Comparative analysis of drowsiness and performance in conditionally automated driving and manual driving considering the effect of circadian rhythm

IF 2.8 3区 工程技术 Q3 TRANSPORTATION Journal of Intelligent Transportation Systems Pub Date : 2024-05-03 DOI:10.1080/15472450.2022.2130292
Qi Zhang , Chaozhong Wu , Hui Zhang , Sara Ferreira
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Abstract

Drowsiness in manual driving (MD) is influenced by circadian rhythms. Conditionally automated driving (CAD) affects drivers’ drowsiness. We conducted a simulator study with 30 participants (every ten subjects in morning group, afternoon group, and evening group) to investigate the effect of circadian rhythm on the changes in drivers’ drowsiness and performance in different driving modes. Each subject was required to complete CAD experiment first and MD experiment later, and experienced 8 risk scenarios in each experiment. The self-reported Karolinska Sleepiness Scale (KSS) was recorded by an investigator every time when the subject drove past the scenario as the drowsiness measurement. The speed, acceleration, time-related metrics, and vehicle lane position were collected as the performance measurements. KSS data were statistically analyzed, and the Spearman’s Rho test was used to confirm the correlation among performance measurements, KSS, and scenarios. The result of the KSS statistical analysis showed that the effect of circadian rhythm on fatigue in MD groups is consistent with the previous studies, but the existence of CAD changes the effect of the circadian rhythm. Compared with the MD, CAD slowed down the drowsiness growth rate in the morning group and promoted the drowsiness growth rate in the evening group. The brake input rate, mean longitude acceleration, max Standard Deviation of Lane Position (SDLP), and the time to pass (TTP) were significantly related to the driver´s drowsiness in both driving modes.

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考虑到昼夜节律的影响,对有条件自动驾驶和手动驾驶中的嗜睡和表现进行比较分析
手动驾驶(MD)中的嗜睡受昼夜节律的影响。有条件自动驾驶(CAD)会影响驾驶员的嗜睡程度。我们对 30 名受试者(上午组、下午组和晚上组每 10 名受试者)进行了模拟研究,探讨昼夜节律对不同驾驶模式下驾驶者嗜睡程度和表现变化的影响。每位受试者需先完成 CAD 实验,后完成 MD 实验,并在每个实验中体验 8 种风险情景。研究人员在受试者每次驾车经过该场景时记录其自我报告的卡罗林斯卡嗜睡量表(KSS),作为嗜睡度测量值。车速、加速度、时间相关指标和车道位置作为性能测量指标。对 KSS 数据进行统计分析,并使用 Spearman's Rho 检验来确认性能测量、KSS 和场景之间的相关性。KSS 统计分析结果表明,昼夜节律对 MD 组疲劳的影响与之前的研究一致,但 CAD 的存在改变了昼夜节律的影响。与 MD 相比,CAD 减慢了晨间组的嗜睡增长速度,促进了晚间组的嗜睡增长速度。在两种驾驶模式下,制动输入率、平均经度加速度、最大车道位置标准偏差(SDLP)和通过时间(TTP)都与驾驶员的嗜睡程度显著相关。
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来源期刊
CiteScore
8.80
自引率
19.40%
发文量
51
审稿时长
15 months
期刊介绍: The Journal of Intelligent Transportation Systems is devoted to scholarly research on the development, planning, management, operation and evaluation of intelligent transportation systems. Intelligent transportation systems are innovative solutions that address contemporary transportation problems. They are characterized by information, dynamic feedback and automation that allow people and goods to move efficiently. They encompass the full scope of information technologies used in transportation, including control, computation and communication, as well as the algorithms, databases, models and human interfaces. The emergence of these technologies as a new pathway for transportation is relatively new. The Journal of Intelligent Transportation Systems is especially interested in research that leads to improved planning and operation of the transportation system through the application of new technologies. The journal is particularly interested in research that adds to the scientific understanding of the impacts that intelligent transportation systems can have on accessibility, congestion, pollution, safety, security, noise, and energy and resource consumption. The journal is inter-disciplinary, and accepts work from fields of engineering, economics, planning, policy, business and management, as well as any other disciplines that contribute to the scientific understanding of intelligent transportation systems. The journal is also multi-modal, and accepts work on intelligent transportation for all forms of ground, air and water transportation. Example topics include the role of information systems in transportation, traffic flow and control, vehicle control, routing and scheduling, traveler response to dynamic information, planning for ITS innovations, evaluations of ITS field operational tests, ITS deployment experiences, automated highway systems, vehicle control systems, diffusion of ITS, and tools/software for analysis of ITS.
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