Description:Field of the Invention
This invention relates to Edge Computing-Enabled Vehicle Density Monitoring and Route Guidance System for Fuel Stations
Background of the Invention
The system presented in this patent addresses congestion at fuel stations by continuously monitoring real-time vehicle density. It identifies overcrowded stations and offers alternative route guidance to less crowded ones, helping drivers avoid long queues and reduce waiting times. By ensuring a more even distribution of vehicles across multiple stations, the system prevents overwhelming any specific location and minimizes bottlenecks. It employs a data-driven approach to optimizes route planning based on vehicle density data and suggests routes that minimize travel time, congestion, and provide efficient fueling opportunities, enhancing journey planning and fueling convenience.
CN105118303B The present invention proposes a kind of vehicle density method of estimation based on depth convolutional neural networks, road video image is collected including the use of video camera, pass through image preprocessing, multi-Scale Pyramid image block is sent into convolutional neural networks, extraction, simply to the feature of higher level of abstraction, obtains the distribution density figure of various scale wagon flow images by bottom mapping of the multiple dimensioned distribution density figure to general image distribution density figure and the total vehicle number of image with fully connected network layers again to the other distribution density figure interested area division of the video image of convolutional neural networks output, area-of-interest pixel is summed to obtain bicycle road or multilane vehicle number. The instantaneous vehicle density in region is calculated by zone length. The present invention substantially increases the accuracy and real-time of vehicle count and vehicle density estimation.
Research Gap: The model presented in this patent uses Edge computing and is able to send the information to a remote server for further processing and storage.
The method presented in this patent uses computer vision algorithms to calculate vehicle density in real-time.
The model presented in this patent is specifically designed for fuel station and route guiding system.
US11183051B2 Methods and software utilizing artificial neural networks (ANNs) to estimate density and/or flow (speed) of objects in one or more scenes each captured in one or more images. In some embodiments, the ANNs and their training configured to provide reliable estimates despite one or more challenges that include but are not limited to, low-resolution images, low framerate image acquisition, high rates of object occlusions, large camera perspective, widely varying lighting conditions, and widely varying weather conditions. In some embodiments, fully convolutional networks (FCNs) are used in the ANNs. In some embodiments, a long short-term memory network (LSTM) is used with an FCN.
Research Gap: The model presented in this patent uses Edge computing and is able to send the information to a remote server for further processing and storage.
The model presented in this patent provides real-time result.
The model presented in this patent is specifically designed for fuel station and route guiding system.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. Present invention is Edge Computing-Enabled Vehicle Density Monitoring and Route Guidance System for Fuel Stations
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The Edge Computing-Enabled Vehicle Density Monitoring and Route Guidance System for Fuel Stations is a comprehensive solution designed to address congestion and optimize the fuelling experience at petrol stations. Edge computing involves processing data closer to the source, which in this case is the fuel station itself. By performing data processing locally, the system reduces latency and enables real-time monitoring and decision-making. By leveraging edge computing technology, the system processes and analyses vehicle density data in real-time, enabling efficient monitoring and decision-making. At the core of the system are the vehicle density monitoring components which cameras strategically placed at different locations within the fuel station. They capture real-time data on the number of vehicles present, their locations, and their movement patterns to collect useful information from it. This data is then processed locally on the device placed on the fuel station using edge computing capabilities and computer vision algorithms. By analysing this information, the system can determine the level of congestion at each pump or fuelling station and it is sent to a central cloud server.
On the cloud server congestion levels of multiple fuel stations are collected on real-time. After that, the server will apply this information for route selection process by applying certain optimal route-finding algorithms such as Dijkstra's Algorithm or A* Algorithm. The processed vehicle density data is then used to provide route guidance and recommendations to drivers and based on the current vehicle density information, the system suggests alternative routes to less crowded fuel stations, allowing drivers to bypass congested locations and reduce waiting times. This guidance is delivered to drivers through a mobile application and the user can interact with the application to get the optimal or alterative suggestions to ensure efficient and reliable operations. This approach allows for faster response times and more accurate recommendations, enhancing the overall effectiveness of the system. By providing drivers with alternative routes to less crowded fuel stations, the system reduces congestion and waiting times, resulting in an improved fuelling experience for drivers and optimized resource utilization for fuel station operators.
