一个视觉前置模型来预测线性和角运动的运动疾病
Daniel Sousa Schulman1, Nishant Jalgaonkar1, Sneha Ojha1
1University of Michigan, Ann Arbor, MI, USA.
Human factors
|September 12, 2023
概括
一个新的模型通过分析视觉前体冲突在现实的驾驶场景中预测乘客的运动病. 该工具有助于设计干预措施并改善自动驾驶汽车乘客体验.
科学领域:
- 人与计算机的互动.
- 汽车工程 汽车工程
- 神经科学是一个神经科学.
背景情况:
- 现有的运动病模型在现实驾驶条件下缺乏准确性.
- 需要采用3D视觉前体运动输入的模型.
- 预测乘客运动病需要改进的模型.
研究的目的:
- 提出并验证一种用于预测乘客运动病的新型模型.
- 为了解释视觉前体冲突在运动恶心的预测.
- 提高对导致乘客不适的因素的了解.
主要方法:
- 综合的主观垂直冲突理论和人类运动感知模型.
- 开发了一种新的架构,集成视觉和前庭6度自由度 (DoF) 运动信号.
- 将模型与来自运动模拟器和道路测试的实验数据进行验证.
主要成果:
- 模型预测显示,在模拟和现实世界驾驶中,与观察到的运动病趋势一致.
- 根据乘客的任务 (例如,手持设备使用与向前转向) 成功预测了运动病的变化.
- 在现实的驾驶条件下展示了该车型的能力.
结论:
- 该模型有效地预测了在不同视觉前体冲突下运动病的趋势.
- 建议对更大的群体进行进一步验证,以进行全面的绩效评估.
- 该模型作为一种有价值的工具,可以缓解乘客的运动病,并为自动驾驶汽车设计提供信息.
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