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.

In a one-on-one air combat game, the opponent’s maneuver strategy is usually not deterministic, which leads us to consider a variety of opponent’s strategies when designing our maneuver strategy. In this paper, an alternate freeze game framework based on deep reinforcement learning is proposed to generate the maneuver strategy in an air combat pursuit. The maneuver strategy agents for aircraft guidance of both sides are designed in a flight level with fixed velocity and the one-on-one air combat scenario. Middleware which connects the agents and air combat simulation software is developed to provide a reinforcement learning environment for agent training. A reward shaping approach is used, by which the training speed is increased, and the performance of the generated trajectory is improved. Agents are trained by alternate freeze games with a deep reinforcement algorithm to deal with nonstationarity. A league system is adopted to avoid the red queen effect in the game where both sides implement adaptive strategies. Simulation results show that the proposed approach can be applied to maneuver guidance in air combat, and typical angle fight tactics can be learnt by the deep reinforcement learning agents. For the training of an opponent with the adaptive strategy, the winning rate can reach more than 50%, and the losing rate can be reduced to less than 15%. In a competition with all opponents, the winning rate of the strategic agent selected by the league system is more than 44%, and the probability of not losing is about 75%.

To enhance the performance of guiding an aircraft to a moving destination in a certain direction in three-dimensional continuous space, it is essential to develop an efficient intelligent algorithm. In this article, a pretrained proximal policy optimization (PPO) with reward shaping algorithm, which does not require an accurate model, is proposed to solve the guidance problem of manned aircraft and unmanned aerial vehicles. Continuous action reward function and position reward function are presented, by which the training speed is increased and the performance of the generated trajectory is improved. Using pretrained PPO, a new agent can be trained efficiently for a new task. A reinforcement learning framework is built, in which an agent can be trained to generate a reference trajectory or a series of guidance instructions. General simulation results show that the proposed method can significantly improve the training efficiency and trajectory performance. The carrier-based aircraft approach simulation is carried out to prove the application value of the proposed approach.

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers’ workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology’s applications in the ATC communication system. Firstly, it offers a comprehensive overview of current research, including ATC corpora, ASR models, evaluation measures and application scenarios. A more comprehensive and accurate evaluation methodology tailored for ATC is proposed, considering advancements in communication sensing systems and deep learning techniques. This methodology helps researchers in enhancing ASR systems and improving the overall performance of ATC systems. Finally, future research recommendations are identified based on the primary challenges and issues. The authors sincerely hope this work will serve as a clear technical roadmap for ASR endeavors within the ATC domain and make a valuable contribution to the research community.

In recent years, the emergence of large-scale pre-trained language models has made transfer learning possible in natural language processing, which overturns the traditional model architecture based on recurrent neural networks (RNN). In this study, we constructed a multi-intention recognition model, Ernie-Gram_Bidirectional Gate Recurrent Unit (BiGRU)_Attention (EBA), for air traffic control (ATC). Firstly, the Ernie-Gram pre-training model is used as the bottom layer of the overall architecture to implement the encoding of text information. The BiGRU module that follows is used for further feature extraction of the encoded information. Secondly, as keyword information is very important in Chinese radiotelephony communications, the attention layer after the BiGRU module is added to realize the extraction of keyword information. Finally, two fully connected layers (FC) are used for feature vector fusion and outputting intention classification vector, respectively. We experimentally compare the effects of two different tokenizer tools, the BERT tokenizer tool and Jieba tokenizer tool, on the final performance of the Bert model. The experimental results reveal that although the Jieba tokenizer tool has considered word information, the effect of the Jieba tokenizer tool is not as good as that of the BERT tokenizer tool. The final model’s accuracy is 98.2% in the intention recognition dataset of the ATC instructions, which is 2.7% higher than the Bert benchmark model and 0.7–3.1% higher than other improved models based on BERT.

In recent years, the civil aviation industry has actively promoted the automation and intelligence of control processes with the increasing use of various artificial intelligence technologies. Air–ground communication, as the primary means of interaction between controllers and pilots, typically involves one or more intents. Recognizing multiple intents within air–ground communication texts is a critical step in automating and advancing the control process intelligently. Therefore, this study proposes a hybrid detection method for multi-intent recognition in air–ground communication text. This method improves recognition accuracy by using different models for single-intent texts and multi-intent texts. First, the air–ground communication text is divided into two categories using multi-intent detection technology: single-intent text and multi-intent text. Next, for single-intent text, the Enhanced Representation through Knowledge Integration (ERNIE) 3.0 model is used for recognition; while the A Lite Bidirectional Encoder Representations from Transformers (ALBERT)_Sequence-to-Sequence_Attention (ASA) model is proposed for identifying multi-intent texts. Finally, combining the recognition results from the two models yields the final result. Experimental results demonstrate that using the ASA model for multi-intent text recognition achieved an accuracy rate of 97.84%, which is 0.34% higher than the baseline ALBERT model and 0.15% to 0.87% higher than other improved models based on ALBERT and ERNIE 3.0. The single-intent recognition model achieved an accuracy of 96.23% when recognizing single-intent texts, which is at least 2.18% higher than the multi-intent recognition model. The results indicate that employing different models for various types of texts can substantially enhance recognition accuracy.