TECHNICAL FIELD
The present disclosure generally relates to processes involving geospatial data
accuracy, and particularly, to a method and system for accuracy of geospatial data
received from an IoT device integrated with a vehicle, thereby significantly
5 lowering the associated location drifts in the received data and improving the
distance calculation thereto.
BACKGROUND
The rise of smartphones and the advent of app store have led to huge change in
urban mobility. Ridesharing and carpooling; meal delivery and freight; electric
10 bikes and scooters; and self-driving cars and urban aviation have brought about a
revolution in the field of transportation.
Several methods of GPS navigation and mobile device positioning are widely
utilized whereby the current location and travel path of a mobile device may be
indicated, measured, displayed, and/or superimposed upon a road map of the
15 region in which the mobile device is currently traveling or located. With such an
apparatus, it is essential to determine the current location of the mobile device as
accurately as possible under various external conditions.
However, with any positioning system, the absolute location and direction
estimates obtained may contain substantial amount of randomly varying error,
20 also known as drifts, that is to say, the geospatial data received from the mobile
devices is not accurate owing to the increased location drifts, thereby leading to
anomalies or deviations in distance calculation and resulting in false analysis and
calculations for the purposes of managing and operating the vehicle fleet
integrated with said mobile devices.
25 It is for the abovementioned reason, the position, travel direction, and other
important information derived from the positional data, such as, GPS positional
data, are required to be subjected to multiple form of filter processing to reduce
3
the effects of the random errors or the location drifts appearing in the data, thereby
helping significantly optimise the geospatial data and improve the resultant
distance calculations for the mobile device and the vehicle thereto.
Therefore, in light of the foregoing limitations and constraints associated with the
5 accuracy of geospatial data received from the mobile devices integrated with the
vehicles, and the resultant miscalculations and shortcomings in the distance
calculation, there exists an urgent need to provide a solution which is simple,
practical, and cost-effective in operation, aiming to overcome the shortcomings
and deficiencies prevalent in state-of-the-art.
10 SUMMARY
Before the present systems and methods, are described, it is to be understood that
this application is not limited to the particular systems, and methodologies
described, as there can be multiple possible embodiments which are not expressly
illustrated in the present disclosure. It is also to be understood that the
15 terminology used in the description is for the purpose of describing the particular
versions or embodiments only and is not intended to limit the scope of the present
application. This summary is provided to introduce concepts related to geospatial
data accuracy, and the method and system for accuracy of geospatial data received
from an IoT device integrated with a vehicle, thereby significantly lowering the
20 associated location drifts in the received data and improving the distance
calculation thereto, and the concepts are further described below in the detailed
description. This summary is not intended to identify essential features of the
claimed subject matter nor is it intended for use in determining or limiting the
scope of the claimed subject matter.
25
According to a first aspect, an embodiment of the present disclosure provides a
method for accuracy of geospatial data received from an IoT device integrated
with a vehicle, comprising:
4
receiving the geospatial data of the vehicle from a GPS tracker embedded
with the IoT device;
filtering out data points where the vehicle ignition is ON as the first level
of filtration;
5 passing the geospatial data received from the GPS tracker through second
level of filtration;
calculating speed of the vehicle using the geospatial data received from the
GPS tracker;
correlating the calculated speed of the vehicle with the threshold of
10 maximum speed for the vehicle as the third level of filtration;
eliminating the data points wherein the calculated speed of the vehicle
exceeds the threshold of maximum speed for the vehicle;
achieving the optimized geospatial data after running through multiple
levels of filtration and removing the anomalies associated with the
15 geospatial data received from the GPS tracker; and passing on the
filtered geospatial data on to the distance calculation measurements and
calculating accurate distance travelled by the vehicle.
In an embodiment, the geospatial data comprises latitude, longitude, altitude, and
20 direction of the vehicle.
In an embodiment, the second level of filtration of the geospatial data received
from the GPS tracker comprises a Kalman filter algorithm.
25 In an embodiment, the threshold of maximum speed for the vehicle is a default
threshold or configurable by a user.
In another embodiment, the threshold of maximum speed for the vehicle is a
function of the terrain, topographical information, and speed limits of roads.
30
5
In another embodiment, the anomalies associated with the geospatial data received
from the GPS tracker is a function of location drifts in the geospatial data received
from the GPS tracker.
