Human-machine collaboration is a key feature of Single Pilot Operations (SPO). With only a single pilot in the cockpit, workload monitoring and adjustment become even more critical compared to Dual-Pilot Operations (DPO). Hence, a dynamic function allocation mechanism must be established—increasing the Level of Automation (LOA) under high workload conditions and reducing it under low workload conditions to maintain situational awareness. To address the challenges of excessive subjectivity and limited knowledge transfer in the existing dynamic function allocation methods, this paper proposes a dynamic function allocation method based on Bayesian-enhanced Q-Learning (BQL). First, a Bayesian Network (BN) is constructed to predict Human-Machine System (HMS) performance, determining when reallocation should be triggered. Compared to the existing trigger mechanisms, this approach enables earlier activation while maintaining non-intrusive. Then, the BN-predicted HMS performance is integrated into the reward feedback for the reinforcement learning algorithm, allowing the system to continuously refine its strategy through interaction with the environment. Finally, flight experiments conducted in a low-fidelity SPO simulator, incorporating both objective physiological monitoring and subjective assessments, validate the effectiveness of the proposed method.

Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.

Single Pilot Operations (SPO), as a NextGen concept of operation, can save both crew costs and human resources for airlines, and has attracted the attention of aviation researchers. To explore in advance the problems that the introduction of SPO into the aviation system would bring, the Human-Centered Design (HCD) approach has been widely used in the development of SPO. A systematic review of the progress of HCD approach in SPO research can promote further development of SPO. In this paper, the literature resources of SPO were firstly retrieved from scientific research databases by subject search and were used as the input of scientometric analysis to obtain the highly cited literature, the number of annual publications, and the co-authorship network, which enables readers to understand the research trends and research groups of current SPO. Secondly, the development, application, and research process of the HCD approach were introduced in detail, and the progress of the HCD approach in SPO research was reviewed systematically from three aspects: concept design, function allocation, and system evaluation. Finally, limitations of current SPO research and future research directions for applying the HCD approach to SPO were also discussed.

With the continuous advancement of the avionics system, crew members are correspondingly reduced, and Single Pilot Operations (SPO) has attracted widespread attention from scholars. To meet the flight requirements in SPO mode, it is necessary to further strengthen air-ground coordination system integration, but at the same time, there will be some safety issues caused by resource integration, function fusion, and task synthesis. Aimed at the safety problems caused by task synthesis, an efficient differential bicluster mining algorithm--DFCluster algorithm is proposed in this paper to discover potential hazardous elements or propagation mechanisms through mining the resource-function matrixes. To mine efficiently, several pruning techniques are designed for generating maximal biclusters without candidate maintenance. The experimental results show that the DFCluster algorithm is more efficient than the existing differential biclustering algorithms under different scales of artificial datasets and public datasets. Then, a typical flight scenario is designed based on SPO air-ground collaborative system architecture, and combined with our proposed DFCluster algorithm for task synthesis safety analysis. Based on the mining results, the SPO air-ground collaborative system architecture is modified, which ultimately improves the safety of the SPO system.