This paper proposes an effective approach for modelling and assessing the risks associated with unmanned aerial vehicles (UAVs) integrated into national airspace system (NAS). Two critical hazards with UAV operations are considered and analyzed, which are ground impacts and midair collisions. Threats to fatalities that result from the two hazards are the focus in the proposed method. In order to realize ground impact assessment, a multifactor risk model is designed by calculating system reliability required to meet a target level of safety for different UAV categories. Both fixed-wing and rotary-wing UAVs are taken into account under a real scenario that is further partitioned into different zones to make the evaluation more precise. Official territory and population data of the operation scenario are incorporated, as well as UAV self-properties. Casualty area of impacting debris can be obtained as well as the probability of fatal injuries on the ground. Sheltering factors are not neglected and defined as four types based on the real scenario. When midair collision fatality risk is estimated, a model of aircraft collisions based on the density of civil flight in different regions over Chinese airspace is proposed. In the model, a relative collision area and flying speed between UAVs and manned aircraft are constructed to calculate expected frequency of fatalities for each province correspondingly. Truthful data with different numbers of UAVs is incorporated in the model with the expected number of fatalities after a collision is included. Experimental simulations are made to evaluate the ground impacts and midair collisions when UAVs operate in the NAS.
Unmanned aerial vehicles (UAVs) are a kind of aircraft without pilots onboard but can be remotely controlled or can fly autonomously based on preprogrammed flight plans [
The integration of UAVs into the NAS presents a number of challenges currently addressed by Federal Aviation Administration (FAA) in the context of the operational safety implications [
Until now UAVs have to be operated in a segregated airspace based on FAA regulation. That is because there are inherent safety concerns with UAVs due to the lack of onboard human pilots [
Risk assessments and estimations of UAVs operating in the NAS become the basement and the first key step. Because it will fundamentally transform existing aviation patterns and its public perceptions. In the past few years, many methods have been proposed to realize the assessments and estimations. Anno [
The main contribution of the work is that an effective approach for modelling and assessing the risks associated with UAVs integrated into NAS is proposed. Two critical hazards of UAV operations are taken into account, namely, ground impacts and midair collisions. The corresponding hazard analysis is conducted and we focus on threats to fatalities generated by the two hazards. A ground impact assessment model is proposed considering system reliability required to meet a target level of safety for different UAVs. Both fixed-wing and rotary-wing UAVs are taken into account under a real scenario, which is further partitioned into different zones to make the evaluation more precise. In the model, territory and population data, casualty areas, and sheltering factors are all indispensable. To estimate the midair collision fatality risk, a model of aircraft collisions based on density of civil flight in different regions over China is proposed. A relative collision area and operating speed between UAVs and manned aircraft are constructed to obtain expected frequency of fatalities for each province using official government data with different numbers of UAVs. Experimental simulations are made to evaluate the ground impacts and midair collisions when UAVs operate in the NAS. The models in this paper provide a generic framework that can be used to structure the development of safety cases for any UAV operation.
The remainder of this paper is organized as follows. Ground impact assessment, including problem description, model establishment, and result analysis, is provided in Section
A model of ground impact assessment for UAVs was proposed to investigate the influence of different factors on the equivalent level of safety (ELOS) in terms of ground fatalities per hour of UAV system operations. The model incorporated total system reliability, UAV size and kinetic energy, and population characteristics and probabilities of fatality in different vicinity of operation.
Once a failure occurs on the UAVs, there will be uncontrolled ground impacts. It is assumed that there will be a number of fatalities on the ground once ground impact events take place, which are further defined as hazardous events [
Problem description of ground impact assessment.
Probability of failure means whether a hazardous event will happen. It could be used to evaluate the frequencies that a UAV malfunctions. When the casualty area is calculated, dimension of UAVs, maximum flying height and velocity, and maximum takeoff mass (MTOM) are incorporated based on the UAV themselves. Casualty area is also strongly affected by glide angle for different UAVs. Combining with the population density, the average height and radius of a human body on the ground could also influence the final assessments. The probability of fatality is highly related to the sheltering factors considering different types of crash areas. Higher sheltering factors will lead to lower probability of fatality.
