Method and system for location prediction of a vehicle during loss of real time location signals

Abstract

Disclosed herein is a method and a system (200) for predicting a location of a vehicle (107) when one or more real time location signals are lost. The method comprises receiving a prerequisite data upon detecting a loss of one or more signals required to track a real time location of a vehicle (107). Based on the prerequisite data the method further comprises fetching a route map associated with the trip of the vehicle (107) and a time taken for traveling a particular distance on the trip till a point of loss of the one or more signals. The method further comprises determining an average speed of the vehicle (107) with respect to the time. Lastly the method further predicts a location of the vehicle based on the average speed and the time. [Figure 2]

Description

TECHNICAL FIELD

[001] The present invention generally relates to the field of fleet management, and more particularly relates to a method and system for predicting a location of a vehicle when one or more real time location signals are lost.BACKGROUND

[002] The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

[003] For a vehicle, tracking a location of the vehicle plays an important role in understanding the distance travelled and helps in estimating a time of arrival of the vehicle at the desired destination. Especially, in logistics, where a fleet of vehicles for example, trucks travel for long distances across varied weather conditions and tough terrains. In suchcases tracking the location of the fleet vehicle is very important for safety reasons and to plan the rest of the fleet management. Currently a real time location is fetched from a Global Positioning System (GPS) by utilizing one or more vehicle GPS tracking devices and sent to the cloud via a cellular network such Global System for Mobile Communications (GSM) using telematics unit. The location is used by fleet owners totrack vehicles. However, in certain scenarios e. g., inside tunnels, bad weather conditions or due to onboard component failure, a loss of GPS signal may be experienced. This causes loss of transmission of the location information of the vehicle. In such cases, the fleet owner won't be able to track fleet vehicles which affects fleet management. Furthermore, loss of track of the fleet vehicle may lead to safety andsecurity concerns.

[004] Currently there are some location prediction techniques that can predict the location of the vehicle in case of real time positioning signal loss. However, such techniques fail to consider all the essential parameters of the vehicle and the trip in calculating andpredicting the location of the vehicle. Therefore, the existing techniques of predicting the location of the vehicle are inaccurate and not precise in predicting the location.

[005] JP2022023388A discloses a method and device for determining vehicle position. The device is configured to specify the position of a vehicle with high accuracy. The device acquires the position of the vehicle, the speed of the vehicle, a yaw rate of the vehicle, a sideslip angle of the vehicle to determine the position of the vehicle. Therefore, this technique does not teach predicting the location of the vehicle. Further, the parameters considered are useful only for determining position and not the location of the vehicle.

[006] There is therefore a need for a method that overcomes the limitations stated above to accurately predict the location of the vehicle.SUMMARY

[007] The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages. Embodiments and aspects of the disclosure described in detail herein are considered a part of the claimed disclosure.

[008] In one non-limiting embodiment of the present disclosure, an AI based method of predicting a location of a vehicle is disclosed. The method further comprises receivinga prerequisite data upon detecting a loss of one or more signals required to track a real time location of a vehicle, wherein the prerequisite data comprises at least a set of data associated with a trip of the vehicle and a set of one or more real time data associated with the one or more parameters of the vehicle. Based on the prerequisite data, fetching a route map associated with the trip of the vehicle and a time taken for traveling aparticular distance on the trip till a point of loss of the one or more signals. Moving ahead, the method further based on the set of one or more real time data, determining an average speed of the vehicle with respect to the time. Lastly, the method describes predicting a location of the vehicle based on the average speed and the time.

[009] In another non-limiting embodiment of the present disclosure, wherein predicting the location of the vehicle based on the average speed and the time comprises calculating the location of the vehicle based on the average speed and the time. The method further comprises marking the location of the vehicle in a fleet board portal map. The methodfurther comprises transmitting the fleet board portal map to a computing device of afleet owner.

[0010] In yet another non-limiting embodiment of the present disclosure, wherein the set of data associated with the trip of the vehicle, comprises at least one or more of an end location of a trip, a terrain information, a traffic data and a time taken for the trip, wherein the set of real time data associated with the one or more parameters of the vehicle, comprises at least one or more of: an engine torque, an engine speed, a trailer pressure, a barometric pressure, an average speed and a vehicle weight in a particular trip.

