GB2598939A

GB2598939A - Method and system for the prodiction of risk maps of airspace, and land areas based on the fusion of data from multiple sources

Info

Publication number: GB2598939A
Application number: GB2014801.1A
Authority: GB
Inventors: Gregory Lloyd Peter; Naraidoo Marcus
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-09-20
Filing date: 2020-09-20
Publication date: 2022-03-23
Anticipated expiration: 2040-09-20
Also published as: GB2602783A; GB202017955D0; GB202014801D0; WO2022101637A1; GB2598939B

Abstract

A system for creating a risk map for air vehicles, such as drones or UAVs, in an airspace at a given time. The risk map considers the loss of radio data links or similar wireless signals that provide information and commands to the air vehicle, and the risk of losing control due to weather or collision with buildings. The system can use a series of sensors to monitor radio channels to assess signal strength, signal interference or noise levels. Computational models of the ground may be used to determine signal conditions due to interference from reflection or scattering caused by the environment in and around the airspace as well as turbulence conditions. The system may use the data to create a three-dimensional map of risk levels for the airspace.

Description

METHOD AND SYSTEM FOR THE PRODICTION OF RISK MAPS OF AIRSPACE AND LAND AREAS BASED ON THE FUSION OF DATA FROM MULTIPLE SOURCES

INVENTOR 1: Peter Gregory Lloyd, 1 Cardwell Close, Emerson Valley, Milton Keynes, MK4 UK INVENTOR 2 Dr. Marcus Naraidoo, 42 Salisbury Road, Redland, Bristol B56 7AT UK

BACKGROUND TO THE INVENTION

Objects such as flying drones or ground-based smart vehicles or other data connected platforms (hereafter "Devices"), whether controlled by a human, controlled by an off-board algorithm, or autonomously controlled will be operating in an environment of multiple external factors (hereafter "Factors"), some or many of which may have a significant impact on the motion and control of the Devices. For instance, flying Devices may be influenced by aerodynamic effects such as gusts, vortices, swirl and currents that are due to a combination of prevailing weather, permanent structures such as buildings, temporary structures such as cranes, and other Devices in their vicinity. The same Devices may rely on signals from sources such as, but not limited to, GPS and cellular data such as 4G and SG, or Radio Frequency Control (RFC) and these signals may suffer from black spots, hot spots, multi-path, interference, intentional and unintentional jamming and the effects of screening by virtue of objects such as buildings, cranes, other Devices and the effects of weather. Devices may indeed depend on other sources of data (perhaps laser optic signals) and may be affected by other natural phenomena (perhaps volcanic dust).

The use of mobile platforms, or Devices, for a multitude of purposes require that the operators, be they under direct human or via automatic control or combination of the two, require information transfer to and from the operators of the Devices as well as the Devices themselves. In the most basic circumstance, a human operator or pilot would use their own natural sensors, principally their eyes and sense of balance, combined with an appreciation of the objective of the journey of the Device. In addition, the safe steerage of the Device on its journey requires knowledge of hazards along the route of the journey that have to be either avoided or safely transited whilst en route. In current situations these Devices usually make use of ground and/or flight as the means of their locomotion. Additionally, the Devices obtain information on their locations as they progress from the beginning through waypoints to their destinations.

In the case of automated and manually guided Devices, the communication of the necessary data and information for planning and execution of the journey relies upon radio frequency and/or GPS communication for both navigation and control functions.

Where the Device is an air vehicle that flies along the route of the journey the data and information needed for safe and efficient passage includes both topological hazards that need to be avoided as well as atmospheric disturbances created by the effect of winds including hazards associated with the effects of the weather above and around the topological features.

In such circumstances the risks associated with the journeys are assessed and mitigated by a combination of knowledge of the aerodynamic hazards and the availability of radio frequency signals of the required strength and clarity to create a form of Derived Intelligence to permit safe control and navigation.

The present invention considers the total, combined effect of different Factors on the Devices, and in particular aims to take the separate Source Data to produced Data Fused Rich Data which can be used to quantify matters such as operational risk or dynamic flight corridor boundaries and other forms of Derived Intelligence.

BRIEF DESCRIPTION OF ASPECTS OF THE INVENTION

The present invention relates to a method and system, and variations thereof, that combines data of different types (hereafter "Source Data") and produces a combined (hereafter "Data Fused") derived data set (hereafter "Rich Data") which can form the basis of multi-dimensional risk maps (hereafter "Derived Intelligence"). The source data can be pre-calculated or measured, calculated or measured in real-time or in any combination thereof.