5 In another embodiment, the optimized geospatial data received through multiple
levels of filtration results in better utilization of vehicle fleet.
In yet another embodiment, the vehicle is one of an electric, solar or hydrogen
powered vehicle.
10
In yet another embodiment, the vehicle comprises two- and three-wheeled
vehicles.
According to a second aspect, an embodiment of the present disclosure provides a
15 system for accuracy of geospatial data received from an IoT device integrated
with a vehicle, performing the steps of method.
Additional aspects, advantages, features and objects of the present disclosure
would be made apparent from the drawings and the detailed description of the
illustrative embodiments construed in conjunction with the appended claims that
20 follow.
It will be appreciated that features of the present disclosure are susceptible to
being combined in various combinations without departing from the scope of the
present disclosure as defined by the appended claims.
A better understanding of the present invention may be obtained through the
25 following examples which are set forth to illustrate but are not to be construed as
limiting the present invention.
The present invention is included in the general business context, which aims to
substitute vehicles powered by traditional fuels, for example gasoline or diesel, by
6
vehicles propelled by renewable sources of energy, such as, electric, solar,
hydrogen, or hybrid. In particular, the present invention is intended for use in
electric vehicles used within cities, which can be highly beneficial to the local
environment due to significant reduction of gaseous emissions as well as
5 significant reduction of noise. Overall environmental benefits can also be
significant when electric vehicles are charged from renewable energy sources.
DESCRIPTION OF THE DRAWINGS
The summary above, as well as the following detailed description of illustrative
embodiments, is better understood when read in conjunction with the appended
10 drawings. For the purpose of illustrating the present disclosure, exemplary
constructions of the disclosure are shown in the drawings. However, the present
disclosure is not limited to specific methods and instrumentalities disclosed
herein. Moreover, those in the art will understand that the drawings are not to
scale. Wherever possible, like elements have been indicated by identical numbers.
15 Embodiments of the present disclosure will now be described, by way of example
only, with reference to the following diagrams wherein:
FIG. 1 is a flow chart depicting a method for accuracy of geospatial data received
from an IoT device integrated with a vehicle, in accordance with an
20 embodiment of the present disclosure.
FIG. 2 is a visual representation depicting the geospatial data (in raw form)
received from the IoT device integrated with the vehicle, in
accordance with an embodiment of the present disclosure.
25 FIG. 3 is a visual representation depicting the geospatial data having passed
through two levels of filtration, in accordance with an embodiment
of the present disclosure.
7
FIG. 4 is a visual representation depicting the geospatial data having passed
through three levels of filtration, in accordance with an
embodiment of the present disclosure.
In the accompanying drawings, an underlined number is employed to represent an
5 item over which the underlined number is positioned or an item to which the
underlined number is adjacent. A non-underlined number relates to an item
identified by a line linking the non-underlined number to the item. When a
number is non-underlined and accompanied by an associated arrow, the nonunderlined number is used to identify a general item at which the arrow is
10 pointing.
DESCRIPTION OF EMBODIMENTS
The following detailed description illustrates embodiments of the present
disclosure and ways in which they can be implemented. Although some modes of
carrying out the present disclosure have been disclosed, those skilled in the art
15 would recognize that other embodiments for carrying out or practicing the present
disclosure are also possible.
As required, detailed embodiments of the present disclosure are disclosed herein;
however, it is to be understood that the disclosed embodiments are merely
exemplary of the disclosure which may be embodied in various forms. Therefore,
20 specific structural and functional details disclosed herein are not to be interpreted
as limiting, but merely as a basis for the claims and as a representative basis for
teaching one skilled in the art to variously employ the present disclosure in
virtually any appropriately detailed structure.
Various other objects, advantages, and features of the disclosure will become
25 more readily apparent to those skilled in the art from the following detailed
description when read in conjunction with the accompanying drawings, in which
like reference numerals designate like parts throughout the figures thereof.
