## 6 Conclusions  
Realizing accurate identification of pilot intention based on physiological signals is an important prerequisite for achieving the harmonious interaction between pilot, aircraft, and flight environment under the intelligent autopilot conditions. At the same time, it is not only the basic requirement for the development of advanced autopilot systems and intelligent cockpits, but an important part of the microscopic research on flight safety. Pilot intention plays an important role in behavior decision-making. Therefore, it is very important to introduce the evolutionary mechanism of intention into the autopilot systems. In this paper, based on the analysis of a large amount of experimental data, the ESAX method and intelligent icon theory are used to extract the combination of physiological features. Then, the crow search algorithm and extreme learning machine are combined to establish a contactless online identification model of pilot intention. Hence, the \"intention generation\" in the dynamic evolution mechanism of the pilot's intention has been accurately identified online. In the theory, the research results of this paper provide new ideas for further intention identification. This study enriches the pilot intention parameter set and lays a foundation for more scholars to carry out real-time online pilot intention identification research. In application, it provides a theoretical basis for the development of \"people-oriented\" safety monitoring and early-warning systems. At the same time, it lays a theoretical fundamental for the study of interaction between intention and micro-features and the intelligitization of aircraft cockpits. Moreover, it provides a basis for further research on human-aircraft-environment intelligent interaction. It is of great practical significance to research the future active safety warnings for flight, pilot micro-behavior assessment, and flight safety.  
Conceptualization, H.W., T.P. and H.S.; methodology, H.W., T.P. and H.Z.; software, H.W. and L.S.; validation, H.W., T.P. and H.L.; investigation, H.W., T.P. and H.Z.; data curation, T.P., L.S. and H.S.; writing--original draft preparation, H.W., T.P. and H.S.; writing--review and editing, T.P. and H.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant NS2020051, in part by the first batch of industry-university-research cooperative collaborative education projects of the Ministry of Education in 2021 under Grant 202101042005, the Jinta Technology-Ergonomics Society \"Human Factors and Ergonomics\" project, in part by the Civil Aircraft Simulated Flight Test Data Procurement Project under Grant NNW2020-JT01-039, in part by the Research on Safety Risk Assessment Technology and Method of Human-Computer Intelligent Interaction in Civil Aircraft Cockpit under Grant U2033202, in part by the \"Experimental technology research and development project\" of Nanjing University of Aeronautics and Astronautics under Grant SYJS2020207Y, and in part by Nanjing University of Aeronautics and Astronautics PhD short-term visiting scholar project under Grant ZDGB2021024.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of College of Civil Aviation, Nanjing University of Aeronautics and Astronautics.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.  
**Conflicts of Interest:** The authors declare no conflict of interest.