Driving is a key feature of many individuals’ lifestyles. Automated vehicles (AVs) offer a range of potential benefits such as mobility solutions for those who cannot drive themselves, in the form of ride-sharing or autonomous taxi services.
AVs also provide plausible solutions to the issue of overcrowded highways as connected cars will communicate with each other and navigate an effective route based on real- time traffic information, making better use of road space by spreading demand (Department for Transport, 2015).
Situation Awareness in Remote Operators of Autonomous Vehicles
The introduction of autonomous vehicles could reduce the number of accidents that stem from errors in human judgement, making our roads safe. Many accidents are attributable to errors in Situation Awareness (SA). Situation awareness is,
“the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future” (Endsley, 1988, p. 792).
However, even entirely driverless vehicles will sometimes require remote human intervention.
Remote Operation
There are significant variations between situation awareness (SA) in normal driving contexts and SA in remote driving operations.
Remote Vehicle Operators (RVOs) will have limited prior awareness of the situation, and will be reliant on second-hand information from the scene.
“An RO who has been alerted by an AV to “drop in” and assess the problem or assume direct driving control will first need to acquire situation awareness (SA) of the remote scene. SA encompasses what is known about the environment, what is happening in it and what might change.” (Mutzenich, et al, 2021, page 1)
In the scenario of a remote ‘drop in’ to an AV, SA will be poorer and take longer to develop.
My research examines what information is necessary for remote operators to build remote SA quickly and accurately when they have not been monitoring the situation before taking over.
PhD Research Studies
Study 1
Study 1 investigated how participants build up mental representations of naturalistic remote driving scenes and whether the information provided from the rear-view footage is fundamental to that process. We took a novel approach to the SA Global Assessment Technique (SAGAT), using 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.
Go to Study1
Study 2
Study 2 investigated whether the presence of a rear-view mirror and the presence of audio delivered a more immersive experience, enhancing SA. We presented 16 videos counterbalanced across four conditions in a 2×2 factorial design (n=94) asking questions at the end of each video designed to measure each level of SA (perception, comprehension, and prediction). We suggest that existing theories of SA need to be more sensitively applied to remote driving contexts such as teleoperation of autonomous vehicles.
Go to Study2
Study 3
Study 3 tests the effect of different formats of presentation (either via a virtual reality (VR) headset or 360-degree screen-based presentation) on SA, measured by choice reaction time in response to videos of the types of edge cases that frequently challenge autonomous vehicles. It also employs eye tracking to explore how remote operators use information from the scene to build up situation awareness.
This study is currently testing in the lab.
Study 4
Study 4 will be a real-world project investigating remote operation in crowded environments for example a loading bay or valet parking. The details of the study will be confirmed based on the outcome/findings of Study 3 but potentially a VR study asking participants to remotely operate a robot at slow speeds conducting a series of manoeuvres. We will test which mode of display provides the best SA for remote operators.
Clare Mutzenich
Situation Awareness in Remote Operators of Autonomous Vehicles
SUPERVISORS
Professor Polly Dalton, Royal Holloway
Dr Szonya Durant, Royal Holloway
Dr Shaun Helman, Transport Research Laboratory (TRL)
Location
Royal Holloway, University of London
Egham, TWO20 5EX
Contact
+44 7725 747121
clare.mutzenich.2018@live.rhul.ac.uk
Clare Mutzenich 