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Highly Automated Driving in the Real World - A Wizard-of-Oz Study on User Experience and Behavior

机译:在现实世界中高度自动驾驶 - 对用户体验和行为的思想型研究

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Past research on human factors of automated driving has primarily focused on human performance aspects in takeover situations after partially or conditionally automated driving periods (SAE L2/3). In this study we investigated user experience and behavioral effects of driving in a highly automated (SAE L4) vehicle in mixed traffic on public roads without unplanned takeovers. The automation feature itself was realized by means of a Wizard-of-Oz approach, involving two trained operators for vehicle and HMI control. Twelve test participants were recruited to experience a series of four highly automated drives of about 30 minutes each. The main research questions were related to how driver attitudes towards automation and system trust develop over time as well as how this affects user behavior with respect to visual attention and engagement in non-driving related tasks (NDRT). The results show that the participants appreciated the automation feature for all road types (urban, rural and motorway scenarios). Positive attitudes towards automation started at an already high level and increased slightly over the course of the driving sessions. Manual analysis of the participants' individual behavior revealed large inter-individual differences for "total time eyes off road" and "frequency of glances towards driving related HMI". Participants did engage in NDRTs between 0 and 93% of the driving time, almost regardless of the driving environment. The explorative study also highlights the benefits and drawbacks of this particular research method. Although the Wizard-of-Oz illusion perfectly worked for all study participants, some critical issues remain to be solved for future applications.
机译:过去关于自动化驾驶的人为因素的研究主要集中在部分或有条件地自动化驱动期之后的收购情况(SAE L2 / 3)中的人类性能方面。在这项研究中,我们调查了在没有计划生表的公共道路上在公共道路上的高度自动化(SAE L4)车辆中驾驶的用户体验和行为效果。自动化功能本身是通过oz-oz-oz方法实现的,涉及用于车辆和HMI控制的两个训练有素的操作员。招募了十二个测试参与者,以体验一系列四个高度自动化的驱动器,约30分钟。主要的研究问题与司机对自动化和系统信任的态度如何发展时间以及如何影响用户行为,以及在非驾驶相关任务(NDRT)中的视觉关注和参与。结果表明,参与者赞赏所有道路类型的自动化功能(城市,农村和高速公路方案)。对自动化的积极态度从已经高的水平开始,在驾驶课程的过程中略有增加。对参与者的单个行为进行手动分析显示“总时光偏离道路”和“驾驶相关HMI的途径频率”的巨大间间差异。参与者确实在0到93%的行车时间之间进行了NDRT,几乎无论驾驶环境如何。探索性研究还突出了这种特定研究方法的益处和缺点。虽然oz-oz-oz-of-oz幻觉为所有学习参与者提供了完美的工作,但仍有一些关键问题仍有待解决为未来的应用程序。

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