Runway incursion prevention method based on a discrete object sensing event‐driven model

Tang, Xin; Xing, Jian; Han, Songchen

doi:10.1049/iet-its.2013.0128

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…In the future AGIVN and IoE, the ADS-B technique can play an important role to facilitate the management of aerial resources in an intelligent manner [15]. Traditional conflict detection and collision avoidance algorithms often rely on collaborative systems (e.g., traffic collision avoidance system (TCAS) [16]), or non-cooperative systems (e.g., stand-alone photoelectric sensors and airborne radars used for obstacle detection [17]). However, information provided by the TCAS and radars is generally two-dimensional and the maintenance cost is relatively high [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Aided Trajectory Prediction and Conflict Detection for Internet of Aerial Vehicles

Cheng

Guo

et al. 2022

IEEE Internet Things J.

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As exploitation of low and medium airspace for air traffic management (ATM) is gaining more attention, aerial vehicles' security issues pose a major challenge to the Air-Ground Integrated Vehicle Networks (AGIVN). Traditional surveillance technology lacks the capacity to support the intensive air traffic management (ATM) of the future. Therefore, an advanced automatic dependent surveillance-broadcast (ADS-B) technique is applied to track and monitor aerial vehicles in a more effective manner. In this paper, we propose a grouping-based conflict detection algorithm based on the preprocessed ADS-B dataset, and analyze the experimental results and visualize the detected conflicts. Then, in order to further improve flight safety and conflict detection, the trajectories of the aerial vehicles are predicted based on machine learning-based algorithms. The results are fed into the conflict detection algorithm to execute conflict prediction. It was shown that the trajectory prediction model using long short-term memory (LSTM) can achieve better prediction performance, especially when predicting the longterm trajectory of aerial vehicles. And the conflict detection results based on the trajectory prediction methods show that the proposed scheme can make it possible to detect whether there would be conflicts within seconds.

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