Source Data tend to be in formats or schemes that are specific to the type of physical or real-world context in which they are measured or calculated.

For instance, measurements of windspeed and direction tend to be done at points, and calculations of windspeed are presented at nodes (or points) in a computational grid. The underlying computational fluid dynamics (hereafter "CFD"), however, will calculate the flow properties through faces of computational cells and then interpolate the results of the calculations to the points.

Radio frequency measurements are often made at points, or through surfaces such as an antenna array. Underlying calculations of computational electromagnetism (hereafter "CEM") can be made at nodes, or edges or faces or in volumes.

Each is a different underlying form of Source Data is also measured or calculated in grids that fill three-dimensional (hereafter "3D") space differently. For instance, to fully resolve very small scale CFD effects the computational space has to have a computational or measurement grid that is fine enough to capture those effects. It is a similar situation for radio frequency, infra-red, optical or other forms of Source Data. Furthermore, some Source Data effects may be relatively static, some will vary slowly with time, and others may vary at a much higher frequency. Source Data, therefore, will have spatial descriptions that are specific to their particular physical characteristics, described in a spatial and temporal manner that is specific to their particular physical complexities, and with values that can be at nodes, edges, areas or volumes.

Different types of Source Data tend to be heterogeneous.

The levels of risk that may be associated with the passage of any Device on any particular route, at any particular day and time, are of great importance to many sectors such as legislators, regulators, insurers and underwriters, legal entities, traffic management operators and service providers. These include, the owners and operators of the Devices, any passengers on such Devices, the owners and occupants of any buildings and structures that might form part of the topologic features along the route, the general by-standers that might be present along the route, the owners of any cargo carried by the Devices, the legal authorities responsible for either or both the national and local governments, the recipients of any services that might be provided by the owners and operators of the Devices during any journey and finally any financial service provider that has provided insurance cover for any or all of the previously mentioned entities in the event that the Device might be involved in some form of accident or incident causing loss of any form, or injuries or fatalities.

The current invention addresses the need for a suitable multi-factor risk assessment method for such Devices.

Traditionally, the heterogeneous nature of Source Data confines the analysis of effects of the physical problem that the data describes to that particular physical consideration. For instance, as assessment of the risk associated with wind gusts on a flying object considers only the aerodynamic CFD data and the particular wind-related risks. Similarly, an assessment of the risk associated with GPS black spots on an object requiring a GPS signal considers only the radio frequency CEM data and the particular GPS-related risks. For example, in that segregated approach it is conceivable that a Device that is affected by both aerodynamic effects and radio frequency effects may have one set of Source Data that suggests a low aerodynamic risk from gusts, and another set of Source Data that suggests a high radio frequency risk from being in a black spot. Also the man made environment such as buildings, bridges, flyovers, towers, masts, cranes, powerful exhaust from air conditioning and industrial plant and plants such as trees can pose obstacles that raise the risk to the flying and ground moving vehicles and devices and data on these will obtained by the system from official layouts and plans as well as automatic and manually assessed sensor data such as CCTV, Infrared cameras, LIDAR and radio frequency radar devices located in, on or above the complex environment in question. Furthermore, masts, cranes, cables billboards, tethered blimps, road traffic, harbour and seaway floating traffic, rail traffic and regular low flying aircraft such as the approaches to airports, airfields and heliports, sports projectiles including balls, arrows, javelins, kites, radio controlled models, shotgun pellets, rifle and pistol bullets and plants such as trees and wildlife such as bird sanctuaries in the otherwise unobstructed airspace will also be taken into account by the system using data from official sources and from live sensing systems in, on and above the complex environment being assessed for risk.

Our invention does not treat Source Data in isolation. The purpose is to take any number of Source Data sets, operating at any spatial or temporal resolution, of any data format or type, and accurately combine these Source Data to produce Data Fused Rich Data that can then be used to assign, for instance, measures of risk or other Derived Intelligence to locations and operations in 3D time-varying real-world scenarios, deployments and situations.

The proposed invention is a Data Fusion method, process, algorithm and application (hereafter the "Risk Assessment System") that leads to the creation of Derived Intelligence for Devices operating in complex environments. The purpose is to receive both homogeneous and heterogeneous Source Data and information concerning all the factors that influence the risks involved in Device operating in complex environments, for example the risks associated with operating flying platforms in urban centres. The major features of this Risk Assessment System are shown in Figure 1.

