Collision probability estimation for small unmanned aircraft systems

Zou, Yiyuan; Zhang, Honghai; Zhong, Gang; Líu, Hao; Feng, Dikun

doi:10.1016/j.ress.2021.107619

Cited by 24 publications

(18 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To analyze the performance of the MUAS conflict detection method, this paper select DJI Matrice 600 as a case study, whose form factor (L × W × )) is set to 1668 mm × 1668 mm × 759 mm, and the probability threshold for conflict determination is 5% based on the validation in [27].…”

Section: A Overview Of Data and Scenarios For Conflict Detectionmentioning

confidence: 99%

Multivariate Combined Collision Detection for Multi-Unmanned Aircraft Systems

Zhang

Zhong

et al. 2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

To mitigate the problem of multiple unmanned aircraft systems (MUAS) conflicts at low altitude and ensure the operational safety, this paper proposes a Multivariate Combined Conflict Detection (MCCD) method for MUAS by combining the characteristics of nominal and probabilistic trajectory method. Firstly, the structural framework of the MCCD method is established based on the concept of potential conflict pool, and a detection pattern is derived for MUAS. Secondly, a three-dimensional conflict fast detection model is constructed by velocity obstacle methods, which can rapidly detect potential conflict risks. Thirdly, a trajectory prediction model is constructed by using bidirectional long-short term memory (Bi-LSTM) network, and then a probabilitybased conflict detection model can be obtained by the expected value and error distribution of trajectory prediction, which can accurately calculate the collision probability of UAS pair. By fully integrating the above models, the fast and accurate detection of MUAS conflicts is achieved. Finally, multiple conflicting trajectories are constructed to analyze the effectiveness of MCCD method, the tests indicate that the average detection time of the proposed method is less than 15ms, the false positive rate is less than 0.01 and the false negative rate is less than 0.0035. The results show that the MCCD has the accuracy advantage and better real-time performance for MUAS conflict detection compared to the method of velocity trend extrapolation, single probabilistic conflict detection and probabilistic neural network.INDEX TERMS conflict detection, multi-unmanned aircraft systems, potential conflict, probability estimation.

show abstract

Section: A Overview Of Data and Scenarios For Conflict Detectionmentioning

confidence: 99%

Multivariate Combined Collision Detection for Multi-Unmanned Aircraft Systems

Zhang

Zhong

et al. 2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, it cannot meet the requirements of free navigation in the ICAO.DOC9750 global navigation plan [19]. The yaw probability-based Kalman filtering is introduced to evaluate the multifactor superimposition effect on the collision model so that the practicability, accuracy, and reproducibility of the event lateral conflict model can be improved in future navigation systems [20,21]. The total yaw error TSE (total system error) under lateral confliction error is generally composed of NSE (navigation system error), PDE (path definition error), and FTE (flight technical error).…”

Section: Kalman Filter Error Model Based On Rnpmentioning

confidence: 99%

Optimization of Lateral Collision Risk of Aircraft Based on the Skid-Slip Event Model

Liu

Zhu

Xie

et al. 2022

International Journal of Aerospace Engineering

Self Cite

View full text Add to dashboard Cite

With the development of aircraft flow under the existing airspace capacity now, the shortage of airspace resources and flight delays have become significantly severe. Therefore, building a safe and efficient mathematical model from quantitative analysis and improving the scientificity of route planning and management are essential propositions for future research on new navigation systems. Based on the traditional event collision risk assessment model, the collision module is upgraded to an ellipsoid according to the performance of aircraft, integrating the boundary curvature optimization characteristics of the TSRRT (task-space rapidly-exploring random trees) algorithm. An aircraft event lateral conflict resolution model based on the TSRRT algorithm is proposed. Taking the A320 aircraft and 737-800 aircraft as experimental subjects, the corresponding collision coefficients are imported into software tools for the simulation and the Kalman filtering is combined to verify the smoothness of the front and rear boundary curvatures. The result proves that the event side collision risk of aircraft based on TSRRT is 15% of the traditional event model and the smooth curvature error is reduced by 80% due to the improvement. Therefore, the improved event model is practical and valuable, providing a theoretical basis for future track-based operations.

show abstract

“…According to the analysis results of Amazon, around 83% of packages could be delivered by UAVs taking into account the loading capacity [3]. Numerous UAVs will enter low-altitude airspace for commercial and civilian uses, especially in urban areas, which raises concerns about possible collisions and crashes [4], [5]. To address the above concerns, the National Aeronautics and Space Administration (NASA) has put forward the concept of operations for unmanned aircraft system traffic management (UTM).…”

Section: Introductionmentioning

confidence: 99%

Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering

et al. 2022

Self Cite

View full text Add to dashboard Cite

To improve the trajectory prediction accuracy of unmanned aerial vehicles (UAVs) with random behavior intentions, this paper presents a short-term four-dimensional (4D) trajectory prediction method based on spatio-temporal trajectory clustering. A spatio-temporal trajectory clustering algorithm is first designed to cluster the UAV trajectory segments divided by a fixed time window. Each trajectory segment is given a category label that represents some certain type of behavior characteristics, such as climbing, turning, descending, etc. The convolutional neural network (CNN) is used to identify the category label of a given trajectory segment by learning the behavior characteristics of different trajectory segments. Based on the long-short-term memory network (LSTM), a short-term trajectory prediction model for different categories of label trajectory segments is established. The global trajectory prediction includes several steps adopting the corresponding prediction models. Historical trajectory data of UAVs are used to validate the proposed prediction method. Experiment results indicate that the method can obtain obviously better prediction accuracy in a short prediction time range (0-3s) with acceptable efficiency compared to LSTM, GRU and velocity trend extrapolation.

show abstract

Collision probability estimation for small unmanned aircraft systems

Cited by 24 publications

References 26 publications

Multivariate Combined Collision Detection for Multi-Unmanned Aircraft Systems

Multivariate Combined Collision Detection for Multi-Unmanned Aircraft Systems

Optimization of Lateral Collision Risk of Aircraft Based on the Skid-Slip Event Model

Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering

Contact Info

Product

Resources

About