8
In a first aspect, an embodiment of the present disclosure provides a method for
accuracy of geospatial data received from an IoT device integrated with a vehicle,
comprising:
receiving the geospatial data of the vehicle from a GPS tracker embedded
5 with said IoT device;
filtering out data points where the vehicle ignition is ON as the first level
of filtration;
passing the geospatial data received from the GPS tracker through second
level of filtration;
10 calculating speed of the vehicle using the geospatial data received from the
GPS tracker;
correlating the calculated speed of the vehicle with the threshold of
maximum speed for the vehicle as the third level of filtration;
eliminating the data points wherein the calculated speed of the vehicle
15 exceeds the threshold of maximum speed for the vehicle;
achieving the optimized geospatial data after running through multiple
levels of filtration and removing the anomalies associated with the
geospatial data received from the GPS tracker; and passing on the
filtered geospatial data on to the distance calculation measurements and
20 calculating accurate distance travelled by the vehicle.
In a second aspect, an embodiment of the present disclosure provides a a system
for accuracy of geospatial data received from an IoT device integrated with a
vehicle, performing the steps of method.
25 Embodiments of the present disclosure provide the abovementioned method and
system.
The server arrangement is coupled in communication with the database
arrangement. The server arrangement is configured to access the information that
30 is stored at the database arrangement.
9
It will be appreciated that the vehicle information is stored in the database
arrangement associated with the server arrangement. A record of information
pertaining to all the vehicles that are associated with the mobility service provider
5 is stored in the database arrangement, which can be accessed by the server
arrangement.
The term "vehicle", as used herein, generally relates to an electric two- and threewheeler vehicles. In some cases, the two- and three-wheeler vehicles can also be
10 hybrid vehicles, or vehicles running on alternate renewable and clean sources of
energy, such as, solar and hydrogen. Throughout the present disclosure, the term
"electric vehicle" refers to a vehicle using electric battery to provide electricity to
the wheels to enable rotatory movement thereto, resulting in movement of the
vehicle from a desired first position to a desired second position. Moreover, the
15 electric vehicle is independent of the combustible fuels such as petrol and diesel.
Generally, the electric vehicle is independent of a combustion engine to provide
power to the vehicle. It will be appreciated that the term electric vehicle may
interchangeably be used in the present disclosure with terms such as vehicle,
electric three-wheeler vehicle, electric two-wheeler vehicle, three-wheeler vehicle,
20 two-wheeler vehicle, mobility vehicle, etc., as can be comprehended by a person
skilled in the art.
The electric vehicle comprises a controller to control the vehicular operation.
Throughout the present disclosure, the term "controller" as used herein refers to
25 programmable and/or non-programmable components configured to control at
least one vehicle operation. Moreover, the controller comprises a plurality of
instructions for controlling the at least one vehicle operation. Optionally, the
controller includes hardware, software, firmware or a combination of these,
suitable for controlling the at least one vehicle operation. Optionally, the
30 controller includes functional components, for example, a processor, a memory, a
10
network adapter, and so forth. Optionally, the controller may be connected to a
sever arrangement to enable amendments of the aforesaid plurality of instructions.
The term "user", as used herein, generally refers to an individual or entity that
uses systems and methods of the present disclosure. A user may be an individual
5 or entity that wishes to use the vehicle, for example, a rider (e.g., driver,
passenger) of the vehicle.
The term "geographical location" (also "geo-location" and "geolocation" herein),
as used herein, generally refers to the geographic location of an object, such as, a
10 user. A geolocation of a user can be determined or approximated using a
geolocation device or system associated with the user, which may be an electronic
device (e.g., mobile device) attached to or in proximity to the user. Geographic
information can include the geographic location of the object, such as coordinates
of the object and/or an algorithm or methodology to approximate or otherwise
15 calculate (or measure) the location of the object, and, in some cases, information
as to other objects in proximity to the object. In some examples, the geographic
information of a user includes the user's geographic location and/or the location of
one or more vehicles in proximity to the user. Geographic information can include
the relative positioning between objects, such as between users, or a user and a
20 vehicle. In some cases, the geolocation of an object (e.g., user, electronic device)
is not necessarily the location of the object, but rather the location that the object
enters an area or structure, such as, a building.
A geolocation device may be a portable (or mobile) electronic device, such as, for
25 example, a smart phone or a tablet personal computer. The geolocation device
refers to an electronic computing device which may execute and run various types
of software to perform the computational tasks, which may or may not be
handheld. Examples of the geolocation device include, but are not limited to, a
mobile device, a smartphone, a desktop computer, a laptop computer, a
11
Chromebook, a tablet computer, and a specialized dedicated device (e.g. a POS
machine).