Feature (1) is the time-varying meteorological Source Data which may be obtained from data feeds or subscriptions from Meteorological Offices or from local or other measurement sources and may be historic, measured, calculated or forecast. Feature (2) is the topographical Source Data which can be obtained from a range of providers such as Ordnance Survey or other sources and which is updated at a frequency which captures any topographic changes or alterations. Feature (3) is the time-varying radio frequency electromagnetic Source Data which can be measured, calculated, predicted or forecast using the appropriate transmitters and materials and which can similarly be provided or discerned from a range of providers, operators or other means. Feature (4) is device or platform specific source data, which may include (but is not limited to) the material characteristics of any device that may have an effect on the aerodynamic characteristics (calculated as a combination of meteorological and topographic data) or electromagnetic characteristics (calculated as a combination of meteorological, topographic and radio infrastructure data) and which include time-varying data regarding the travel path and travel characteristics of the device and determine the radio frequency signal conditions due to multipath interference caused by reflections, diffractions, refractions and scattering from the environment in, under and around the airspace. Feature (5) is the Source Data covering operational procedures extant in the area of operations, e.g. recognised and enforced low level airways in within the urban volume. Feature (6) is the Source Data input covering air traffic conflict, e.g. known areas where platform routing crosses approach routes to landing and take-off hubs and also live traffic Source Data if available in real time assessments of risk. Feature (7) is the input of Source Data on surface traffic that may conflict with the intended route of the Device, e.g. major shipping routes that traverse planned or actual flight paths of other Devices where urban areas also include harbours and seaways or canals. Feature (8) is the main algorithm for processing the input Source Data from Features (1) though (7), see Figure 2 and the next subsection for details of this algorithm. Feature (24) is the Data Fused Rich Data output from the main algorithm Feature (8) and may take the form of databases or information schema or tables of calculated Derived Intelligence such as risk and/or sets of 3D plots of risk levels, such as colour coded maps of risk levels.

In Figure (2), Feature (10) is time-varying real-time live Source Data that is measured on either or both of the moving devices or in existing (or planned) infrastructure that measures and monitors for meteorological, air quality, electromagnetic radio frequency signal quality or any other measurement. Such Source Data may be stored on the device and accessed later, or transmitted by the device to another device or to some other collection or receiving service, application or infrastructure, or similarly requested from the device. Feature (11) is offboard from the devices and is a means to use any number of Source Data sets for a predictive, or for a predictor-corrector, or for a refinement or adjustment where any of the original Source Data has deficiencies such as poor resolution, data gaps (whether spatial or temporal) or in order to resolve conflicts between Source Data sets. Feature (12) is a rule set which places constraints on Source Data such that the fusion algorithms and processes are best configured to generate outputs that are appropriate for whatever the particular end use. For elaboration, slow moving devices needs fused data at a lower frequency than fast moving devices and Feature (12) ensures that all such considerations are effectively managed and delivered. Feature (13), in similarity to Feature (12), ensures that the appropriate fused data is delivered to the particular end use, and that inappropriate data is not. Feature (14) prepares and pre-conditions classes of fused data which is then fed into Feature (15) where the fused data are then converted into measures of risk appropriate for whatever the particular end use. For the avoidance of doubt, the majority of the Data Fusion takes place in Feature (14).

The main algorithm for the proposed invention is shown in Figure (3).

The data needed to assess the risks to the safe operation of the drones and air vehicles within the Complex Environment is taken from the output of Feature (15) into Feature (16) Data input, which checks that the data input from Feature (15) is of the correct format and quality for use by the main algorithm. The output of Feature (16) then goes to the Feature (17) which creates a functional connection that serves the purpose of spatially and temporarily aligning Source Data in readiness for Data Fusion which is performed by the algorithms of Feature (18). This is followed by a range of Error Correction methods of Feature (19) before any Mathematical Weighting of Data Types, which takes the data from Feature (19) and the data from Feature (21) Baseline Risk Assessment Weighting Library, which contains weighting, or adjustment values for the sources of data provided by Feature (19) based on both historic risk realisation and the predicted conditions also indicated in the data from Feature (19) examples of which include forecasted weather conditions and forecasted air traffic conditions within the Complex Environment to be assessed for risks. Feature (20) takes the weighted values of data and further weights then based on historic and predicted conditions.