[0011] In yet another non-limiting embodiment of the present disclosure, the method further comprises tracking an ignition cycle of the vehicle to update the one or more information associated with the trip of the vehicle and the real time data associated with the one or more parameters of the vehicle.

[0012] In yet another non-limiting embodiment of the present disclosure, the method further comprises tracking a moving or an idle state of the vehicle based on the set of real time data associated with the one or more parameters of the vehicle. The method further comprises updating the moving or an idle state of the vehicle on the fleet board portal map.

[0013] In yet another embodiment of the present disclosure, an AI based system for predicting a location of a vehicle, is disclosed. The AI based system comprises a memory, a processor and an AI based location prediction model. The processor receives a prerequisite data upon detecting a loss of one or more signals required to track a realtime location of a vehicle, wherein the prerequisite data comprises at least a set of data associated with a trip of the vehicle and a set of one or more real time data associated with the one or more parameters of the vehicle. Further, the AI based location prediction model, based on the prerequisite data, fetches a route map associated with the trip of the vehicle and a time taken for traveling a particular distance on the trip till a point ofloss of the one or more signals. The AI based location prediction model further, based on the real time data determines an average speed of the vehicle with respect to the time. Lastly, The AI based location prediction model further predicts a location of the vehicle based on the average speed and the time.

[0014] In yet another embodiment of the present disclosure, wherein to predict the location of the vehicle based on the average speed and the time, the AI based location prediction model calculates the location of the vehicle based on the average speed and the time and marks the location of the vehicle in a fleet board portal map. Further, the AI based location prediction model transmits the fleet board portal map to a computing device of a fleet owner.

[0015] In yet another embodiment of the present disclosure, wherein the set of data associated with a trip of the vehicle, comprises at least one or more of: an end location of a trip, aterrain information, a traffic data and time taken for the trip and wherein the set of real time associated with the one or more parameters of the vehicle, comprises at least one or more of: an engine torque, an engine speed, a trailer pressure, a barometric pressure, an average speed of the vehicle and a vehicle weight in a particular trip.

[0016] In yet another embodiment of the present disclosure, the AI based location prediction model tracks an ignition cycle of the vehicle to update the one or more information associated with the trip of the vehicle and the real time associated with the one or more parameters of the vehicle.

[0017] In yet another embodiment of the present disclosure, the AI based location prediction model further tracks a moving or an idle state of the vehicle based on the set of real time data associated with the one or more parameters of the vehicle. Further, the AI based location prediction model updates the moving or an idle state of the vehicle on the fleet board portal map.

[0018] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.BRIEF DESCRIPTION OF DRAWINGS

[0019] The features, nature, and advantages of the present disclosure will become more apparent from the detailed description set forth below when taken in conjunction withthe drawings in which like reference characters identify correspondingly throughout. Some embodiments of system and / or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying Figs., in which:

[0020] Figure 1 depicts an exemplary environment for predicting a location of a vehicle, in accordance with embodiments of the present disclosure.

[0021] Figure 2 depicts an exemplary block diagram illustrating a location prediction of a 5 vehicle, in accordance with embodiments of the present disclosure.

[0022] Figure 3 represents flowchart of an exemplary method for predicting a location of a vehicle, in accordance with embodiments of the present disclosure.

[0023] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in a computer readable medium and executed by a computer or processor, whether or not such computer or processor isexplicitly shown.DETAILED DESCRIPTION

[0024] The foregoing has broadly outlined the features and technical advantages of the present disclosure in order that the detailed description of the disclosure that follows may be better understood. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure.

[0025] The novel features which are believed to be characteristic of the disclosure, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each ofthe figures is provided for the purpose of illustration and description only and is notintended as a definition of the limits of the present disclosure.

[0026] As discussed earlier, if a positioning information becomes unavailable, a fleet owner or a management service will not be able to track the vehicle. This situation may arise dueto the position of the vehicle being in a low network area or the vehicle travelling in adeep tunnel or due to onboard component failure, etc. These scenarios can triggersecurity issues for the fleet management service and may loss the track of the fleet service and affects their business.

[0027] In order to overcome the above-mentioned challenges, the present disclosure provides technique(s) for predicting the location of a vehicle, by considering real time parametersof the vehicle and the trip data to predict the location accurately and precisely. Furthermore, with the technique(s) of the present disclosure, enables accurate location prediction of the vehicle. A detailed explanation of the proposed technique(s) is disclosed in the forthcoming paragraphs.