In some cases, the geolocation of an object, such as a vehicle, can be determined
5 using the manner in which a mobile device associated with the object
communicates with a communication node, such as, a wireless node. In an
example, the geolocation of an object can be determined using node triangulation,
such as, e.g., wireless node, WiFi (or Wi-Fi) node, satellite triangulation, and/or
cellular tower node triangulation. In another example, the geolocation of a user
10 can be determined by assessing the proximity of the user to a WiFi hotspot or one
or more wireless routers. In some cases, the geolocation of an object can be
determined using a geolocation device that includes a global positioning system
("GPS"), such as a GPS subsystem (or module) associated with a mobile device.
15 In some situations, the geolocation of an object can be determined with the aid of
visual and/or audio information captured by an electronic device of the user, such
as, for example, images and/or video captured by a camera of the electronic
device.
20 The term "geospatial data", as used herein, generally refers to the information that
describes objects, events or other features with a location on or near the surface of
the earth. In other words, geospatial is anything that consists of, derive from, or
relates to data that is directly linked to specific geographical locations.
The present disclosure comprises a processing arrangement configured for
25 carrying out the whole purpose of the invention i.e., to bring in accuracy in the
geospatial data received from IoT device integrated with a vehicle. The
“processing arrangement” refers to a structure and/or module that includes
programmable and/or non-programmable components configured to store, process
and/or share information or data for accuracy in the geospatial data received from
30 IoT device integrated with the vehicle to help significantly reduce the location
12
drifts associated with the received data and improve the distance calculation
thereafter. Optionally, the processing arrangement includes any arrangement of
physical or virtual computational entities capable of enhancing information to
perform various computational tasks. Furthermore, it will be appreciated that the
5 processing arrangement may be implemented as a hardware server and/or plurality
of hardware servers operating in a parallel or in a distributed architecture.
Optionally, the processing arrangement is supplemented with additional
computation system, such as, neural networks, and hierarchical clusters of pseudoanalog variable state machines implementing artificial intelligence algorithms. In
10 an example, the processing arrangement may include components, such as, a
memory, a processor, a data communication interface, a network adapter, and the
like, to store, process and/or share information with other computing devices.
Optionally, the processing arrangement is implemented as a computer program
that provides various services (such as database service) to other devices,
15 modules, or apparatus. Moreover, the processing arrangement refers to a
computational element that is operable to respond to and process instructions to
perform the data access transactions. Optionally, the processing arrangement
includes, but is not limited to, a microprocessor, a microcontroller, a complex
instruction set computing (CISC) microprocessor, a reduced instruction set
20 (RISC) microprocessor, a very long instruction word (VLIW) microprocessor,
Field Programmable Gate Array (FPGA) or any other type of processing circuit,
for example, as aforementioned. Additionally, the processing arrangement is
arranged in various architectures for responding to and processing the instructions
enabling accuracy of the geospatial data received from IoT device integrated with
25 the vehicle via the claimed system and the method. For the purposes of the present
disclosure, the processing arrangement is configured to perform various functions
in a specified order.
As used herein, the term “processor” can include general purpose processors,
specialized processors such as central processing units (CPUs), graphics
30 processing units (GPUs), digital signal processors (DSPs), microcontrollers
13
(MCUs), embedded controller (ECs), field programmable gate arrays (FPGAs), or
other types of specialized processors, as well as base band processors used in
transceivers to send, receive, and process wireless communications.
In an embodiment, the vehicle is configured to host a software application
5 management and infotainment (RTM) arrangement (SAMI (RTM)). The term
‘software application management and infotainment (RTM) arrangement’ used
herein relates to a device-functionality software and/or an operating system
software configured to execute other application programs and interface between
the application programs and associated hardware. In an example, the software
10 application management and infotainment (RTM) arrangement may be operating
within a dashboard of the vehicle. In an example, the software application
management and infotainment (RTM) arrangement may be operable to provide an
infotainment (RTM) arrangement and/or system for the user of the vehicle, such
as making phone calls, and accessing web-based content, such as, traffic
15 conditions and weather forecasts, and so forth.
The vehicle may further include sensors connected to a controller, and a memory
(not shown) that stores information relating geospatial data, amongst other related
information, etc. The vehicle may further include communication circuits for
communicating with networks and servers wirelessly. In an embodiment, the
20 vehicle may further include one or more and/or additional sensors, a speedometer.
