摘要
为探究敌车方向的多模态告警对不同认知负荷水平下装甲车辆乘员反应的影响,招募20名成年男性开展告警类型和认知负荷水平双因素工效学实验。基于装甲车辆乘员任务虚拟仿真实验平台,面向装甲车辆乘员某一假定作业任务,针对敌车方向对不同认知负荷水平下的乘员进行告警。实验结果表明:相比视觉告警,包含触觉的多模态告警更容易使乘员理解且更符合乘员的认知状态,显著减少了乘员的反应时间、歼敌时间和反应错误率;相比低认知负荷水平,高认知负荷水平下乘员的信息理解能力显著下降,反应时间、歼敌时间和反应错误率显著增加;在视觉通道信息量过载或认知负荷增大时,可采用多模态告警来加快乘员的反应时间,提升作战绩效。该研究可为装甲车辆舱室多模态人机交互告警设计提供理论依据。
To explore the effects of multimodal warning on the response of armored vehicle occupants regarding the direction of enemy vehicles under different cognitive load levels,20 adult males were recruited to take two-factor ergonomic experiments on the warning type and cognitive load level.The experiments were based on a virtual simulated armored vehicle occupant task platform designed for a hypothetical operational task for armored vehicle occupants and warned the occupants under different cognitive load levels about enemy vehicle directions.Results show that multimodal warning with haptics was easier for occupants to understand the situation and more in line with their cognitive state than visual warning.This type of warning significantly reduced their reaction time,annihilation time,and response error rate.Occupants with a high cognitive load level showed a significant decrease in information comprehension and a significant increase in reaction time,annihilation time,and response error rate compared to those with a lower load.The findings suggest that multimodal warning can be used to speed up occupant reaction and improve combat performance when the visual channel is overloaded or the cognitive load is high.This study provides a theoretical basis for designing multimodal human-computer interaction warnings in armored vehicle compartments.
作者
孙晓东
金晓萍
解芳
孙厚杰
郑思涓
SUN Xiaodong;JIN Xiaoping;XIE Fang;SUN Houjie;ZHENG Sijuan(College of Engineering,China Agricultural University,Beijing 100083,China;China North Vehicle Research Institute,Beijing 100072,China)
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2023年第4期972-981,共10页
Acta Armamentarii
基金
军队科研计划项目(2020年)。
关键词
装甲车辆
乘员
告警
多模态
认知负荷
人机交互
armored vehicle
occupant
warning
multimodal
cognitive load
human-computer interaction