Nordhoff S
Department Transport and Planning, Delft University of Technology, Delft, The Netherlands.
Sci Rep. 2024 Apr 15;14(1):8654. doi: 10.1038/s41598-024-57882-6.
A better understanding of automation disengagements can lead to improved safety and efficiency of automated systems. This study investigates the factors contributing to automation disengagements initiated by human operators and the automation itself by analyzing semi-structured interviews with 103 users of Tesla's Autopilot and FSD Beta. The factors leading to automation disengagements are represented by categories. In total, we identified five main categories, and thirty-five subcategories. The main categories include human operator states (5), human operator's perception of the automation (17), human operator's perception of other humans (3), the automation's perception of the human operator (3), and the automation incapability in the environment (7). Human operators disengaged the automation when they anticipated failure, observed unnatural or unwanted automation behavior (e.g., erratic steering, running red lights), or believed the automation is not capable to operate safely in certain environments (e.g., inclement weather, non-standard roads). Negative experiences of human operators, such as frustration, unsafe feelings, and distrust represent some of the adverse human operate states leading to automation disengagements initiated by human operators. The automation, in turn, monitored human operators and disengaged itself if it detected insufficient vigilance or speed rule violations by human operators. Moreover, human operators can be influenced by the reactions of passengers and other road users, leading them to disengage the automation if they sensed discomfort, anger, or embarrassment due to the automation's actions. The results of the analysis are synthesized into a conceptual framework for automation disengagements, borrowing ideas from the human factor's literature and control theory. This research offers insights into the factors contributing to automation disengagements, and highlights not only the concerns of human operators but also the social aspects of this phenomenon. The findings provide information on potential edge cases of automated vehicle technology, which may help to enhance the safety and efficiency of such systems.
更好地理解自动化脱离可以提高自动化系统的安全性和效率。本研究通过分析对103名特斯拉Autopilot和FSD Beta用户的半结构化访谈，调查了导致人类操作员和自动化本身引发自动化脱离的因素。导致自动化脱离的因素按类别呈现。我们总共确定了五个主要类别和三十五个子类别。主要类别包括人类操作员状态（5个）、人类操作员对自动化的感知（17个）、人类操作员对其他人的感知（3个）、自动化对人类操作员的感知（3个）以及自动化在环境中的能力不足（7个）。当人类操作员预期会出现故障、观察到不自然或不需要的自动化行为（例如，不稳定转向、闯红灯），或者认为自动化在某些环境（例如，恶劣天气、非标准道路）中无法安全运行时，他们会脱离自动化。人类操作员的负面体验，如沮丧、不安全的感觉和不信任，是导致人类操作员引发自动化脱离的一些不利人类操作状态。反过来，自动化会监测人类操作员，如果检测到人类操作员警惕性不足或违反速度规则，就会自行脱离。此外，人类操作员可能会受到乘客和其他道路使用者反应的影响，如果他们因自动化的行为感到不适、愤怒或尴尬，就会导致他们脱离自动化。分析结果被综合成一个自动化脱离的概念框架，借鉴了人类因素文献和控制理论中的观点。这项研究深入探讨了导致自动化脱离的因素，不仅突出了人类操作员的担忧，还强调了这一现象的社会层面。研究结果提供了有关自动驾驶汽车技术潜在边缘情况的信息，这可能有助于提高此类系统的安全性和效率。