[0028] Figure 1 depicts an exemplary environment 100 for predicting a location of a vehicle 107, in accordance with embodiments of the present disclosure. The exemplary environment 100 illustrates the vehicle 107, which may operate in a fleet service. In a normal scenario a real time location of the vehicle 107 is fetched from a Global Positioning System (GPS) 105 by utilizing one or more vehicle GPS tracking devices109 and sent to a cloud 106 via a cellular network such Global System for Mobile Communications (GSM) using telematics unit 108. In some scenario where the vehicle 107 may travel in a bad or low network area and because of that the GPS tracking device 109 of the vehicle 107 may receive a low or no signal from the GPS 105. In such scenario, the vehicle 107 may not be able to transmit the positioning or locationinformation of the vehicle with a fleet service provider.

[0029] In a non-limiting example, the vehicle 107 may be an autonomous vehicle or a human driven vehicle. For example, vehicle 107 may be a truck, a car, a watercraft, an aerial vehicle. In a non-limiting example, the vehicle 107 without limitation may includetrucks, buses, coaches, trailers, taxis, delivery trucks or the similar vehicles etc. The vehicle 107 may comprise the telematics unit 108 and the GPS signal tracking device 109 and one or more sensors 110. In a non-limiting example, the telematics unit 108 may be any known vehicle telematics unit to communicate the one or more real timevehicle parameters and location of the vehicle 107 with the fleet owner. In case of thelow or no GPS signal the fleet service provider, or the fleet owner may receive the predicted location of the vehicle 107 via a fleet board portal 111. The fleet board portal 111 may be accessed via a fleet owner's computing device 112.

[0030] According to an exemplary aspect of the present disclosure, the logistics fleet service provider may need to track the location of the vehicle 107 timely to update and plan the fleet management. A system 101 which is recited on the cloud 106 may detect a loss of one or more signals required to track a real time location of the vehicle 107. In a non-limiting example, the signal may be a real time position sensing signal such as a globalpositioning system (GPS) signal, as described in the earlier embodiments. The system 101 may comprise an AI based location prediction model 102, a processor 103, a memory 104. The AI based location prediction model 102 may predict the location of the vehicle 107 based on the one or more prerequisite data of the trip and the one or more real time data of the vehicle received from the one or more sensors 110. A detailedexplanation of the system 101 and the AI based location prediction model 102 is provided in the forthcoming paragraphs in conjunction with Figures 2-3.

[0031] Figure 2 depicts an exemplary block diagram illustrating a system 200 for predicting the location of a vehicle 107 (of Figure 1), in accordance with embodiments of thepresent disclosure. In one non-limiting example, the vehicle 107 may be a fleet vehicle that can be operated in a fleet service to transport people or goods. The system 200 may recite on a cloud (not shown in fig.).

[0032] In some implementations, the system 200 may receive a prerequisite data including a real time data of the vehicle 107 from one or more sensors 201, that are recited on the vehicle. The system 200 may comprise a trip data database 202, a processor 203, a map information 204, memory 205, an AI based location prediction model 206. The system 200 may be communicatively coupled to a fleet owner's computing device 209 through the cloud (not shown in fig.). The AI based location prediction model 206 may furthercomprise a prerequisite data receiving unit 207, and a location prediction unit 208. The system 200 may predict the location of the vehicle 107 when one or more signals required to track a real time location of a vehicle are lost. The processor 203 may be operatively coupled to the one or more sensors 201, the trip data database 202, the memory 205 and the AI based location prediction model 206 to perform functions ofthe present disclosure.

[0033] In the illustrated figure, the AI based location prediction model 206 is shown to reside outside the processor 203 and may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. However, one of ordinary skill will appreciate that in other implementations, the AI based location prediction model 206 may also form a part of the processor 203 and may be implemented through software or hardware or a suitablecombination of software and hardware as per the implementation requirements of the present disclosure. In said implementation, the processor 203 may perform all the functions carried out by the different units 207, 208 of the AI based location prediction model 206. In one non-limiting example, the AI based location prediction model 206may be a generic AI model that is pre-trained and suitable to predict the location of thevehicle 107 in case of loss of the one or more signal that used to provide the real-time location of the vehicle.

