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通过基于姿势不稳定性参数的乘客舒适度预测模型来改进智能车辆控制
Bin Xu1, Hang Zhou1, Yuanlong Zhou1
1School of Automobile, Chang'an University, Xi'an 710064, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
智能车辆中的乘客不适与姿势摆动速度和角度速度变化有关. 机器学习模型以高准确度预测舒适度,帮助未来的汽车设计.
科学领域:
- 汽车工程 汽车工程
- 人与计算机的交互
- 生物力学 生物力学
背景情况:
- 智能汽车的性能越来越多地被乘客的舒适性评估.
- 需要客观的舒适度来补充主观的反.
- 了解车辆动态和乘客感知之间的关系至关重要.
研究的目的:
- 调查乘客姿势动态与智能汽车主观舒适度之间的相关性.
- 确定影响乘客不适的关键动力学参数.
- 开发基于姿势不稳定的乘客舒适性的预测模型.
主要方法:
- 在五个驾驶条件下对60名参与者进行了实地测试.
- 收集的乘客姿势,车辆运动和主观舒适度数据.
- 雇员对对样本T测试和脊回归用于分析.
- 开发并验证机器学习和深度学习模型.
主要成果:
- 较高的姿势摆动速度与乘客增加的不适相关.
- 视角速度的变化被确定为不适的主要驱动因素.
- 机器学习模型在预测舒适度方面实现了87.1%的准确性.
- 深度学习模型在预测舒适度方面实现了89%的准确性.
结论:
- 乘客的姿势不稳定,特别是角度速度变化,显著影响舒适度.
- 智能汽车算法设计应优先考虑缓解角速度变化.
- 预测模型为增强智能汽车舒适性提供了宝贵的工具.
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