考虑认知负载的收购绩效预测模型:主观和客观因素的分析
Huimin Ge1, Peitong Wu1, Lei Dong1
1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, Jiangsu, China.
Traffic injury prevention
|February 3, 2025
概括
增加的认知负载会增加驾驶员的紧张感,影响性能. 一个基因算法优化的逆向传播 (GA-BP) 神经网络模型提高了驾驶员接管预测的准确性.
科学领域:
- 人与计算机的交互
- 认知心理学 认知心理学
- 汽车工程 汽车工程
背景情况:
- 司机的认知负载显著影响驾驶性能和安全.
- 了解认知负载,生理反应和接管性能之间的关系对于先进的驾驶辅助系统 (ADAS) 至关重要.
研究的目的:
- 研究不同认知负载对驾驶员眼睛运动和心电图 (ECG) 的影响.
- 开发和验证使用基因算法优化后向传播 (GA-BP) 神经网络对驾驶员接管性能进行预测模型.
主要方法:
- 使用UC-win/road软件模拟了高速公路驾驶场景,并使用N-back任务进行了各种难度的认知负载诱导任务.
- 收集和分析了驾驶员的眼睛运动,心电图数据和主观的NASA-TLX得分.
- K-means集群确定了认知负载水平,并确定了重要的客观指标来构建GA-BP预测模型.
主要成果:
- 较高的认知负载水平与增加的驾驶员紧张和生理压力相关.
- 在预测收购业绩方面,GA-BP模型比标准BP模型显著改进.
- 具体的改进包括精度增加5.51%,回忆增加7.08%,精度增加6.19%,在10秒的时间窗口中F1得分增加9.71%.
结论:
- 认知任务难度直接影响驾驶员的紧张和认知负载.
- 与传统的BP模型相比,GA-BP模型为驾驶员接管性能提供了更高的预测准确性,提高了驾驶员安全评估.
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