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中国条件自动驾驶接管意向:现状与影响因素。

Take-Over Intention during Conditionally Automated Driving in China: Current Situation and Influencing Factors.

机构信息

School of Transportation, Southeast University, Nanjing 210096, China.

School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei 230009, China.

出版信息

Int J Environ Res Public Health. 2021 Oct 21;18(21):11076. doi: 10.3390/ijerph182111076.

DOI:10.3390/ijerph182111076
PMID:34769595
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8583141/
Abstract

Drivers' take-over intention is important for the design of the automated driving systems and affects the safety of automated driving. This study explored the influence factors on drivers' take-over intention during conditionally automated driving, examined the correlations among factors through path analysis, and established a take-over intention model. A questionnaire survey was conducted in Hefei, China, and a sample of 277 drivers was obtained. Our study shows that the average take-over intention of those aged under 20 is lower than that of the older age groups. In the positive emotions (PE) scenarios, the take-over intention of aged 31-40 is significantly higher than that of the other age groups. Education and occupation have a significant influence on the take-over intention. The perceived ease of use (PEofU) and perceived usefulness (PU) of automated driving are significantly negatively correlated with drivers' take-over intention in the road conditions (RC) and climate conditions (CC) scenarios. In addition, through path model analysis, our study shows that trust in the safety of autonomous vehicles (AVs) plays an important role in drivers' take-over intention. Technology acceptance, risk perception and self-efficacy has indirectly correlated with take-over intention through trust in the safety of AVs. In general, drivers with lower technology acceptance, lower self-efficacy and higher risk perception are less likely to trust automated driving technology and have shown stronger intention to take-over the control of the vehicles.

摘要

驾驶员接管意图对于自动化驾驶系统的设计非常重要,并且影响自动化驾驶的安全性。本研究探讨了条件自动化驾驶过程中驾驶员接管意图的影响因素,通过路径分析检验了因素之间的相关性,并建立了接管意图模型。在中国合肥进行了问卷调查,获得了 277 名驾驶员的样本。我们的研究表明,20 岁以下驾驶员的平均接管意图低于其他年龄组。在积极情绪(PE)场景中,31-40 岁年龄组的接管意图明显高于其他年龄组。教育和职业对接管意图有显著影响。在道路条件(RC)和气候条件(CC)场景中,驾驶员对自动化驾驶的易用性感知(PEofU)和有用性感知(PU)与接管意图呈显著负相关。此外,通过路径模型分析,我们的研究表明,对自动驾驶汽车(AV)安全的信任在驾驶员接管意图中起着重要作用。技术接受度、风险感知和自我效能感通过对自动驾驶汽车安全的信任与接管意图间接相关。一般来说,技术接受度较低、自我效能感较低和风险感知较高的驾驶员不太可能信任自动化驾驶技术,并且表现出更强的接管车辆控制的意图。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab46/8583141/66eaccf2375c/ijerph-18-11076-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab46/8583141/db344230942a/ijerph-18-11076-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab46/8583141/93d6a563daec/ijerph-18-11076-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab46/8583141/66eaccf2375c/ijerph-18-11076-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab46/8583141/db344230942a/ijerph-18-11076-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab46/8583141/93d6a563daec/ijerph-18-11076-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab46/8583141/66eaccf2375c/ijerph-18-11076-g003.jpg

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本文引用的文献

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The effects of takeover request modalities on highly automated car control transitions.接管请求模式对高度自动化汽车控制转换的影响。
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Psychosocial factors associated with intended use of automated vehicles: A simulated driving study.
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Accid Anal Prev. 2018 Jun;115:202-208. doi: 10.1016/j.aap.2018.03.021. Epub 2018 Apr 6.
4
Effect of different alcohol levels on take-over performance in conditionally automated driving.不同酒精水平对条件自动化驾驶中接管性能的影响。
Accid Anal Prev. 2018 Jun;115:89-97. doi: 10.1016/j.aap.2018.03.001. Epub 2018 Mar 15.
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Effective cues for accelerating young drivers' time to transfer control following a period of conditional automation.有效线索可加速年轻驾驶员在条件自动化期间后转移控制权的时间。
Accid Anal Prev. 2018 Jul;116:14-20. doi: 10.1016/j.aap.2017.10.005. Epub 2017 Oct 12.
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Analyzing crowdsourced ratings of speech-based take-over requests for automated driving.分析基于语音的自动驾驶接管请求的众包评级
Appl Ergon. 2017 Oct;64:56-64. doi: 10.1016/j.apergo.2017.05.001. Epub 2017 May 22.
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Take-over again: Investigating multimodal and directional TORs to get the driver back into the loop.再次接管:研究多模态和定向转矩以让驾驶员重新参与进来。
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