Each of these sensors and components may be selectively and electrically
communicatively coupled together, as appropriate, such that data can be
transferred between them, as intended. Those skilled in the art will recognize still
other possible sensor types and associated measurables, as well as recognize that
25 the system can comprise any combination of sensor types and measurables.
It is noted that the system may comprise a single processor or any number of
processors, as well as one or more memories that perform the processing functions
of the entire system. For example, a single processor may be used to process data
from the mobile device, also referred to as an IoT device integrated with the
14
vehicle. As such, the terms “device” and the “component” may comprise
hardware portions, software portions, or both depending upon how the system is
designed and configured.
In an embodiment, the vehicle may be coupled with a mobile device, such as, a
5 mobile phone or a tablet computer. The mobile device may be equipped with one
or more sensors that are capable of collecting data, such as accelerometers,
gyroscopes, microphones, cameras, and compasses. The mobile device may also
be capable of wireless data transmission.
In an embodiment, one or more cameras may be disposed on the vehicle. The
10 cameras may collectively form a vision sensing system. Multiple cameras may be
provided. The cameras may be capable of capturing image data for environmental
sensing. The cameras may be the same type of cameras or different types of
cameras. In some embodiments, the cameras may include stereo cameras.
Optionally, the cameras may include one or more monocular cameras. In some
15 instances, combinations of stereo cameras and monocular cameras may be
provided. Any description herein of cameras may apply to any type of vision
sensors and may be referred to interchangeably as imaging devices. Although
certain cases provided herein are described in the context of cameras, it shall be
understood that the present disclosure can be applied to any suitable imaging
20 device, and any description herein relating to cameras can also be applied to any
suitable imaging device, and any description herein relating to cameras can also
be applied to other types of imaging devices.
In an embodiment, the vehicle may include one or more sensors to detect objects
near it. For example, the vehicle may include proximity sensors that can detect
25 vertical structures, such as, buildings, towers, etc. The collected proximity sensor
data may be used to determine a distance from the vehicle to the detected
buildings and other structures, which may be indicative of the route or path of the
vehicle.
15
It is recognized that one or more of the sensors may be subject to error in the form
of drift, also referred to as location drifts, and thus, the current measurements may
be inaccurate to some degree affected by the location drifts in the data points
received from the geospatial data from GPS tracker embedded with IoT device
5 and integrated with the vehicle. As such, the present disclosure makes use of
multiple levels of filtration to help correct and control the location drifts resulting
into anomalies in the data, and provide optimized geospatial data with significant
correction in the anomaly causing location drifts, thereby resulting in improved
and trusted distance calculation measurements for the vehicle.
10 In the present disclosure, the threshold of maximum speed for the vehicle
represents the speed that the vehicle should not exceed while traveling at a
particular location i.e., the maximum threshold speed of the vehicle. In an
example, the threshold maximum speed may be known, and based on the legal
requirements for speed for any given location. In another example, the maximum
15 threshold speed may be based on predetermined speed or velocity restrictions or
limits to safely travel at any given location. The said restrictions or limits, for
example, may be based on one or more characteristics pertaining to a particular
vehicle intended to operate at the defined location, such as, but not limited to, the
type of vehicle, the weight of the vehicle, the center of gravity of the vehicle, the
20 type of load being carried by the vehicle, and others as can be well recognized and
comprehended by persons skilled in the art. Further, the person skilled in the art
may recognize various other possibilities and parameters for defining and/or
assigning possible restrictions to the maximum threshold speed of the vehicle.
Referring FIG. 1, illustrated is a flow chart depicting the steps of a method for
25 accuracy of geospatial data received from an IoT device integrated with a vehicle,
in accordance with an embodiment of the present disclosure. At a step 102, the
geospatial data of the vehicle, including longitude, latitude, altitude, direction,
etc., is received from a GPS tracker embedded with the IoT device. The IoT
device may be any of the hardware, such as sensors, actuators, gadgets,
30 appliances, or machines, etc., that are programmed for certain applications and
16
can transmit data over the internet or other networks. At a further step 104, the
geospatial data is passed through first level of filtration, such that the data points
where the status of Ignition is “ON” i.e., when the vehicle is “ON” or in the “start
mode” are considered for filtration. At a step 106, the geospatial data is passed
5 through next level of filtration.
