基于非驾驶任务的智能座舱出错因子提取与分析

Error Factor Extraction and Analysis of Intelligent Cockpit Based on Non-driving Task

目的针对当前汽车智能座舱系统逐渐由驾驶为主转变为关注驾驶与舱内体验两方面的趋势,通过提取并分析驾驶员的人因差错,探讨智能座舱人机交互界面的设计分析方法。方法基于实车操作实验,选取了10个常见的智能座舱非驾驶任务,对其交互过程中的出错因子进行了提取,结合错误背后的认知机制,形成智能座舱错因聚类,并横向对比了三款不同座舱界面的出错因子复现频率。结果在认知框架的不同模块中,出错因子的复现频率存在显著差异。基于认知理论,对错因背后的设计表征进行了深入归纳与分析。结论将错因分析研究方法引入智能座舱界面的设计研究中,能够更加有效地分析驾驶员对界面信息的认知状态,以及其对驾乘体验的影响,为智能座舱的人机交互设计研究提供了参考依据。

The work aims to explore the design and analysis methods of human-machine interaction(HMI)in intelligent cockpits by extracting and analyzing human errors during the the driving process in response to the evolving trend of automotive intelligent cockpit systems shifting from a focus solely on driving to encompassing both driving and in-cabin experiences.Ten common non-driving tasks in intelligent cockpits were selected and error factors in their interaction processes were extracted through real-world operational experiments.By considering the cognitive mechanisms underlying these errors,clusters of factors contributing to errors in intelligent cockpits were established and the recurrence frequencies of error factors among three different cockpit interfaces were compared.The results indicated significant variations in error factor recurrence frequencies across different modules of the cognitive framework.Based on the cognitive theory,an in-depth deductive analysis was conducted on the interface design features contributing to these differences in error factors.In general,the incorporation of the human error analysis research methodology into the design research process of intelligent cockpit interfaces can significantly enhance the analysis of drivers'cognitive perceptions of interface information.It enables the evaluation of its impact on the driving experience,thereby offering valuable insights for human-machine interaction research in the field of intelligent cockpit design.