Vertical impact analysis.
The basic horizontal casualty area is illustrated in Figure
Horizontal impact analysis.
In (
Probability of fatality
Figure
Velocity determination for fixed-wing UAVs.
For rotary UAVs, the impact velocity is obtained with another way as shown in Figure
Velocity determination for rotary-wing UAVs.
There is no doubt that
When the velocities of all kinds of UAVs at the crashing point are obtained, the corresponding kinetic energy could be then determined as given in the equation below as well as the maximum takeoff mass (MTOM):
Once all the factors are taken into account, the ground risk assessment model could be completed using
The scenario considered in this paper is a real case, which is Changqing Campus, Shandong Jiaotong University, Jinan, China. The flying area is shown in Figure
Simulation scenario.
The Changqing Campus is characterized by different population densities and offers different kinds of shelters for people on the ground. In the proposed model, four types of shelters are defined, namely, reinforced concrete buildings, trees, sparse trees, and areas without obstacles. The reinforced concrete buildings could offer high population density but as well as high values of sheltering factor. The others are characterized by low population densities and sparse trees can only be able to offer poor sheltering effects. The sheltering factor is an absolute real number as mentioned before. It is evaluated according to a qualitative estimation of the operative scenario. Different sheltering factors [
Different sheltering factors in different areas.
Type No. | Area | Sheltering factor |
---|---|---|
Type 1 | Reinforced concrete buildings | |
Type 2 | Trees | |
Type 3 | Sparse trees | |
Type 4 | Area without obstacles | |
Suppose the total area where UAVs operate is
In order to evaluate the average population density and sheltering factor accurately, the area has been partitioned into six separate flying zones, which is shown in Figure
Percentage of different types of areas and central angles.
Zone No. | Buildings | Trees | Sparse Tree | No Obstacles | Central Angle |
---|---|---|---|---|---|
Zone 1 | 17.32% | 21.37% | 23.74% | 37.57% | 78.0° |
Zone 2 | | 58.32% | 32.39% | 9.29% | 26.2° |
Zone 3 | 27.27% | 49.69% | 5.81% | 17.23% | 88.7° |
Zone 4 | | 41.95% | 21.10% | 36.95% | 23.6° |
Zone 5 | 11.14% | 46.45% | 6.68% | 35.73% | 87.0° |
Zone 6 | | 7.69% | 62.03% | 30.28% | 56.5° |
Partition of the flight area.
Combining with Figure
Another scenario parameter that should be paid attention to is the density of population in each zone. Based on the official data from the school website, the total number of people in Changqing Campus, Shandong Jiaotong University, is
Distribution of population in different zones.
Zone No. | Central Angle | Population | Percentage |
---|---|---|---|
Zone 1 | 78 | | |
Zone 2 | 26.2 | 395 | 1.5% |
Zone 3 | 88.7 | | |
Zone 4 | 23.6 | 395 | 1.5% |
Zone 5 | 87 | | |
Zone 6 | 56.5 | 1843 | 7% |
The ground impact model of (
UAV parameters for ground impact analysis.
Type | Model | Wingspan | Length | MTOM | Speed |
---|---|---|---|---|---|
Fixed | Firebird | 1200 | 830 | 1.2 | 83 km/h |
X8 | 2120 | 820 | 4.2 | 110 km/h | |
Rotary | Typhoon H | 457 | 520 | 1.98 | 48.6 km/h |
Zenith ATX8 | 600 | 600 | 9.65 | 72 km/h |
Firebird and X8 are two fixed-wing aircraft with similar length. But X8 is larger than Firebird with wider wingspan, as well as heavier maximum takeoff mass. X8 could fly at
Another parameter is the flying height of UAVs in the operation airspace. Considering the performances of the four UAVs in Table
The average height and radius of human beings on the ground that is needed in (
Probability of fatality and sheltering factor in different flying zones.
