标题:Influencing Factors of Driving Decision-Making Under the Moral Dilemma
作者:Li, Sixian; Zhang, Junyou; Li, Pengcheng; Wang, Yongqing; Wang, Qiaoqiao
作者机构:[Li, Sixian; Zhang, Junyou; Wang, Yongqing] Shandong Univ Sci & Technol, Coll Transportat, Dept Transportat Engn, Qingdao 266590, Shandong, Peoples R 更多
通讯作者:Zhang, JY
通讯作者地址:[Zhang, JY]Shandong Univ Sci & Technol, Coll Transportat, Dept Transportat Engn, Qingdao 266590, Shandong, Peoples R China.
来源:IEEE ACCESS
出版年:2019
卷:7
页码:104132-104142
DOI:10.1109/ACCESS.2019.2932043
关键词:Autonomous vehicles; accident liability; driving decision-making factor;; gray relation entropy analysis; moral dilemma
摘要:Autonomous vehicles (AVs) are supposed to make appropriate strategies to ensure driving safety and improve traffic efficiency. However, not all collisions will be avoidable. In a typical dilemma scenario of this paper, we have to make hard decisions from two evils, sparing a child with red-light running behavior or crashing into a well-equipped motorcycle driver without violation of the traffic law. Combined with the existing traffic laws and the accident liability judgment cases in China, 12 typical dilemma scenarios are established. In order to acquire data to analyze the driving decision-making factors involving ethics and legal under the moral dilemma of AVs, we conduct a series of experiments in virtual reality environments. Furthermore, key factors are extracted to characterize the driving decisions under different scenarios and quantified by the gray relation entropy analysis method. Particularly, the ethical factor is represented by the quantity and type of collision targets, the legal factor is qualified by rights of way. The results showed that priority levels of the influencing factors in each moral dilemma were considerably different. Nevertheless, when the quantity of collision targets on two sides is equal, more participants prefer to protect the ones that comply with traffic rules. The research results can provide the basis for designing moral algorithms for the AVs.
收录类别:SCOPUS;SCIE;SSCI
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073934808&doi=10.1109%2fACCESS.2019.2932043&partnerID=40&md5=8936743cfaca766403d032ae9867ca7e
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