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This study investigates the impact of multi-task type and takeover method on task performance, with a particular focus on situational awareness support in modern armored vehicles and unmanned swarm combat environments, especially in the context of closed-cabin environment monitoring by drones. The results reveal that takeover method significantly affects task performance, with passive takeover outperforming active takeover in tasks such as multi-target recognition (T2) and multi-guided strike (T3), suggesting that automated systems are more advantageous in complex tasks. The multi-task type also had a significant effect on task performance, with path planning (T1) tasks being more suited for active takeover, while recognition and strike tasks rely more on passive takeover. Moreover, the situational awareness challenges in multi-task environments further expose the limitations of traditional interface designs, particularly in closed-cabin environments where drone monitoring tasks face the challenge of integrating multi-source sensor data. These dynamic and complex task environments increase operators’ cognitive load, and traditional interface designs fail to address this issue effectively. The study suggests that optimizing information presentation and interaction feedback mechanisms through adaptive interaction design can significantly improve operators’ situational awareness and decision-making efficiency in complex battlefield environments. This research proposes a method based on output-layer adaptive interaction design, aiming to enhance situational awareness in multi-task environments through the optimization of information presentation and dynamic adjustment strategies, particularly in the context of drone monitoring in closed-cabin environments. Future research should continue exploring the integration of artificial intelligence technologies with adaptive interaction design to further enhance situational awareness and operational efficiency in multi-task scenarios, particularly in the complex contexts of unmanned swarm combat and battlefield monitoring.

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Adaptive Interaction Design for Situational Awareness in Multi-task Scenarios

  • 会议论文

  • 发表日期：

    2025年

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  • Dapeng Wei,
  • Miaomiao Fan,
  • Zhaoyang Mi,
  • Ruojuan Li

摘要

This study investigates the impact of multi-task type and takeover method on task performance, with a particular focus on situational awareness support in modern armored vehicles and unmanned swarm combat environments, especially in the context of closed-cabin environment monitoring by drones. The results reveal that takeover method significantly affects task performance, with passive takeover outperforming active takeover in tasks such as multi-target recognition (T2) and multi-guided strike (T3), suggesting that automated systems are more advantageous in complex tasks. The multi-task type also had a significant effect on task performance, with path planning (T1) tasks being more suited for active takeover, while recognition and strike tasks rely more on passive takeover. Moreover, the situational awareness challenges in multi-task environments further expose the limitations of traditional interface designs, particularly in closed-cabin environments where drone monitoring tasks face the challenge of integrating multi-source sensor data. These dynamic and complex task environments increase operators’ cognitive load, and traditional interface designs fail to address this issue effectively. The study suggests that optimizing information presentation and interaction feedback mechanisms through adaptive interaction design can significantly improve operators’ situational awareness and decision-making efficiency in complex battlefield environments. This research proposes a method based on output-layer adaptive interaction design, aiming to enhance situational awareness in multi-task environments through the optimization of information presentation and dynamic adjustment strategies, particularly in the context of drone monitoring in closed-cabin environments. Future research should continue exploring the integration of artificial intelligence technologies with adaptive interaction design to further enhance situational awareness and operational efficiency in multi-task scenarios, particularly in the complex contexts of unmanned swarm combat and battlefield monitoring.