Situation Awareness in Remote Operators of Autonomous Vehicles: Developing a Taxonomy of Situation Awareness in Video-Relays of Driving Scenes

Mutzenich, Clare; Durant, Szonya; Helman, Shaun; Dalton, Polly · 2021 · OpenAlex-citations

DOI: 10.3389/fpsyg.2021.727500

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Summary

This study addresses the lack of standardized frameworks for assessing Situation Awareness (SA) in remote operators (ROs) of autonomous vehicles (AVs). While fully driverless vehicles may eventually require human intervention for edge cases, existing SA models, such as Endsley’s three-level hierarchy, were developed for in-situ drivers or military pilots and do not adequately account for the unique challenges of remote operation via video relays. The authors argue that remote SA is a flexible, fluctuating process rather than a serial one, necessitating a new taxonomy to capture how operators build mental models from limited 2D video feeds. To develop this taxonomy, the researchers employed a qualitative "freeze and probe" methodology using verbal elicitation. Ten participants with UK driving licenses viewed eight naturalistic driving videos across four road types (motorway, rural, residential, and A roads). The study used a within-subjects design where each participant watched four videos with a rear-view camera insert ("rear-view present") and four without ("rear-view absent"), counterbalanced to control for road type effects. At the end of each video, participants verbally described what was happening (SA Comprehension) and what might happen next (SA Prediction). The audio recordings were transcribed and analyzed using inductive thematic analysis to identify recurring patterns in participants' reports without imposing pre-existing theoretical constraints. The analysis yielded a comprehensive taxonomy of SA elements derived directly from participant descriptions. The findings revealed that participants’ reports demonstrated clear interactions between comprehension and prediction, suggesting that remote SA acquisition involves combining these levels globally rather than sequentially. The study also assessed inter-rater reliability to ensure the taxonomy’s robustness, achieving high agreement between coders. The results highlight that remote operators rely on specific semantic information from video feeds to construct their mental models, distinct from the sensory inputs available to in-situ drivers. The significance of this work lies in providing a foundational framework for understanding remote driving SA. By moving beyond rigid quantitative metrics like the Situation Awareness Global Assessment Technique (SAGAT), this taxonomy captures the nuanced, subjective nature of remote perception. The authors conclude that existing SA theories must be sensitively adapted for remote contexts. This taxonomy is intended to inform the design of remote operator interfaces, training programs, and future quantitative measures, ultimately supporting the safe integration of human oversight in highly automated driving systems.

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Topics

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• situational awareness
• teleoperation remote driving
• situation awareness theory
• driverless ads
• anticipation
• mode awareness

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• Synthesis & Review: research agenda
• Theoretical Contribution: conceptual framework, theory or model