个性化运动病模型:对个体特定参数的估计和统计建模
Varun Kotian1, Daan M Pool2, Riender Happee1
1Faculty of Mechanical Engineering, Cognitive Robotics, Delft University of Technology, Delft, Netherlands.
Frontiers in systems neuroscience
|July 1, 2025
概括
个性化运动病模型可以提高自动驾驶汽车的预测准确度. 这种个性化的方法捕捉了用户独特的敏感性,在非驾驶活动中提高了安全性和舒适性.
科学领域:
- 人与计算机的互动.
- 汽车工程 汽车工程
- 人类因素 心理学 心理学
背景情况:
- 自动驾驶汽车将用户从司机转移到乘客,导致非驾驶活动和潜在的运动病.
- 车辆和模拟器中预期和感知运动之间的差异会导致疾病,需要改进运动控制.
- 移动病易感性的个体差异需要个性化的对策.
研究的目的:
- 开发和验证一个个性化的框架,用于预测自动驾驶汽车的运动病.
- 为了捕捉各个运动和视觉条件中运动病易感度的个体差异.
- 为个性化干预提高运动恶心预测模型的准确性.
主要方法:
- 结合了群体平均感官冲突模型与个性化积累模型 (AM).
- 在被动运动条件下使用车辆和模拟器实验的三个数据集验证了框架.
- 利用一个具有两个参数 (增益K1和时间常数T1) 的个性化AM (AM2) 来建模个体反应.
主要成果:
- 与组平均的AM0模型相比,AM2模型在匹配个体运动恶心反应方面实现了1.7的平均改善因子.
- AM2模型使用个性化参数准确地模拟了各种运动和视觉条件的个体疾病反应.
- 参数分布的高斯混合模型在一个未见的数据集中预测了移动性疾病,平均RMSE为0.47.
结论:
- 拟议的个性化积累模型 (AM2) 框架有效地捕捉了自动驾驶汽车中个人运动病的易感性.
- 这种个性化的方法提高了预测的准确性,并减少了对人口层面进行广泛测试的需求.
- 该框架提供了一个强大的解决方案,用于开发定制的运动控制策略,以减轻自动驾驶车辆乘客的疾病.
关键词:
自动化车辆自动化车辆驾驶模拟器上的驾驶模拟器建模建模是什么意思运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness模拟器疾病的疾病更多相关视频
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