University of Iowa, Iowa City, USA.
Harvey Mudd College, Claremont, CA, USA.
Appl Ergon. 2024 Nov;121:104346. doi: 10.1016/j.apergo.2024.104346. Epub 2024 Jul 16.
Integrating Artificial Intelligence (AI) and drone technology into bridge inspections offers numerous advantages, including increased efficiency and enhanced safety. However, it is essential to recognize that this integration changes the cognitive ergonomics of the inspection task. Gaining a deeper understanding of how humans process information and behave when collaborating with drones and AI systems is necessary for designing and implementing effective AI-assisted inspection drones. To further understand human-drone-AI intricate dynamics, an experiment was conducted in which participants' biometric and behavioral data were collected during a simulated drone-enabled bridge inspection under two conditions: with an 80% accurate AI assistance and with no AI assistance. Results indicate that cognitive and behavioral factors, including vigilance, cognitive processing intensity, gaze patterns, and visual scanning efficiency can influence inspectors' performance respectively in either condition. This highlights the importance of designing inspection protocols, drones and AI systems based on a comprehensive understanding of the cognitive processes required in each condition to prevent cognitive overload and minimize errors. We also remark on the visual scanning and gaze patterns associated with a higher chance of missing critical information in each condition, insights that inspectors can use to enhance their inspection performance.
将人工智能（AI）和无人机技术集成到桥梁检查中具有许多优势，包括提高效率和增强安全性。然而，必须认识到，这种集成改变了检查任务的认知工效学。为了设计和实施有效的 AI 辅助检查无人机，有必要更深入地了解人类在与无人机和 AI 系统协作时如何处理信息和行为。为了进一步了解人机-无人机-AI 的复杂动态，进行了一项实验，在模拟的无人机辅助桥梁检查中，参与者的生物识别和行为数据在两种情况下进行了收集：AI 辅助准确率为 80%和没有 AI 辅助。结果表明，认知和行为因素，包括警觉性、认知处理强度、注视模式和视觉扫描效率，可能会分别影响检查人员在任何一种情况下的表现。这突出了基于对每种情况下所需认知过程的全面理解来设计检查协议、无人机和 AI 系统的重要性，以防止认知过载和最小化错误。我们还注意到与在每种情况下错过关键信息的可能性较高相关的视觉扫描和注视模式，这些见解可被检查人员用于提高其检查性能。