The Figure 1 represents flowchart for the given system which starts by installing an edge-based system on the fuel stations at a strategic position to obtain a good view. The camera captures the video footage and applies pre-trained computer vision model to calculated the count of vehicles present on a certain fuel station in real-time. After the count is determined that count is used to determine vehicle density and congestion level of that fuel station. The edge-based system sends the data to a cloud server and the server uses that received data for calculation of optimal paths which provides a balanced distribution of vehicles and saves time for the user by suggesting the user to choose another fuel station.
The Fig.2 is a block diagram to presents the working of the edge-based node and its connected components. The edge device i.e., camera components (41) are responsible for capturing real time video of passengers and providing with necessary details to the processing unit (40). The processing unit apply computer vision model (43) to the acquired video footage to compute the numbers of vehicle at a given point and provide with real-time result by processing it at the edge. The estimated parameters are then sent to a cloud server to be used in a real-time system for route guidance mechanism internet or Wi-Fi (42). All the components of the device are given the power supply to operate (44).
The Fig.3 is a block diagram to presents the overall working process of the model. The edge-based node (10) first computes the number of vehicles in a fuel station at any given instance of time by processing the video footage captured from the camera by applying certain computer vision algorithms. The edge device sends the computed result to the Edge gateway (20) through Wi-Fi communication and from there it is send to the cloud server through internet. The cloud server (30) then stores the data into the database (50) and also interact with multiple users to guide them for most optimal fuel station location thorough a user interface or mobile application (40). This will help users in making optimal decision for saving time and regulate traffic flow.

BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1 System Architecture
Figure 2 System Architecture
Figure 3 System Architecture
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.

DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
These and other advantages of the present subject matter would be described in greater detail with reference to the following figures. It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter and are included within its scope.
The Edge Computing-Enabled Vehicle Density Monitoring and Route Guidance System for Fuel Stations is a comprehensive solution designed to address congestion and optimize the fuelling experience at petrol stations. Edge computing involves processing data closer to the source, which in this case is the fuel station itself. By performing data processing locally, the system reduces latency and enables real-time monitoring and decision-making. By leveraging edge computing technology, the system processes and analyses vehicle density data in real-time, enabling efficient monitoring and decision-making. At the core of the system are the vehicle density monitoring components which cameras strategically placed at different locations within the fuel station. They capture real-time data on the number of vehicles present, their locations, and their movement patterns to collect useful information from it. This data is then processed locally on the device placed on the fuel station using edge computing capabilities and computer vision algorithms. By analyzing this information, the system can determine the level of congestion at each pump or fueling station and it is sent to a central cloud server.
On the cloud server congestion levels of multiple fuel stations are collected on real-time. After that, the server will apply this information for route selection process by applying certain optimal route-finding algorithms such as Dijkstra's Algorithm or A* Algorithm. The processed vehicle density data is then used to provide route guidance and recommendations to drivers and based on the current vehicle density information, the system suggests alternative routes to less crowded fuel stations, allowing drivers to bypass congested locations and reduce waiting times. This guidance is delivered to drivers through a mobile application and the user can interact with the application to get the optimal or alterative suggestions to ensure efficient and reliable operations. This approach allows for faster response times and more accurate recommendations, enhancing the overall effectiveness of the system. By providing drivers with alternative routes to less crowded fuel stations, the system reduces congestion and waiting times, resulting in an improved fueling experience for drivers and optimized resource utilization for fuel station operators.
The Figure 1 represents flowchart for the given system which starts by installing an edge-based system on the fuel stations at a strategic position to obtain a good view. The camera captures the video footage and applies pre-trained computer vision model to calculated the count of vehicles present on a certain fuel station in real-time. After the count is determined that count is used to determine vehicle density and congestion level of that fuel station. The edge-based system sends the data to a cloud server and the server uses that received data for calculation of optimal paths which provides a balanced distribution of vehicles and saves time for the user by suggesting the user to choose another fuel station.
The Fig.2 is a block diagram to presents the working of the edge-based node and its connected components. The edge device i.e., camera components (41) are responsible for capturing real time video of passengers and providing with necessary details to the processing unit (40). The processing unit apply computer vision model (43) to the acquired video footage to compute the numbers of vehicle at a given point and provide with real-time result by processing it at the edge. The estimated parameters are then sent to a cloud server to be used in a real-time system for route guidance mechanism internet or Wi-Fi (42). All the components of the device are given the power supply to operate (44).
