The introduction of autonomous vehicles (AVs) could prevent many accidents attributable to human driver errors in Situation Awareness (SA). However, even entirely driverless vehicles will sometimes require remote human intervention. Current driving taxonomies and existing SA frameworks do not acknowledge the significant human factor challenges that are unique to a driver in charge of a vehicle that they are not physically occupying or continuously monitoring. Teleoperators will have to build up a mental model of the remote environment facilitated by monitor view and video feed.
The current study took a novel approach to the SA Global Assessment Technique (SAGAT), employing a qualitative verbal elicitation task, to investigate what people report from a remote scene when they are not constrained by rigid questioning. This enabled the construction of a taxonomy of SA in remote driving contexts. Participant transcripts suggested that acquiring SA in remote scenes is a flexible and fluctuating process that involves deriving, combining and updating the SA Levels of ‘comprehension’ and ‘prediction’ in parallel rather than serially, as has sometimes been implied by previous SAGAT methodologies (Endsley, 2000; Endsley, 2017; Jones & Endsley, 1996).
We suggest that existing theories of SA need to be more sensitively applied to remote driving contexts such as teleoperation of autonomous vehicles.