[0034] In one implementation, the processor 203 may be implemented as one or moremicroprocessors, microcomputers, microcontrollers, digital signal processors, centralprocessing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 203 may be configured to fetch and execute computer-readable instructions stored in the memory 205.

[0035] In a non-limiting embodiment, the memory 205 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and / or non-volatile memory, such as read only memory (ROM),erasable programmable ROM, flash memories, hard disks, optical disks, and magnetictapes. Data / information may be stored within the memory 205 in the form of various data structures. The memory 205 may also store other data such as temporary data and temporary files, generated by the processor 203 or the AI based location prediction model 206 for performing the various functions of the present disclosure.

[0036] In some implementations, the system 200 may communicatively be coupled to the one or more sensors 201, that are recited on the vehicle 107. In a non-limiting example, the sensors may be any known sensors used to sense a weight, an altitude, a speed, a torque, a position, a pressure, a yaw rate of the vehicle. The one or more sensors 201 may sensea set of real time data associated with the one or more parameters of the vehicle, may comprise at least one or more of an engine speed, an engine torque, a trailer pressure, a barometric pressure, an average speed, a vehicle weight including trailer weight, and a tyre pressure etc. In a non-limiting example, the trailer pressure may comprise suspension pressure, brake pressure etc. In a non-limiting example, the one or moresensors 201 may periodically collect the one or more real time parameters of the vehicle 107 and may further transmit them to the AI based location prediction model 206. In an exemplary embodiment, the AI based location prediction model 206 may be able to track and update the moving or idle state of the vehicle 107 on a map of the fleet board portal.

[0037] In some implementations, the system 200 may further be communicatively coupled with the trip data database 202. In a non-limiting example, the trip data database 202 may be implemented locally on the system or may be implemented remotely on a cloud server. The trip data database 202 may comprise a set of data associated with the tripof the vehicle, may comprise at least one or more of an end location of a trip, a terrain information, a traffic data and a time taken for the trip etc. In a non-limiting example, the trip data database 202 may periodically collect the one or more trip data parameters of the vehicle 107.

[0038] According to one exemplary embodiment, the system 200 may be communicatively coupled with the map information 204. The map information 204 may be a local database recited in the vehicle or may be implemented remotely on a cloud server to receive the real time positioning information of the vehicle from a positioning system such as global positioning system (GPS). The map information 204 may provide thenavigation signal to the vehicle and also will locate the vehicle in a fleet board portal for tracking the vehicle. In a non-limiting example, the system 200 may receive the one or more information such as an estimated time or arrival, weather conditions, road conditions, terrain information etc.

[0039] According to one exemplary embodiment, the system 200 may be communicatively coupled with the memory 205. In a non-limiting embodiment, the memory 205 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and / or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, data / information may be stored within the memory 205 in the form of various data structures. The memory 205 may also store other data such as temporary data and temporary files, generated by theprocessor 203 for performing the various functions of the present disclosure.

[0040] In operation, the AI based location prediction model 206 may receive the prerequisite data via the prerequisite data receiving unit 207. The prerequisite data may comprise at least a set of data associated with a trip of the vehicle and a set of real time data associated with the one or more parameters of the vehicle. The set of data associated with the trip of the vehicle may be received from the trip data database 202 as described in the earlier embodiments. The set of real time data associated with the one or more parameters of the vehicle may be received via the one or more sensors 201 as described in the earlier embodiments. The location prediction unit 208 of the AI based location prediction model 206 may fetch a route map associated with the trip of the vehicle and a time taken for traveling a particular distance on the trip till a point of loss of the one or more signals. The location prediction unit 208 may further determine an average speed of the vehicle with respect to the time and predict a location of the vehicle based on the average speed and the time.

[0041] In an exemplary embodiment, the location prediction unit 208 may calculate the speed of the vehicle using an engine speed (RPM), a wheel radius (m), a gear ratio, based on the following equation: Vehicle speed (m / s) = Engine speed (RPM) * Wheel radius (m) * Gear ratio. In another exemplary embodiment the location prediction unit 208 maycalculate vehicle speed using an engine torque (Nm), a wheel radius (m), a gear ratio, a final drive ratio, based on the following equation: Vehicle speed (m / s) = Engine torque (Nm) * Gear ratio * Final drive ratio / Wheel radius (m). Further, the vehicle speed in meters per second (m / s) is converted into kilometers per hour (k / m) or miles per hour (mph) to determine the real time speed of the vehicle. The location prediction unit may calculate the average speed of the vehicle based on the speed of the vehicle determined from the engine speed and the engine torque.