It is to be noted that in order to reduce the amount of location drifts or errors in
the geospatial data that is received from the IoT device integrated with the
vehicle, and improve the distance calculations using the optimized geospatial data,
10 the Kalman filter acts as the error correction or reduction component under the
second level of filtration process. The Kalman filter may receive positional data or
information from one or more sensors or components, such as, accelerometers,
gyroscopes, and the like, that are embedded with the IoT or mobile device
integrated with the vehicle. As such, data from different sources or components
15 may be received by the Kalman filter for the purposes of correcting/reducing the
location drifts in the geospatial data, and generating more accurate location
coordinates, i.e., latitude and longitude of the mobile device or the vehicle.
Those skilled in the art will recognize that the Kalman filter is not intended to be
20 limiting in any way, and that this only comprises one type of error correction or
reduction component. Indeed, other types may be used, as will be apparent to
those skilled in the art. The Kalman filter is an estimator that incorporates inputs
in an optimal manner (per the kinematic system model), and thus, reduces error. It
also smooths out statistical errors from the sequence of incoming measurements
25 i.e., the positional data.
The step 108 involves speed calculation of the vehicle using the geospatial data
received from the GPS tracker. The step 110 involves third level of filtration, such
that the calculated speed of the vehicle is correlated with the threshold of
30 maximum speed for the vehicle, which is, for example, 60 km/hr, and is not
limiting in any way, and can be any suitable speed as per the suitabilities,
17
requirements, and different restrictions/constraints pertaining to different kinds of
vehicles, terrains, etc., as listed above and can be well recognized and
comprehended by a person skilled in the art. As a consequence of the speed
correlation, the data points for which the calculated speed of the vehicle exceeds
5 the threshold of maximum speed for the vehicle are eliminated from the filtration
process, and as a result of the same, the optimized geospatial data is achieved
under step 112, after having run through multiple levels of filtration, such that the
anomalies associated with the geospatial data received from the GPS tracker are
removed, and the final filtered data is passed on to the distance calculation
10 measurements for accurate calculations of the distance travelled by the vehicle,
under the final step 114 of the method.
The steps 102 to 114 of the method illustrated hereinabove, are only illustrative
and other alternatives can also be provided where one or more steps are added,
15 one or more steps are removed, or one or more steps are provided in a different
sequence without departing from the scope of the claims herein.
Moreover, the present disclosure also relates to a system for accuracy of
geospatial data received from an IoT device integrated with a vehicle, as described
above. The various embodiments and variants disclosed above apply mutatis
20 mutandis to the present system.
Referring FIG. 2, a visual representation 200 of the raw geospatial data received
from the IoT device integrated with the vehicle is depicted in accordance with an
embodiment of the present disclosure. The figure is for illustrative purposes only,
25 and should not be considered limiting in nature. The frequency of receiving the
raw geospatial data from the IoT device is, for example, set at 30 seconds, for this
very illustration, and is again not limiting in nature, and could be any suitable time
frame, as can be recognized by a person skilled in the art. Further, in an example,
18
as illustrated herein, the distance travelled by the vehicle as calculated from the
readings of the raw geospatial data received from the IoT device is 408 kms.
Referring FIG. 3, a visual representation 300 of the geospatial data having passed
through two levels of filtration is depicted in accordance with an embodiment of
5 the present disclosure. In an aspect, the raw geospatial data received from the IoT
device is made to pass through two levels of filtration aiming to correct and/or
reduce/lower the location drifts associated with the received data and remove the
anomalies thereto, helping derive accurate distance calculations. In the illustration
as depicted, at a first level filtration, only the data points with status ignition of the
10 vehicle “ON” are considered and filtered accordingly, making the data further
pass through, for example, Kalman filter algorithm, as the second level of
filtration. It will be appreciated there could be any suitable algorithm, other than
Kalman filter algorithm, as can be perceived by a person skilled in the art, and the
present example and illustration as shown in an embodiment of the presented
15 disclosure should not be construed to be limiting in nature. It is to be noted that
the two levels of filtration as mentioned hereinabove, helps significantly
correct/reduce the location drifts and provides much accurate distance
calculations, upon the readings of the data points made to pass through said
filtrations being plotted on the data frame. Accordingly, the distance travelled by
20 the vehicle, as in the present example of illustration, after the two levels of
filtration is recorded as 207 kms.
