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The frequent intrusion of unmanned aerial vehicles (UAVs) into civil aviation airports poses a severe challenge to the safety management of the airport. The subsequent flight delays and even the closure of the runway have seriously affected the normal operation of the airport. To measure the collision risk of the Unmanned-Manned (U-M) encounter (intruding UAV to manned aircraft), a collision risk assessment platform is developed in this paper to predict the U-M encounter collision risk of the airport restricted area. The designed platform can be used to dynamically generate 3D trajectories of the U-M encounter, and to assess the collision risk with the changing of the relative distance in the probabilistic curve. Further, to predict the collision risk of the airport restricted area, the collision risk heatmap representation is then introduced. A case study of Changi Airport, Singapore is employed to generate the runway collision risk heatmap based on the designed collision risk assessment platform. The results demonstrate how the collision risk of the separate runway and the integrated runway distributes. Civil Aviation Authority of Singapore (CAAS) National Research Foundation (NRF) Submitted/Accepted version This project is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme.

Tracking systems are a core component of future UAS Traffic Management (UTM) systems. This study aims at providing technical fundamentals for supporting the robust standardization of UTM tracking systems. To evaluate the impact of tracking system performances on the effectiveness of centralized conflict detection, we propose a simulation-based quantitative method. Monte Carlo simulations were used to acquire data for tracking systems behaviors under various operational uncertainties for a pair-wise flight encounter scenario. Sensitivity analysis of safety performance in managing Well-Clear Violations (WCVs) were performed with respect to performance parameters (transmission availability, UAS-side and ground side position update rates and transmission latency) for a cooperative tracking system. It was found that extrapolation techniques help to mitigate long intervals between successive UA position updates. UAS position and velocity update rates over 1 Hz are recommended. Civil Aviation Authority of Singapore (CAAS) National Research Foundation (NRF) Submitted/Accepted version This research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of National Research Foundation, Singapore and the Civil Aviation Authority of Singapore.

With the promise to revolutionize many industries, the popularity of Unmanned Aerial Vehicles (UAVs) has surged in the last two decades. The use of multirotor UAVs in urban areas is gaining traction, with applications such as cargo delivery, emergency response, and recreational flight. However, unlike manned aviation, UAVs do not have a standardized design, testing, and certification procedures. Hence, a lot of countries raised their concerns about the risk of potential multi-rotor UAV crashes and impacting pedestrians on the ground. Apart from high-rise buildings, covered linkways are a ubiquitous sight in countries like Singapore with a primary role to provide shelter for pedestrians in the event of rain. Therefore, this study investigates the collision severity of a multirotor UAV impacting a covered linkway. Briefly put, the level of protection that covered linkways can provide to pedestrians from a multirotor UAV falling from height is analyzed. The methodology in this study is divided into three phases. In Phase 1, Finite Element Analysis (FEA) is employed to simulate the impact of two specific multirotor UAV designs and solid spheres with comparable material properties as they collide onto a simplified covered linkway panel using ABAQUS/CAE. Subsequently, parametric analysis using Python scripting is performed to assess the collision severity due to variations in mass and crash speed of rigid sphere. Phases 2 and 3 explore the impact of the honeycomb structure modelling on the impact simulation. Findings from this study will be beneficial for the computation of sheltering factor in UAV ground risk assessment. Submitted/Accepted version

The wind is one of the major factors that may cause unmanned aerial vehicles (UAVs) to crash and pose fatality risk to the population and property damage risk to infrastructures. This paper investigates wind patterns on temporal scales to identify high-risk periods in terms of wind conditions for safe UAV operations in urban airspace. The research starts with the historical wind speed data analysis using statistical approaches. As the wind speed data does not follow normal distribution after checking, a nonparametric approach of the Kruskal-Wallis test is applied for hypothesis testing to see if there is a significant difference in the median wind speed in different years. Regression analyses are also performed for monthly wind speed data to check any significant trends that could facilitate the predictions of average wind speed in the long term. This study will contribute to safe air traffic management for UAV operations in low-altitude urban airspace by mitigating adverse wind effects. Civil Aviation Authority of Singapore (CAAS) Nanyang Technological University National Research Foundation (NRF) Submitted/Accepted version This research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme. The Research Student Scholarship (RSS) provided by the Nanyang Technological University (NTU) to the second author is also acknowledged.

The Unmanned Aerial Vehicles (UAVs) have gained increasing attention recently, and depth estimation is one of the essential tasks for the safe operation of UAVs, especially for drones at low altitudes. Considering the limitations of UAVs’ size and payload, innovative methods combined with deep learning techniques have taken the place of traditional sensors to become the mainstream for predicting per-pixel depth information. Since supervised depth estimation methods require a massive amount of depth ground truth as the supervisory signal. This article proposes an unsupervised framework to tackle the issue of predicting the depth map given a sequence of monocular images. Our model can solve the problem of scale ambiguity by training the depth subnetwork jointly with the pose subnetwork. Moreover, we introduce a modified loss function that utilizes a weighted photometric loss combined with the edge-aware smoothness loss to optimize the training. The evaluation results are compared with the model without weighted loss and other unsupervised monocular depth estimation models (Monodepth and Monodepth2). Our model shows better performance than the others, indicating potential assistance in enhancing the capability of UAVs to estimate distance with the surrounding environment. Civil Aviation Authority of Singapore (CAAS) Submitted/Accepted version This research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme.