Machine Learning Attempt to Conflict Detection for UAV with System Failure in U-Space: Recurrent Neural Network, RNNn

Komatsu, Rina; Bechina, A.; Guldal, Serkan; Şaşmaz, Merivan

doi:10.1109/icuas54217.2022.9836147

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…In contrast to previous studies [23] that have assumed a static environment, or have deployed a grid mission network model (as UAS traverses a region along a structured grid) [89], the proposed model was validated using a drone delivery system scenario which in turn addresses uncertainties generated by adverse weather, as well as dynamic and static obstacles. The present study contributes to the limited research on the prediction of air traffic flow for UTM systems, addressing the limitation that the existing literature covers either trajectory prediction [90,91] or conflict detection and resolution [92,93]. Deployment of the model aims to address issues related to UAV traffic management (e.g., congestion).…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Architecture for UAV Traffic-Density Prediction

Alharbi

Petrunin

Panagiotakopoulos

2023

Drones

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: The research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network and encoder-decoder LSTM framework to integrate air traffic flow prediction with the intrinsic complexity metric. This adapted complexity metric takes into account the important differences between ATM and UTM operations, such as dynamic flow structures and airspace density. Additionally, the proposed methodology has been evaluated and verified in a simulation scenario environment, in which a drone delivery system that is considered essential in the delivery of COVID-19 sample tests, package delivery services from multiple post offices, an inspection of the railway infrastructure and fire-surveillance tasks. Moreover, the prediction model also considers the impacts of other significant factors, including emergency UTM operations, static no-fly zones (NFZs), and variations in weather conditions. The results show that the proposed model achieves the smallest RMSE value in all scenarios compared to other approaches. Specifically, the prediction error of the proposed model is 8.34% lower than the shallow neural network (on average) and 19.87% lower than the regression model on average.

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