Multi-Aircraft Trajectory Collaborative Prediction Based on Social Long Short-Term Memory Network

Xu, Zhengfeng; Zeng, Weili; Chu, Xiao; Cao, Puwen

doi:10.3390/aerospace8040115

Cited by 39 publications

(19 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shi et al [26] test the single-step application of a LSTM network to predict the next state vector from the last 10 observations. Xu et al [27] test the multi-step application of a LSTM network to predict the next 30 state vectors from the last 20 observations. This work focuses on the prediction of the trajectories of several aircraft simultaneously.…”

Section: A Testing Conditions Of Neural Network In the State Of The Artmentioning

confidence: 99%

“…Our conclusions from the study of the state of the art is that while many (often similar) models have been proposed, there is little consideration about researching how certain factors or variants may affect prediction. Only three of the aforementioned proposals [27], [29], [32] test the application of multi-step prediction, none of them report the effect of using different trajectory lengths for prediction, and only one of them [31] considered the use of differential features. In a real use-case, these works offer few insights on what strategies and practices can offer the best results, and what information can benefit the models.…”

Section: A Testing Conditions Of Neural Network In the State Of The Artmentioning

confidence: 99%

See 1 more Smart Citation

Neural Networks for Aircraft Trajectory Prediction: Answering Open Questions About Their Performance

Ayala

Vidal

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The increase in air traffic in the recent years has motivated the development of technologies to monitor air space and warn about possible collisions by predicting the trajectories that will be followed by aircraft. In this field, neural networks have become prominent thanks to their potential to learn to predict maneuvers without providing aspects that are difficult to model such as atmospheric conditions, or detailed aircraft parameters. A variety of models have been proposed; however, these are often tested in very limited setups, leaving many unanswered questions about how they perform in certain conditions, or whether or not their accuracy can be improved by training models for specific trajectories, using additional features, predicting more distant points directly, etc. This may be problematic for researchers or developers of these systems, who have no way of knowing what strategies will yield the best results. We have identified ten open research questions that have not been answered through in-depth testing. This motivated us to carry out a novel experimental study that performs aircraft trajectory prediction with several dozens configuration variants to answer the aforementioned questions by means of a much more complete evaluation. Some of the conclusions of our study stand in contrast with some popular practices in the state of the art, which casts some doubts on the simplicity of their application; for example, differential features are crucial for proper performance but are not mentioned by most studies, while complex, more elaborate models may lead to worse results than simple ones. Other important insights include the benefit from specialized models in more challenging scenarios, the influence of the known trajectory length in said scenarios, the step degradation of predictions when predicting further into the future, or the detrimental effect of adding additional features. These insights should help guide future research about the application of neural networks when it comes to aircraft trajectory prediction and their eventual inclusion in final systems.

show abstract

Section: A Testing Conditions Of Neural Network In the State Of The Artmentioning

confidence: 99%

Section: A Testing Conditions Of Neural Network In the State Of The Artmentioning

confidence: 99%

Neural Networks for Aircraft Trajectory Prediction: Answering Open Questions About Their Performance

Ayala

Vidal

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Authors using these models are mostly capitalising on framework availability of the past years and are often used jointly for comparison, with the linear regression often used as a baseline. Nonetheless, (ii)-(v) AI models have also been used for other type of predictions such as Route choice [39], the Structure of a Sector configuration [40], the Environmental State of the Airspace, ATCO action prediction [41], or-short-term-4D Trajectory prediction [42,43]. Multi-Agent Systems on their side have been used to model and predict more complex tasks, like Indicators of the Traffic such as delay propagation on networks [44], 4D Trajectory, and to a certain extent, 5D Traffic prediction [45] and CTR Traffic-CTR Traffic being easier to predict due to the important amount of constraints.…”

Section: Categorisation Insightsmentioning

confidence: 99%

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

et al. 2022

View full text Add to dashboard Cite

Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed.

show abstract

“…Meanwhile, GRU can effectively alleviate the gradient disappearance and gradient explosion that may occur during RNN training, thus effectively solving the problem of long-term memory. LSTM network is also a variant of RNN [40]. Its performance is almost the same as that of GRU, but GRU is simpler in structure, which can reduce the amount of calculation and improve the training efficiency [41].…”

Section: Bigrumentioning

confidence: 99%

Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model

2021

View full text Add to dashboard Cite

The prerequisite for victory in war is the rapid and accurate identification of the tactical intention of the target on the battlefield. The efficiency of manual recognition of the combat intention of air targets is becoming less and less effective with the advent of information warfare. Moreover, if the traditional method of combat intention of air targets is based only on data from a single moment in time, the characteristic information on the time-series data is difficult to capture effectively. In this context, we design a new deep learning method attention mechanism with temporal convolutional network and bidirectional gated recurrent unit (Attention-TCN-BiGRU) to improve the recognition of the combat intent of air targets. Specifically, suitable characteristics are selected based on the combat mission and air posture to construct a characteristic set of air target intentions and encode them into temporal characteristics. Each characteristic in the characteristic set is given an appropriate weight through the attention mechanism. In addition, temporal convolutional network (TCN) is used to mine the data for latent characteristics and bidirectional gated recurrent unit (BiGRU) is used to capture long-term dependencies in the data. Experiments comparing with other methods and ablation demonstrate that Attention-TCN-BiGRU outperforms state-of-the-art methods in terms of accuracy in recognizing target intent in the air.

show abstract

Multi-Aircraft Trajectory Collaborative Prediction Based on Social Long Short-Term Memory Network

Cited by 39 publications

References 47 publications

Neural Networks for Aircraft Trajectory Prediction: Answering Open Questions About Their Performance

Neural Networks for Aircraft Trajectory Prediction: Answering Open Questions About Their Performance

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model

Contact Info

Product

Resources

About