Referring FIG. 4, a visual representation 400 of the geospatial data having passed
through three levels of filtration is depicted in accordance with an embodiment of
the present disclosure. After two levels of filtration, the data is made to pass
25 through another level of filtration. The speed of the vehicle is calculated at every
latitude, longitude and compared with the maximum speed of the vehicle, which,
for the purposes of present example is 60 km/hr, and by no means, limiting in any
context. Further, the data points in which the calculated speed is less than the
threshold of maximum speed for the vehicle are taken into consideration and
30 passed on to the distance calculation formula or measurements, in the form of the
19
optimized filtered geospatial data, helping provide accurate distance calculations,
which in the present example, in the final step after three levels of filtration, is
found to be 87 kms only, thereby highlighting a significant and marked
improvements from that of the unfiltered data.
5 Methods as described herein can be implemented by way of machine (e.g.,
computer processor) executable code stored on an electronic storage location of
the computer system. The machine executable or machine-readable code can be
provided in the form of software. During use, the code can be executed by a
processor. The code can be pre-compiled and configured for use with a machine
10 having a processor adapted to execute the code, or can be compiled during
runtime. The code can be supplied in a programming language that can be
selected to enable the code to execute in a pre-compiled or as-compiled fashion.
Aspects of the systems and methods provided herein can be embodied in
programming. Various aspects of the technology may be thought of as “products”
15 or “articles of manufacture” typically in the form of machine (or processor)
executable code and/or associated data that is carried on or embodied in a type of
machine readable medium. Machine-executable code can be stored on an
electronic storage unit, such as memory (e.g., read-only memory, random-access
memory, flash memory) or a hard disk. “Storage” type media can include any or
20 all of the tangible memory of the computers, processors or the like, or associated
modules thereof, such as various semiconductor memories, tape drives, disk
drives and the like, which may provide non-transitory storage at any time for the
software programming. All or portions of the software may at times be
communicated through the Internet or various other telecommunication networks.
25 Such communications, for example, may enable loading of the software from one
computer or processor into another, for example, from a management server or
host computer into the computer platform of an application server. Thus, another
type of media that may bear the software elements includes optical, electrical and
electromagnetic waves, such as used across physical interfaces between local
30 devices, through wired and optical landline networks and over various air-links.
20
The physical elements that carry such waves, such as wired or wireless links,
optical links or the like, also may be considered as media bearing the software. As
used herein, unless restricted to non-transitory, tangible “storage” media, terms
such as computer or machine “readable medium” refer to any medium that
5 participates in providing instructions to a processor for execution. Hence, a
machine readable medium, such as computer-executable code, may take many
forms, including but not limited to, a tangible storage medium, a carrier wave
medium or physical transmission medium. Non-volatile storage media include, for
example, optical or magnetic disks, such as any of the storage devices in any
10 computer(s) or the like, such as may be used to implement the databases, etc.
shown in the drawings. Volatile storage media include dynamic memory, such as
main memory of such a computer platform. Tangible transmission media include
coaxial cables; copper wire and fiber optics, including the wires that comprise a
bus within a computer system. Carrier-wave transmission media may take the
15 form of electric or electromagnetic signals, or acoustic or light waves such as
those generated during radio frequency (RF) and infrared (IR) data
communications. Common forms of computer-readable media therefore include
for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other
magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium,
20 punch cards paper tape, any other physical storage medium with patterns of holes,
a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory
chip or cartridge, a carrier wave transporting data or instructions, cables or links
transporting such a carrier wave, or any other medium from which a computer
may read programming code and/or data. Many of these forms of computer
25 readable media may be involved in carrying one or more sequences of one or
more instructions to a processor for execution.
As indicated above, the techniques introduced here implemented by, for example,
programmable circuitry (e.g., one or more microprocessors), programmed with
software and/or firmware, entirely in special-purpose hardwired (i.e., non30 programmable) circuitry, or in a combination or such forms. Special-purpose
21
circuitry can be in the form of, for example, one or more application-specific
integrated circuits (ASIC s), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), etc.
