Vehicle automation and assistance technologies have been touted as a means to reduce traffic collisions by minimizing or eliminating “error-prone” and inefficient human operators. In concept, automation exists on a continuum that includes engaged driving by a human operator augmented by automated support features, vigilant driver monitoring of vehicle behavior with the possibility of driver take-over, to full automation with no active monitoring by a human operator. Moreover, the degree of automation varies by vehicle features (e.g., lane centering, emergency braking, adaptive cruise control, parking), by setting, meaning that automated features may or may not be available depending on specific attributes of the traffic environment (e.g., traffic volume, road geometry, etc), and by implementation (e.g., haptic vs. auditory warnings). Thus, these automotive “transportation tools” are highly heterogeneous and pose unique challenges and opportunities for driver training. In this paper, we report the results of an experimental study (n = 36) to determine if enhanced vehicle feedback influences driver trust, effort, frustration, and performance (indexed by reaction time) in a virtual driving environment. Results are contextualized in the extant literature on learning to operate motor vehicles and outline key research questions essential for understanding the processes by which skilled performance develops with respect to a real-world practical tool: the increasingly automated automobile.

中文翻译：

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交通工具：通过联网车辆学习

车辆自动化和辅助技术被吹捧为通过最小化或消除“容易出错”和效率低下的人工操作员来减少交通碰撞的一种手段。从概念上讲，自动化存在于一个连续统一体上，包括由人工操作员参与驾驶并通过自动支持功能增强、驾驶员对车辆行为的警惕性监控以及驾驶员接管的可能性，以及没有人工操作员主动监控的完全自动化。此外，自动化程度因车辆功能（例如，车道居中、紧急制动、自适应巡航控制、停车）和设置而异，这意味着自动化功能可能会或可能不会根据交通环境的特定属性（例如，交通量、道路几何形状等），以及通过实施（例如，触觉与听觉警告）。因此，这些汽车“运输工具”高度多样化，为驾驶员培训带来了独特的挑战和机遇。在本文中，我们报告了一项实验研究的结果（n = 36）以确定增强的车辆反馈是否会影响虚拟驾驶环境中的驾驶员信任、努力、挫折和表现（以反应时间为索引）。结果在现有的关于学习操作机动车辆的文献中进行了背景化，并概述了关键研究问题，这些问题对于理解技能表现相对于现实世界实用工具（日益自动化的汽车）的发展过程至关重要。