Mutzenich Clare, Durant Szonya, Helman Shaun, Dalton Polly
Royal Holloway, University of London, London, United Kingdom.
Transport Research Laboratory, Crowthorn, United Kingdom.
Front Psychol. 2021 Nov 10;12:727500. doi: 10.3389/fpsyg.2021.727500. eCollection 2021.
Even entirely driverless vehicles will sometimes require remote human intervention. Existing SA frameworks do not acknowledge the significant human factors challenges unique to a driver in charge of a vehicle that they are not physically occupying. Remote operators will have to build up a mental model of the remote environment facilitated by monitor view and video feed. We took a novel approach to "freeze and probe" techniques to measure SA, employing a qualitative verbal elicitation task to uncover what people "see" in a remote scene when they are not constrained by rigid questioning. Participants ( = ) watched eight videos of driving scenes randomized and counterbalanced across four road types (motorway, rural, residential and A road). Participants recorded spoken descriptions when each video stopped, detailing what was happening (SA Comprehension) and what could happen next (SA Prediction). Participant transcripts provided a rich catalog of verbal data reflecting clear interactions between different SA levels. This suggests that acquiring SA in remote scenes is a flexible and fluctuating process of combining comprehension and prediction globally rather than serially, in contrast to what has sometimes been implied by previous SA methodologies (Jones and Endsley, 1996; Endsley, 2000, 2017b). Inductive thematic analysis was used to categorize participants' responses into a taxonomy aimed at capturing the key elements of people's reported SA for videos of driving situations. We suggest that existing theories of SA need to be more sensitively applied to remote driving contexts such as remote operators of autonomous vehicles.
即使是完全无人驾驶的车辆有时也需要远程人工干预。现有的态势感知框架并未认识到，对于未实际乘坐的车辆的驾驶员而言，存在一些独特的重大人为因素挑战。远程操作员将不得不借助监控视图和视频馈送来构建远程环境的心理模型。我们采用了一种新颖的“冻结与探测”技术来测量态势感知，运用定性的言语启发任务来揭示人们在不受严格问题限制时在远程场景中“看到”了什么。参与者（(n = )）观看了八个驾驶场景视频，这些视频在四种道路类型（高速公路、乡村道路、住宅道路和A级公路）上随机排列并进行了平衡处理。每个视频停止时，参与者记录口头描述，详细说明正在发生的事情（态势感知理解）以及接下来可能发生的事情（态势感知预测）。参与者的文字记录提供了丰富的言语数据目录，反映了不同态势感知水平之间的清晰交互。这表明在远程场景中获取态势感知是一个灵活且波动的过程，是在全球范围内而非按顺序地将理解和预测相结合，这与先前的态势感知方法（琼斯和恩德斯利，1996年；恩德斯利，2000年、2017年b）有时所暗示的情况形成对比。归纳主题分析用于将参与者的回答分类为一种分类法，旨在捕捉人们报告的驾驶情境视频的态势感知的关键要素。我们建议，现有的态势感知理论需要更灵活地应用于远程驾驶情境，如自动驾驶车辆的远程操作员。