5 The term “data processing apparatus” refers to data processing hardware and
encompasses all kinds of apparatus, devices, and machines for processing data,
including by way of example a programmable processor, a computer, or multiple
processors or computers. The apparatus can also be, or further include, special
purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an
10 ASIC (application specific integrated circuit). The apparatus can optionally
include, in addition to hardware, code that creates an execution environment for
computer programs, e.g., code that constitutes processor firmware, a protocol
stack, a database management system, an operating system, or a combination of
one or more of them.
15
Data processing apparatus for implementing machine learning models can also
include, for example, special-purpose hardware accelerator units for processing
common and compute-intensive parts of machine learning training or production,
i.e., inference, workloads.
20
Machine learning models can be implemented and deployed using a machine
learning framework, e.g., a TensorFlow framework, a Microsoft Cognitive Toolkit
framework, an Apache Singa framework, or an Apache MXNet framework.
25 Another aspect provides a computer readable medium comprising machineexecutable code that, upon execution by a computer processor, implements any of
the methods above or elsewhere herein, alone or in combination.
Another aspect provides a system comprising a computer processor (or other
30 logic) and a memory location coupled to the computer processor, the memory
location comprising machine-executable code that, upon execution by the
22
computer processor, implements any of the methods above or elsewhere herein,
alone or in combination.
Modifications to embodiments of the invention described in the foregoing are
5 possible without departing from the scope of the invention as defined by the
accompanying claims. Expressions such as “including”, “comprising”,
“incorporating”, “consisting of”, “have”, “is” used to describe and claim the
present invention are intended to be construed in a non-exclusive manner, namely
allowing for items, components or elements not explicitly described also to be
10 present. Reference to the singular is also to be construed to relate to the plural
where appropriate.
While the present invention is illustrated by description of several embodiments
and while the illustrative embodiments are described in detail, it is not the
intention of the applicants to restrict or in any way limit the scope of the appended
15 claims to such detail. Additional advantages and modifications within the scope of
the appended claims will readily appear to those sufficed in the art. The invention
in its broader aspects is therefore not limited to the specific details, representative
apparatus and methods, and illustrative examples shown and described.
Accordingly, departures may be made from such details without departing from
20 the spirit or scope of applicants' general concept.
Numerals included within parentheses in the accompanying claims are intended to
assist understanding of the claims and should not be construed in any way to limit
subject matter claimed by these claims.

We Claim:
1. A method for accuracy of geospatial data received from an IoT device
integrated with a vehicle, comprising:
5 receiving the geospatial data of said vehicle from a GPS tracker embedded
with said IoT device;
filtering out data points where the vehicle ignition is ON as the first level
of filtration;
passing the geospatial data received from said GPS tracker through second
10 level of filtration;
calculating speed of said vehicle using the geospatial data received from
said GPS tracker;
correlating the calculated speed of said vehicle with the threshold of
maximum speed for said vehicle as the third level of filtration;
15 eliminating the data points wherein the calculated speed of said vehicle
exceeds the threshold of maximum speed for said vehicle;
achieving the optimized geospatial data after running through multiple
levels of filtration and removing the anomalies associated with the
geospatial data received from said GPS tracker; and
20 passing on the filtered geospatial data on to the distance calculation
measurements and calculating accurate distance travelled by said vehicle.
2. The method as claimed in claim 1, wherein the geospatial data comprises
latitude, longitude, altitude, and direction of said vehicle.
25
3. The method as claimed in claim 1, wherein the second level of filtration of
the geospatial data received from said GPS tracker comprises a Kalman
filter algorithm.
24
4. The method as claimed in claim 1, wherein the threshold of maximum
speed for said vehicle is a default threshold or configurable by a user.
5. The method as claimed in claim 4, wherein the threshold of maximum
5 speed for said vehicle is a function of the terrain, topographical
information, and speed limits of roads.
6. The method as claimed in claim 1, wherein the anomalies associated with
the geospatial data received from said GPS tracker is a function of location
10 drifts in the geospatial data received from said GPS tracker.
7. The method as claimed in claim 1, wherein the optimized geospatial data
received through multiple levels of filtration results in better utilization of
vehicle fleet.
15
8. The method as claimed in any of the preceding claims, wherein said
vehicle is one of an electric, solar or hydrogen powered vehicle.
9. The method as claimed in claim 8, wherein said vehicle comprises two20 and three-wheeled vehicles.
10. A system for accuracy of geospatial data received from an IoT device
integrated with a vehicle, performing the steps of method as claimed in
any one of the preceding claims 1-9