From the histograms in the figure, Firebird is the most fatal compared with the other three no matter where it operates. Typhoon H is a little better than Firebird. Zenith ATX8 is the safest of the four. Actually, the differences between Firebird and Typhoon H are not huge at all which are given in Figure
Figure
Parameters in different zones.
Zone 1 | Zone 2 | Zone 3 | Zone 4 | Zone 5 | Zone 6 | |
---|---|---|---|---|---|---|
Ratio | | 7.28% | | 6.56% | | 15.69% |
| | 0.0085 | | 0.0095 | | 0.0185 |
| | 14.903 | | 10.501 | | 7.741 |
Table
In Table
Results for Firebird.
Firebird | | | | | 1/ |
---|---|---|---|---|---|
Zone 1 | 1411.2 | 11.83 | 0.19 | | |
Zone 2 | 0.17 | | | ||
Zone 3 | | 1.50E-07 | 6.67E+06 | ||
Zone 4 | 0.26 | 3.45E-07 | 2.90E+06 | ||
Zone 5 | 0.18 | 1.39E-07 | 7.18E+06 | ||
Zone 6 | | 1.23E-07 | 8.14E+06 |
Results for X8.
X8 | | | | | 1/ |
---|---|---|---|---|---|
Zone 1 | 4939.2 | 26.18 | 0.15 | | |
Zone 2 | 0.13 | | | ||
Zone 3 | | 9.07E-08 | 1.10E+07 | ||
Zone 4 | 0.22 | 1.82E-07 | 5.50E+06 | ||
Zone 5 | 0.14 | 7.89E-08 | 1.27E+07 | ||
Zone 6 | | 5.95E-08 | 1.68E+07 |
Results for Typhoon H.
Typhoon H | | | | | 1/ |
---|---|---|---|---|---|
Zone 1 | 2508.9 | 2.63 | 0.17 | | |
Zone 2 | 0.15 | | | ||
Zone 3 | | 7.67E-07 | 1.30E+06 | ||
Zone 4 | 0.24 | 1.66E-06 | 6.03E+05 | ||
Zone 5 | 0.16 | 6.92E-07 | 1.45E+06 | ||
Zone 6 | | 5.69E-07 | 1.76E+06 |
Results for Zenith ATX8.
ATX8 | | | | | 1/ |
---|---|---|---|---|---|
Zone 1 | 13278.4 | 3.53 | 0.13 | | |
Zone 2 | 0.11 | | | ||
Zone 3 | | 8.63E-07 | 1.16E+06 | ||
Zone 4 | 0.19 | 1.55E-06 | 6.47E+05 | ||
Zone 5 | 0.12 | 7.12E-07 | 1.40E+06 | ||
Zone 6 | | 4.69E-07 | 2.13E+06 |
When the UAV Firebird flies in Changqing Campus, Zone 6 is the most fatal with
Table
The similar results can be found for Typhoon H in all the six zones. Zone 6 is still the most fatal and Zone 3 is safest of the six. The probabilities of failure in Zone 1, Zone 3, Zone 5, and Zone 6 are in the same order of magnitudes. Zone 1 needs Typhoon H to fly safely without any malfunctions up to
Table
Based on Table
In this part an estimation model of midair collisions between UAVs and other civil manned aircraft are proposed. Actual civil flight data of taking off and landing in 2015 over Chinese airspace are incorporated to develop the model. The expected frequency of fatalities in the midair is used as an evaluation standard.
In this paper the midair collision risk estimation was based on the use of an existing general gas model [
The model incorporates domestic air traffic density data in 2015 over Chinese airspace. UAVs are supposed to be deployed randomly in a specified airspace. Once there are threatened civil aircraft entering the space, the collision volumes of UAVs will be extruded. The number of UAVs and civil aircraft in the airspace determines the occupation rate of the airspace. Relative collision area and speed between UAVs and civil aircraft could generate effects on the final collision frequency.
