Towards pilot's situation awareness enhancement: A framework of adaptive interaction system and its realization. (January 2023) - Yu, Weiwei; Jin, Dian; Zhao, Feng; Zhang, Xiaokun
Abstract: Adaptive interaction system in flight control always aims to enhance the pilot's situation awareness (SA) to achieve human-in-the-loop control. Most adaptive interaction systems are always activated according to the pilot's current workload state. However, the pilot may already lose important information during a high workload, and thus the corresponding reaction of the adaptive interaction system would lag. Moreover, most adaptive interaction systems adopt the expert's knowledge as a reference to generate information. Still, the tacit knowledge that reveals the information interrelationship is seldom studied, despite being the foundation of the interactive information display. To solve the above problems, we proposed an adaptive interaction system architecture with three subsystems. Firstly, we developed a workload level prediction subsystem, where physiological parameters are used to predict future workload levels, thus avoiding interaction system lag; Secondly, we developed a tacit expert knowledge mining subsystem to discover the interrelationship hidden in the expert's perceived information, which will guide the interactive information interface. Thirdly, we developed a tips information inference subsystem to provide the lost SA information based on expert knowledge and the pilot's online perceived information. The effectiveness of the proposed system is verified via a comparative experiment utilizing the control interface of a remotely piloted aircraft. Highlights: An adaptive interaction system is constructed to enhance the pilot's SA. Tacit knowledge is mined based on the visual cognitive graph to guide information display. Future mental workload level is predicted to trigger the interaction system. Tips information is inferred online to generate the lost SA information.