Attiqur Rehman, A. Ghaffarianhoseini, N. Naismith, Abdulbasit Almhafdy, Amirhosein Ghaffarianhoseini, J. Tookey, Shafiq Urrehman
{"title":"Development of trust-based autonomous driving framework in New Zealand","authors":"Attiqur Rehman, A. Ghaffarianhoseini, N. Naismith, Abdulbasit Almhafdy, Amirhosein Ghaffarianhoseini, J. Tookey, Shafiq Urrehman","doi":"10.1108/sasbe-04-2023-0086","DOIUrl":null,"url":null,"abstract":"PurposeAutonomous vehicles (AVs) have the potential to transform the infrastructure, mobility and social well-being paradigms in New Zealand (NZ) amid its unprecedented population and road safety challenges. But, public acceptance, co-evolution of regulations and AV technology based on interpersonal and institutional trust perspectives pose significant challenges. Previous theories and models need to be more comprehensive to address trust influencing autonomous driving (AD) factors in natural settings. Therefore, this study aims to find key AD factors corresponding to the chain of human-machine interaction (HMI) events happening in real time and formulate a guiding framework for the successful deployment of AVs in NZ.Design/methodology/approachThis study utilized a comprehensive literature review complemented by an AV users’ study with 15 participants. AV driving sprints were conducted on low, medium and high-density roads in Auckland, followed by 15 ideation workshops to gather data about the users’ observations, feelings and attitudes towards the AVs during HMI.FindingsThis research study determined nine essential trust-influencing AD determinants in HMI and legal readiness domains. These AD determinants were analyzed, corresponding to eight AV events in three phases. Subsequently, a guiding framework was developed based on these factors, i.e. human-machine interaction autonomous driving events relationship identification framework (HMI-ADERIF) for the deployment of AVs in New Zealand.Research limitations/implicationsThis study was conducted only in specific Auckland areas.Practical implicationsThis study is significant for advanced design research and provides valuable insights, guidelines and deployment pathways for designers, practitioners and regulators when developing HMI Systems for AD vehicles.Originality/valueThis study is the first-ever AV user study in New Zealand in live traffic conditions. This user study also claimed its novelty due to AV trials in congested and fast-moving traffic on the four-lane motorway in New Zealand. Previously, none of the studies conducted AV user study on SUV BMW vehicle and motorway in real-time traffic conditions; all operations were completely autonomous without any input from the driver. Thus, it explored the essential autonomous driving (AD) trust influencing variables in human factors and legal readiness domains. This research is also unique in identifying critical AD determinants that affect the user trust, acceptance and adoption of AVs in New Zealand by bridging the socio-technical gap with futuristic research insights.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart and Sustainable Built Environment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/sasbe-04-2023-0086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
PurposeAutonomous vehicles (AVs) have the potential to transform the infrastructure, mobility and social well-being paradigms in New Zealand (NZ) amid its unprecedented population and road safety challenges. But, public acceptance, co-evolution of regulations and AV technology based on interpersonal and institutional trust perspectives pose significant challenges. Previous theories and models need to be more comprehensive to address trust influencing autonomous driving (AD) factors in natural settings. Therefore, this study aims to find key AD factors corresponding to the chain of human-machine interaction (HMI) events happening in real time and formulate a guiding framework for the successful deployment of AVs in NZ.Design/methodology/approachThis study utilized a comprehensive literature review complemented by an AV users’ study with 15 participants. AV driving sprints were conducted on low, medium and high-density roads in Auckland, followed by 15 ideation workshops to gather data about the users’ observations, feelings and attitudes towards the AVs during HMI.FindingsThis research study determined nine essential trust-influencing AD determinants in HMI and legal readiness domains. These AD determinants were analyzed, corresponding to eight AV events in three phases. Subsequently, a guiding framework was developed based on these factors, i.e. human-machine interaction autonomous driving events relationship identification framework (HMI-ADERIF) for the deployment of AVs in New Zealand.Research limitations/implicationsThis study was conducted only in specific Auckland areas.Practical implicationsThis study is significant for advanced design research and provides valuable insights, guidelines and deployment pathways for designers, practitioners and regulators when developing HMI Systems for AD vehicles.Originality/valueThis study is the first-ever AV user study in New Zealand in live traffic conditions. This user study also claimed its novelty due to AV trials in congested and fast-moving traffic on the four-lane motorway in New Zealand. Previously, none of the studies conducted AV user study on SUV BMW vehicle and motorway in real-time traffic conditions; all operations were completely autonomous without any input from the driver. Thus, it explored the essential autonomous driving (AD) trust influencing variables in human factors and legal readiness domains. This research is also unique in identifying critical AD determinants that affect the user trust, acceptance and adoption of AVs in New Zealand by bridging the socio-technical gap with futuristic research insights.
目的在新西兰,自动驾驶汽车(AVs)有可能改变基础设施、机动性和社会福利模式,因为新西兰面临前所未有的人口和道路安全挑战。但是,基于人际和机构信任视角的公众接受度、法规和自动驾驶汽车技术的共同发展构成了重大挑战。以往的理论和模型需要更加全面,以解决在自然环境中影响自动驾驶(AD)因素的信任问题。因此,本研究旨在找到与实时发生的人机交互(HMI)事件链相对应的关键自动驾驶因素,并为在新西兰成功部署 AV 制定指导框架。在奥克兰的低、中、高密度道路上进行了 AV 驾驶冲刺,随后举行了 15 次构思研讨会,以收集用户在人机交互过程中对 AV 的观察、感受和态度方面的数据。对这些影响信任的决定因素进行了分析,这些因素与三个阶段中的八个视听事件相对应。随后,根据这些因素制定了一个指导框架,即人机交互自动驾驶事件关系识别框架(HMI-ADERIF),用于在新西兰部署自动驾驶汽车。研究局限性/影响本研究仅在奥克兰特定地区进行。实践意义本研究对高级设计研究具有重要意义,为设计人员、从业人员和监管人员开发自动驾驶汽车的人机交互系统提供了宝贵的见解、指南和部署途径。在新西兰的四车道高速公路上,在拥堵和快速行驶的交通中进行 AV 试验,也是这项用户研究的新颖之处。在此之前,没有任何一项研究对宝马 SUV 汽车和高速公路的实时交通状况进行过 AV 用户研究;所有操作都是完全自主的,无需驾驶员的任何输入。因此,该研究探讨了在人为因素和法律准备领域中影响自动驾驶(AD)信任度的基本变量。这项研究也是独一无二的,通过弥合社会技术差距与未来研究见解,确定了影响新西兰用户对自动驾驶汽车的信任、接受和采用的关键自动驾驶决定因素。