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Human Autonomy in Future Drone Traffic: Joint Human-AI Control in Temporal Cognitive Work
Jonas Lundberg1, Mattias Arvola2, Karljohan Lundin Palmerius1
1Department of Science and Technology, Linköping University, Norrköping, Sweden.
Frontiers in Artificial Intelligence
|August 6, 2021
Summary
Human autonomy in autonomous systems involves trade-offs between automation capabilities and meaningful human roles. Understanding these dynamics is key for designing future human-computer collaboration in areas like drone traffic management.
Area of Science:
- Human-Computer Interaction
- Cognitive Systems Engineering
- Automation Studies
Background:
- Increasing computer intelligence and autonomy are transforming human operator roles.
- Future human-system interaction will be more supervisory or situational.
- Reconsidering human autonomy alongside autonomous systems is crucial.
Purpose of the Study:
- To describe a design case for a future autonomous drone traffic management system.
- To explore human collaboration with autonomous systems using scenario-based design and cognitive work analysis.
- To analyze temporal aspects of human and automated work in relation to autonomy.
Main Methods:
- Scenario-based design and cognitive work analysis.
- Computer simulations for designing human-autonomous system collaboration.
- The Joint Control Framework (JCF) and Score notation for temporal analysis.
Main Results:
- Developed a design case for autonomous urban drone traffic management.
- Analyzed human and automated work using Levels of Autonomy in Cognitive Control.
- Identified temporal patterns in autonomy across abstraction hierarchy levels.
- Discussed how lower-level autonomy can impact higher-level autonomy and vice versa.
Conclusions:
- Human autonomy in autonomous systems is defined by trade-offs between automation potential and human values.
- The temporal nature of autonomy is critical in minute-to-minute operative work.
- Effective human-automation communication is essential for traffic management systems.
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