[0042] According to one exemplary embodiment, the location prediction unit 208 may further calculate the location of the vehicle based on the average speed and the time. The location prediction unit 208 may use the pre-requisite data to fetch the route map (latitude, longitude and altitude) of the entire trip and time taken for the travel. Further,the location prediction unit 208 may use the prerequisite data such as an average speedof the vehicle calculated from the engine speed and the engine torque, a real time speed of the vehicle in the current trip based on the GSM or wireless connectivity signal, a current trip average speed of vehicle till the connection is lost, an average speed of the vehicle in various altitudes, a barometric pressure and trailer pressure of the vehicle tocalculate the average speed. The location prediction unit 208 upon calculating theaverage speed may determine the latitude, longitude of vehicle with respect to time. The location prediction unit 208 may further mark the predicted location of the vehicle in a fleet board portal map and transmit the fleet board portal map to a computing device of a fleet owner. In a non-limiting example, the location prediction unit 208 may furthertrack an ignition cycle of the vehicle to update the one or more information associatedwith the trip of the vehicle and the real time data associated with the one or more parameters of the vehicle. In an exemplary embodiment, the system 200 may track a moving or an idle state of the vehicle based on the engine speed, torque and update the vehicle status on the fleet board portal.

[0043] According to one exemplary embodiment, the system 200 may be communicatively coupled with the fleet owner's computing device 209. In a non-limiting example, a computing device 209 may be a mobile or portable computing device, a desktop computer, a server, and / or the like".

[0044] According to one exemplary embodiment, the system 200 may consider all the essential parameters to predict the accurate and precise location of the vehicle based on the set of prerequisite data associated with the vehicle and the trip.

[0045] Figure 3 represents flowchart of an exemplary method 300 for predicting a location of a vehicle 107, in accordance with embodiments of the present disclosure. The order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 300 may be considered to be implemented by the AI Model and / or by processor of the system 200 of Fig. 2.

[0046] At step 301, the method 300 may include receiving a prerequisite data upon detecting a loss of a real time location of a vehicle. In one example but not limited thereto, the prerequisite data may comprise at least a set of data associated with a trip of the vehicle and a set of real time data associated with the one or more parameters of the vehicle. Inone implementation, the AI based location prediction model 206 and / or the processor 203 may receive the prerequisite data upon on detecting a loss of one or more signals required to track a real time location of a vehicle.

[0047] At step 302, based on the prerequisite data the method 300 may include fetching a route map associated with the trip of the vehicle and a time taken for traveling a particular distance on the trip till a point of loss of the one or more signals. In one implementation, the AI based location prediction model 206 may fetch the route map associated with the trip of the vehicle and the time taken for traveling the particular distance.

[0048] At step 303, based on the set of one or more real time data the method 300 may include determining an average speed of the vehicle with respect to the time. In one implementation, the AI based location prediction model 206 may determine the average speed of the vehicle.

[0049] At step 304, the method 300 may include predicting a location of the vehicle based on the average speed and time. In one implementation, the AI based location prediction model 206 may predict the location of the vehicle.

[0050] The order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described.

[0051] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of thedescription. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed.

[0052] Alternatives will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of thedisclosed embodiments.

[0053] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which informationor data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term "computer- readable medium" should be understood to include tangible items and exclude carrier waves and transient signals,i.e., are non-transitory. Examples include random access memory, read-only memory, volatile memory, non-volatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

[0054] Suitable processors include, by way of example, a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a graphic processing unit, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Arrays circuits, any other type of integrated circuit, and / or a state machine.

[0055] Advantages of the embodiment of the present disclosure are illustrated herein-As previously indicated, the present disclosure facilitates an accurate and precise location prediction of the vehicle, in case of low / no GPS signal. Further, the present disclosure may also predict estimated time of arrival (ETA) for the vehicle to reach end location with expected delay time, in case of low / no GPS signal. Furthermore, the fleet management will be able to track the vehicle even in low / no GPS signal area and plan the fleet service.