The output of Feature (20) is then passed to Feature (22) Calculation of Predicted Risk Assessment, which draws information from Features (23), (24) and (25) which are Feature (23) Basic Risk Assessment Map Library, which contains historic risk maps of the risk to drones and air vehicles for the Complex Environment, Feature (24) Statistical Process Algorithms and Feature (25) Historic Data Assessment and Model Library, to look for patterns of circumstances that have led to incidents involving drones and air vehicles in the Complex Environment underassessment and similar ones. Feature (22) then draws together all these sources of information and calculates the compound risk for each block of air space within the Complex Environment for the time period that the algorithm predicts reliable data is available. Feature (22) then outputs its risk assessment to Feature (26) Assembly of Risk Map for Current Situation which takes the data from Feature (22) and generates maps of risk for the airspace within the Complex Environment in formats for the end users in the Drone and Air Vehicle operators, insurers, airspace control authorities, airspace safety data distributers and other third parties as demand arises.

A typical structure of the Risk Assessment Map Feature (26) is shown in Figure (4). This is one example of a plurality of data visualisations possible of the Risk Map of Feature (26) and shows an urban area where Feature (27), the columns, represent zones within the city, Feature (28), the column heights represent the level of risk within that zone and Feature (29), the grey scale represents type of risk.

Claims

The following CLAIMS are asserted in support of this invention. CLAIMS In the following, the term "risk level" is a form of derived intelligence. Throughout, a claim based on the term risk level is equally applicable to an equivalent claim for any other form of derived intelligence.1. A system that determines the risk level in a volume of airspace at a specific time or times in the future for air vehicles and Devices from loss of radio data links that provide information on the location of the Device in its environment.
2. A system that determines the risk level in a volume of airspace at a specific time or times in the future for air vehicles and Devices from loss of radio links that provide control commands from distant piloting functions.
3. A system that determines the risk level in a volume of air space at a specific time or times in the future for air vehicles and Devices from loss of radio data links that permit transfer of information to and from the vehicles and Devices.
4. A system that determines the risk level in a volume at a specific time or times in the future of air space for air vehicles and Devices from loss of control due to air movements caused by the interaction of the natural wind with both the natural topography on the ground under the airspace and also the man made environment such as buildings, bridges, flyovers, towers, masts, cranes, powerful exhaust from air conditioning and industrial plant and plants such as trees.
5. A system that determines the risk level in a volume of air space at a specific time or times in the future for air vehicles and Devices from collision with both the natural topography on the ground under the airspace and also the man made environment such as buildings, bridges, flyovers, towers, masts, cranes, cables billboards, tethered blimps, road traffic, harbour and seaway floating traffic, rail traffic and regular low flying aircraft such as the approaches to airports, airfields and heliports, sports projectiles including balls, arrows, javelins, kites, radio controlled models, shotgun pellets, rifle and pistol bullets and plants such as trees and wildlife such as bird sanctuaries in the otherwise unobstructed airspace.
6. A system that determines the risk level in a volume of air space at a specific time or times in the future for air vehicles and Devices as in Claims 1 to 5 by means of data input from sensors deployed around the airspace including radio channel monitoring for signal strength, signal to interference and noise levels.
7. A system that determines the risk level in a volume of air space at a specific time or times in the future for air vehicles and Devices as in Claims 1 to 5 by means of data input from sensors deployed around the airspace including radio channel monitoring for signal strength, signal to interference and noise levels.
8. A system that determines the risk level in a volume of air space at a specific time or times in the future for air vehicles and Devices as in Claims 1 to 5 by means of computational models of the ground environment in order to determine the radio frequency signal conditions due to multipath interference caused by reflections, diffractions and scattering from the environment in, under and around the airspace.
9. A system that determines the risk level in a volume of air space at a specific time or times in the future for air vehicles and Devices as in Claims 1 to S by means of computational models of the ground environment in order to determine the smooth wind and also turbulence conditions due to aerodynamic size, shape, location and orientation of the buildings and structures of the environment in, under and around the airspace.
10. A system that determines the risk level in a volume of air space at a specific time or times in the future for air vehicles and Devices as in Claims 1to 5 by means of computational algorithm that takes as input the data from claims 1 to 9 and fuses them into a risk level three dimensional map of risk levels within the defined airspace under consideration for use by air vehicle and Device operators, regulators, insurers and those undertaking planning of changes to the built environment.