In (
(
In (
Combining with all the analysis above, the expected number of collisions per hour of UAV flights in the NAS with
By using the equations above, (
Finally based on analysis above, the midair collision risk estimation model can be generated. The expected frequency of fatalities per hour of UAV flights in the NAS can be obtained with
In midair collision risk estimation, since severe uncertainty often occurs in uncertain and dynamic environments, the related factors should be incorporated in the model in order to reduce the influence of severe uncertainty. Based on the existing works, it turns out that the model is often quite sensitive to even minor errors in the transition probabilities. As given in (
Simulations over the whole Chinese airspace using real data will be given in the next part.
All the UAVs operating in the NAS are set as large UAVs. The core parameters needed in the proposed model, such as frontal exposer area, flying speed and their numbers, are given in Table
Core parameters of UAVs.
Collision area | Speed | Number of UAVs |
---|---|---|
| | |
Expected frequency of fatalities in China.
Figure
There is no doubt that Shanghai and Beijing are the two most dangerous in China no matter how many UAVs are there in the airspace. When
Tibet and Qinghai are the safest of all with the lowest expected frequency of fatalities under all the situations. The reason is that the density of civil flights in the airspace is low over the whole year. Their expected frequency of fatalities are
Tianjin, Shandong, Jiangsu, Zhejiang, Fujian, Henan, Chongqing are seven provinces, which are much safer than Shanghai and Beijing. But these seven provinces are still dangerous compared with the other provinces, which are determined by the density of manned aircraft in the airspace. The expected fatal frequencies of the seven provinces are all above
Neimenggu, Xinjiang, and Gansu are the three provinces that are more dangerous than Tibet and Qinghai, but much safer compared with the others under all kinds of UAVs settings. The safety levels of Heilongjiang, Jilin, Hebei, ShanxiT, Anhui, Jiangxi, Hubei, Hunan, Guangxi, Sichuan, Yunnan, Guizhou, ShanxiX, and Ningxia are in the medium range.
Based on the analysis above, we could conclude that the majority of the midair collision risk is concentrated over metropolitan areas with major airports by approximately an order of magnitude. The structure of air traffic in the NAS is clear, with large collision risk along several well-traveled routes. The expected level of safety calculated by using this method does not adequately capture the expected level of safety in low density regions. Civil flight density, and therefore collision risk, is expected to be highest on major flight levels and within the airway boundaries, reflecting the operation of the majority of air traffic along airways in the NAS. The structure of operations on flight levels and along airways is likely to create local regions of increased density in dimensions, which is not analyzed by this method in this paper.
This paper has introduced an effective approach for modelling and assessing the risks associated with UAVs integrated into NAS. Both ground impact hazard and midair collision fatality risk are estimated, in which threats to fatalities generated by the two hazards are the focus. Based on system reliability required to meet a target level of safety for different UAVs, a ground impact assessment model is proposed. Both fixed-wing and rotary-wing UAVs are considered under a real scenario. In the model territory and population data, casualty areas, and sheltering factors are all indispensable. Since the fatal injuries yields the probability that an impact may cause a fatality, a random number generation process using this probability, is used to determine if the fatality happens or not for each simulation. A model of aircraft collisions is designed to estimate the midair collision fatality risk based on density of civil flight in different regions over China. The relative collision area and operating speed between UAVs and manned aircraft are constructed to obtain expected frequency of fatalities for each province with official government data. Experimental simulations are made to evaluate the ground impacts and midair collisions when UAVs operate in the NAS. The models in this paper provides a generic framework that can be used to structure the development of safety cases for any UAV operation.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
The authors declare that they have no conflicts of interest.
This work was supported by the National Natural Science Foundation of China [Grants nos. U1433203, U1533119, and L142200032] and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China [Grant no. 61221061].