[0056] REFERENCE NUMERALSDescription Reference number100 Exemplary environment101, 200 system102, 206 AI Based Location Prediction Model103, 203 Processor104, 205 Memory105 GPS106 Cloud107 Vehicle108 Telematics Unit109 GPS signal Tracking Device110, 201 One or more Sensors111 Fleet Board Portal112 Fleet Owner's Computing Device202 Trip Data Database204 Map Information207 Prerequisite Data Receiving Unit208 Location Prediction Unit209 Fleet Owner's Computing Device301-304 Method steps

Claims

1. An AI based method of predicting a location of a vehicle, comprising: receiving a prerequisite data upon detecting a loss of one or more signals required to track a real time location of a vehicle, wherein the prerequisite data comprises at least a set of data associated with a trip of the vehicle and a set of one or more real time data associated with the one or more parameters of the vehicle; based on the prerequisite data, fetching a route map associated with the trip of the vehicle and a time taken for traveling a particular distance on the trip till a point of loss of the one or more signals, based on the set of one or more real time data, determining an average speed of the vehicle with respect to the time; and predicting a location of the vehicle based on the average speed and the time.

2. The method as claimed in claim 1, wherein predicting the location of the vehicle based on the average speed and the time, comprising: calculating the location of the vehicle based on the average speed and the time; marking the location of the vehicle in a fleet board portal map; and transmitting the fleet board portal map to a computing device of a fleet owner.

3. The method as claimed in claim 1, wherein the set of data associated with the trip of the vehicle, comprises at least one or more of: an end location of a trip, a terrain information, a traffic data and a time taken for the trip; and wherein the set of real time data associated with the one or more parameters of the vehicle, comprises at least one or more of: an engine torque, an engine speed, a trailer pressure, a barometric pressure, an average speed and a vehicle weight in a particular trip.

4. The method as claimed in claim 1, further comprising: tracking an ignition cycle of the vehicle to update the one or more information associated with the trip of the vehicle and the set of real time data associated with the one or more parameters of the vehicle.

5. The method as claimed in claim 1, further comprising: tracking a moving or an idle state of the vehicle based on the set of real time data associated with the one or more parameters of the vehicle; and updating the moving or the idle state of the vehicle on the fleet board portal map.

6. An AI based system for predicting a location of a vehicle, comprises: a memory; a processor electronically coupled to the memory, the processor configured to: receive a prerequisite data upon detecting a loss of one or more signals required to track a real time location of a vehicle, wherein the prerequisite data comprises at least a set of data associated with a trip of the vehicle and a set of one or more real time data associated with the one or more parameters of the vehicle; an AI based location prediction model coupled to the processor, wherein based on the prerequisite data, the AI based location prediction model configured to: fetch a route map associated with the trip of the vehicle and a time taken for traveling a particular distance on the trip till a point of loss of the one or more signals, wherein based on the real time data, the AI based location prediction model configured to: determine an average speed of the vehicle with respect to the time; and predict a location of the vehicle based on the average speed and the time.

7. The system as claimed in claim 6, wherein to predict the location of the vehicle based on the average speed and the time, the AI based location prediction model configured to: calculate the location of the vehicle based on the average speed and the time; mark the location of the vehicle in a fleet board portal map; and transmit the fleet board portal map to a computing device of a fleet owner.

8. The system as claimed in claim 6, wherein the set of data associated with a trip of the vehicle, comprises at least one or more of: an end location of a trip, a terrain information, a traffic data and time taken for the trip; and wherein the set of real time associated with the one or more parameters of the vehicle, comprises at least one or more of: an engine torque, an engine speed, a trailer pressure, a barometric pressure, an average speed of the vehicle and a vehicle weight in a particular trip.

9. The system as claimed in claim 6, wherein the AI based location prediction model further configured to: track an ignition cycle of the vehicle to update the one or more information associated with the trip of the vehicle and the set of real time data associated with the one or more parameters of the vehicle.

10. The system as claimed in claim 6, wherein the AI based location prediction model further configured to: track a moving or an idle state of the vehicle based on the set of real time data associated with the one or more parameters of the vehicle; and update the moving or an idle state of the vehicle on the fleet board portal map.