Review of Take-over Performance of Automated Driving: Influencing Factors, Models, and Evaluation Methods

被引：0
|
作者：
Wang W.-J. [1 ]
Li Q.-K. [2 ,3 ]
Zeng C. [4 ,5 ]
Li G.-F. [6 ]
Zhang J.-L. [1 ]
Li S.-B. [1 ]
Cheng B. [1 ]
机构：
[1] School of Vehicle and Mobility, Tsinghua University, Beijing
[2] Beijing Key Laboratory of Human-computer Interaction, Institute of Software, Chinese Academy of Sciences, Beijing
[3] Automotive Software Innovation Center (Chongqing), Chongqing
[4] College of Information Science and Engineering, Henan University of Technology, Henan, Zhengzhou
[5] College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, Xinjiang
[6] College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing
来源：
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | 2023年 / 36卷 / 09期
基金：
中国国家自然科学基金;
关键词：
automotive engineering; conditionally automated driving; driver; driving behavior; human factors; review; take-over performance;
D O I：
10.19721/j.cnki.1001-7372.2023.09.017
中图分类号：
学科分类号：
摘要：
Conditionally automated driving systems, though advanced, are not universally adept at managing all driving scenarios and require driver intervention when necessary. The efficacy of driver take-over is paramount for the safety, user experience, and broader acceptance of such automated vehicles. A plethora of recent studies rigorously examined driver take-over performance, but certain challenges persist. This study presented a systematic review of extant literature concerning driver take-over performance, encapsulating the influencing factors, the models, and the various evaluation methodologies employed. The determinants influencing takeover performance span driver-specific factors, traffic environment parameters, and features of the automated driving systems. Concerning the modeling of take-over performance, distinctions were drawn between classical statistical models, machine learning approaches, and structural equation models. The study further encapsulated prevailing evaluation indices specific to take-over performance, alongside holistic evaluation methodologies. Findings from the review pinpoint that current indicators for influencing factors lack comprehensiveness. Additionally, a discernible imbalance between interpretability and predictive accuracy is observed in the existing models. Furthermore, the present evaluation methods for take-over performance necessitate refinement. As a roadmap for future inquiries, this study advocates for the initiation of comprehensive measures of take-over performance based on subjective evaluation of human drivers. Then, there is an imperative to develop quantitative indicators of the influencing factors of take-over performance from human-machine-environment aspects. Conclusively, calls are made for crafting high-precision predictive models for take-over performance that duly recognize the intricate interdependencies of myriad influencing factors. Pursuing such avenues of research is vital to provide theoretical support for elevating driver take-over performance, thus propelling the evolution of conditionally automated driving. © 2023 Xi'an Highway University. All rights reserved.
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页码：202 / 224
页数：22
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