Jolla, CA, 92093, USA. rokaufma@ucsd.edu.
Scientific reports
|June 6, 2024
概括
人工智能教练可以通过解释驾驶技巧来教授驾驶技巧.
科学领域:
- 人与计算机的互动.
- 教育中的人工智能
- 汽车工程 汽车工程
背景情况:
- 自动驾驶技术的进步为人工智能辅助驾驶指令创造了机会.
- 提高人类驾驶技能仍然是一个关键的需求.
- 有效的人工智能学习沟通策略尚未明确.
研究的目的:
- 调查AI教练解释如何影响新手驾驶员的学习.
- 评估信息类型 ("什么"与"为什么") 和模式 (听觉与视觉) 对学习成果的影响.
- 了解人工智能解释与驾驶性能,认知负载,自信,专业知识和信任之间的关系.
主要方法:
- 对41名新手参与者进行了前后实验.
- 参与者根据AI解释类型和呈现方式被分配到四组.
- 测量了驾驶表现,认知负载,自信,专业知识和信任;进行了采访,以了解学习过程.
主要成果:
- 人工智能教练有效地向新手教授性能驱动技能.
- 信息类型和呈现方式都对绩效结果产生了重大影响.
- 学习成功与解释如何引导注意力,缓解不确定性和管理认知过载有关.
结论:
- 有效,适合模式的AI解释对于车辆中有效的人机界面 (HMI) 通信至关重要.
- 人工智能辅导设计必须与人类的学习和认知过程保持一致,以避免压倒用户.
- 提供了未来自动驾驶汽车HMI和AI教练开发的八个设计含义.
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