Francesco N Biondi, Amy S McDonnell, Mobina Mahmoodzadeh, Noor Jajo, Balakumar Balasingam, David L Strayer
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Response times to a detection task were recorded over eight consecutive time periods.</p><p><strong>Results: </strong>Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found.</p><p><strong>Conclusion: </strong>Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo.</p><p><strong>Application: </strong>The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. 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引用次数: 0
摘要
目的: 本研究使用检测任务来测量驾驶员在操作四种不同的部分自动化系统时的警惕性变化:本研究使用检测任务来测量驾驶员在操作四种不同的部分自动驾驶系统时警惕性的变化:研究表明,在手动和全自动驾驶过程中,检测任务的表现会出现时间性下降,但使用这种方法测量驾驶员在道路部分自动驾驶过程中警惕性变化的准确性尚未得到证实:方法:受试者在手动和部分自动驾驶模式下驾驶四种不同的车辆(特斯拉 Model 3、凯迪拉克 CT6、沃尔沃 XC90 和日产 Rogue),这些车辆都配备了 2 级系统。在连续八个时间段内记录检测任务的响应时间:贝叶斯分析表明,时间段具有主效应,模式与时间段之间存在交互作用。此外,还发现了车辆的主效应以及时间段 x 车辆之间的交互作用:结果表明,在部分自动驾驶过程中,检测任务的成绩随时间的推移下降得更厉害。针对不同车辆的分析还显示,不同车辆的检测任务表现也不同,沃尔沃的反应时间最慢:应用:在自动驾驶模式下检测性能的下降幅度更大,这表明操作 2 级系统会导致警惕性下降,这一现象引起了人为因素从业人员和监管人员的兴趣。我们还认为,观察到的车辆相关差异可归因于其车载界面的独特设计。
Vigilance Decrement During On-Road Partially Automated Driving Across Four Systems.
Objective: This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems.
Background: Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven.
Method: Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods.
Results: Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found.
Conclusion: Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo.
Application: The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces.
期刊介绍:
Human Factors: The Journal of the Human Factors and Ergonomics Society publishes peer-reviewed scientific studies in human factors/ergonomics that present theoretical and practical advances concerning the relationship between people and technologies, tools, environments, and systems. Papers published in Human Factors leverage fundamental knowledge of human capabilities and limitations – and the basic understanding of cognitive, physical, behavioral, physiological, social, developmental, affective, and motivational aspects of human performance – to yield design principles; enhance training, selection, and communication; and ultimately improve human-system interfaces and sociotechnical systems that lead to safer and more effective outcomes.