Deep reinforcement learning (DRL) has been widely adopted recently for its ability to solve decision-making problems that were previously out of reach due to a combination of nonlinear and high dimensionality. In the last few years, it has spread in the field of air traffic control (ATC), particularly in conflict resolution. In this work, we conduct a detailed review of existing DRL applications for conflict resolution problems. This survey offered a comprehensive review based on segments as (1) fundamentals of conflict resolution, (2) development of DRL, and (3) various applications of DRL in conflict resolution classified according to environment, model, algorithm, and evaluating indicator. Finally, an open discussion is provided that potentially raises a range of future research directions in conflict resolution using DRL. The objective of this review is to present a guidance point for future research in a more meaningful direction.

As a forward-looking development in air traffic control (ATC), remote towers rely on virtualized information presentation, which may exacerbate visual fatigue among controllers and compromise operational safety. This study proposes a visual fatigue recognition model based on multimodal physiological signals. A 60-min simulated remote tower task was conducted with 36 participants, during which eye-tracking (ET), electroencephalography (EEG), electrocardiography (ECG), and electrodermal activity (EDA) signals were collected. Subjective fatigue questionnaires and objective ophthalmic measurements were also recorded before and after the task. Statistically significant features were identified through paired t-tests, and fatigue labels were constructed by combining subjective and objective indicators. LightGBM was then employed to rank feature importance by integrating split frequency and information gain into a composite score. The top 12 features were selected and used to train a multilayer perceptron (MLP) for classification. The model achieved an average balanced accuracy of 0.92 and an F1 score of 0.90 under 12-fold cross-validation, demonstrating excellent predictive performance. The high-ranking features spanned four modalities, revealing typical physiological patterns of visual fatigue across ocular behavior, cortical activity, autonomic regulation, and arousal level. These findings validate the effectiveness of multimodal fusion in modeling visual fatigue and provide theoretical and technical support for human factor monitoring and risk mitigation in remote tower environments.

Fatigue in air traffic controllers (ATCOs), particularly within remote tower operations, poses a substantial risk to aviation safety due to its detrimental effects on vigilance, decision-making, and situational awareness. While electroencephalography (EEG) provides a promising avenue for objective fatigue monitoring, existing models often fail to adequately capture both the spatial dependencies across brain regions and the temporal dynamics of cognitive states. To address this challenge, we propose a novel EEG-based fatigue detection framework, Spatio-Temporal Graph with Center Loss Network (STG-CLNet), which jointly models topological brain connectivity and temporal EEG evolution. The model leverages a multi-stage graph convolutional network to encode spatial dependencies and a triple-layer LSTM module to capture temporal progression, while incorporating center loss to enhance feature discriminability in the embedding space. We constructed a domain-specific EEG dataset involving 34 ATCO participants operating in high- and low-traffic remote tower simulations, with fatigue labels derived from three validated subjective metrics. Experimental results demonstrate that STG-CLNet achieves superior classification performance (accuracy = 96.73%, recall = 92.01%, F1-score = 87.15%), outperforming several strong baselines, including LSTM and EEGNet. These findings underscore the potential of STG-CLNet for integration into real-time cognitive monitoring systems in air traffic control, contributing to both theoretical advancement and operational safety enhancement.

Artificial intelligence for aircraft guidance is a hot research topic, and deep reinforcement learning is one of the promising methods. However, due to the different movement patterns of destinations in different guidance tasks, it is inefficient to train agents from scratch. In this article, a policy-reuse algorithm based on destination position prediction is proposed to solve this problem. First, the reward function is optimized to improve flight trajectory quality and training efficiency. Then, by predicting the possible termination position of the destinations in different moving patterns, the problem is transformed into a fixed-position destination aircraft guidance problem. Last, taking the agent in the fixed-position destination scenario as the baseline agent, a new guidance agent can be trained efficiently. Simulation results show that this method can significantly improve the training efficiency of agents in new tasks, and its performance is stable in tasks with different similarities. This research broadens the application scope of the policy-reuse approach and also enlightens the research in other fields.

Real-time detection of fatigue states in air traffic controllers (ATCOs) is crucial for ensuring air traffic safety. Existing methods exhibit limitations such as poor real-time performance, intrusiveness, and susceptibility to lighting and occlusion. This paper proposes FF-YOLO, an improved YOLO11-based deep learning algorithm, to detect ATCO fatigue states through facial feature analysis. A custom dataset comprising 25,154 facial images collected from 10 ATCOs was constructed for model training and validation. The FF-YOLO model introduces the CA-C3K2 module for fine-grained feature extraction under complex lighting, incorporates a spatial–channel attention mechanism for improved detection accuracy during occlusion, and MPDIoU loss for enhanced accuracy on multi-scale facial images with accelerated convergence. Experimental results show FF-YOLO achieves 94.2% mAP@50, 74.7% mAP@50–95, 83.8% precision, and 73.8% recall, with gains of +13.7%, +11.6%, +0.6%, and +5.9% over YOLO11n, respectively, thereby enabling real-time and accurate detection of ATCO fatigue states. Future work will expand datasets with larger and more diverse ATCO populations to enhance generalizability.

To enhance flight safety and operational efficiency, extracting key actions, flight parameters, and status information from civil aviation flight crew instructions generated during pre-flight and in-flight procedures is crucial. However, such texts are highly condensed and involve complex multi-role interactions, easily leading to entity boundary drift and category misclassification. To address this, this paper proposes a joint key information extraction framework based on a lightweight pre-trained language model (TinyBERT) and a Role-Aware Fusion mechanism, abbreviated as TR-BiGRU-CRF. This framework introduces the Role-Aware Fusion mechanism to resolve semantic ambiguity caused by multi-party interactions, utilizes TinyBERT for semantic representation that balances accuracy and computational efficiency, and employs BiGRU-CRF for robust sequence feature modeling and decoding. Experiments on a flight crew instruction dataset show that the proposed method achieves 92.2% precision, 91.8% recall, a 92.0% F1 score, and an overall prediction accuracy of 92.6%. Compared to the BiGRU-CRF baseline, it significantly improves accuracy, precision, and F1 score by 11.4, 13.3, and 13.5 percentage points, respectively. These results prove that the proposed method effectively mitigates boundary drift and category confusion, providing strong support for flight crew instruction understanding and safety decision-making.