The Fig.3 is a block diagram to presents the overall working process of the model. The edge-based node (10) first computes the number of vehicles in a fuel station at any given instance of time by processing the video footage captured from the camera by applying certain computer vision algorithms. The edge device sends the computed result to the Edge gateway (20) through Wi-Fi communication and from there it is sends to the cloud server through internet. The cloud server (30) then stores the data into the database (50) and also interact with multiple users to guide them for most optimal fuel station location thorough a user interface or mobile application (40). This will help users in making optimal decision for saving time and regulate traffic flow.
Disclosed herein An Edge Computing-Enabled Vehicle Density Monitoring and Route Guidance System for Fuel Stations comprises Computing Unit (40), Camera (41), Internet + Wi-Fi (42), Computer Vision Model (43), Power Supply (44), Edge based Node ‘1’ (10) Edge Gateway (20), Cloud Server (30), Mobile Dashboard (40).
In another embodiment, the camera captures the video footage and applies pre-trained computer vision model to calculated the count of vehicles present on a certain fuel station in real-time.
In another embodiment, the count is determined that count is used to determine vehicle density and congestion level of that fuel station; and the edge-based system sends the data to a cloud server and the server uses that received data for calculation of optimal paths which provides a balanced distribution of vehicles and saves time for the user by suggesting the user to choose another fuel station. In another embodiment, the edge device camera components (41) are responsible for capturing real time video of passengers and providing with necessary details to the processing unit (40). In another embodiment, the processing unit apply computer vision model (43) to the acquired video footage to compute the numbers of vehicle at a given point and provide with real-time result by processing it at the edge. In another embodiment, the estimated parameters are then sent to a cloud server to be used in a real-time system for route guidance mechanism internet or Wi-Fi (42). All the components of the device are given the power supply to operate (44).
In another embodiment, the edge-based node (10) first computes the number of vehicles in a fuel station at any given instance of time by processing the video footage captured from the camera by applying certain computer vision algorithms.
In another embodiment, the edge device sends the computed result to the Edge gateway (20) through Wi-Fi communication and from there it is sends to the cloud server through internet.
In another embodiment, the cloud server (30) then stores the data into the database (50) and also interact with multiple users to guide them for most optimal fuel station location thorough a user interface or mobile application (40).
In another embodiment, system helps users in making optimal decision for saving time and regulate traffic flow; and the system possibly is expanded to for monitoring vehicle density at other places such as parking lots; and the system possibly is integrated to the vehicles with connected system to provide them real-time updates. (built-in feature); and this system is possibly used for predictive analytics to anticipate future vehicle density trends or traffic conditions.
ADVANTAGES OF THE INVENTION:
1. This model identifies stations with high demand and direct drivers to less crowded stations, ensuring a more balanced distribution of vehicles and optimizing resource utilization.
2. The model minimizes waiting times at fuel pumps by guiding drivers to fuel stations with lower vehicle density.
3. This model helps reduce traffic congestion in the vicinity of popular fueling locations which improves overall traffic flow and reduces bottlenecks.
4. This model uses edge computing technology to analyze data locally near the fuel stations. This results in faster response times and improved reliability.
, Claims:We Claim:
1. An Edge Computing-Enabled Vehicle Density Monitoring and Route Guidance System for Fuel Stations comprises Computing Unit (40), Camera (41), Internet + Wi-Fi (42), Computer Vision Model (43), Power Supply (44), Edge based Node ‘1’ (10) Edge Gateway (20), Cloud Server (30), Mobile Dashboard (40).
2. The system as claimed in claim 1, wherein the camera captures the video footage and applies pre-trained computer vision model to calculated the count of vehicles present on a certain fuel station in real-time.
3. The system as claimed in claim 1, wherein after the count is determined that count is used to determine vehicle density and congestion level of that fuel station; and the edge-based system sends the data to a cloud server and the server uses that received data for calculation of optimal paths which provides a balanced distribution of vehicles and saves time for the user by suggesting the user to choose another fuel station.
4. The system as claimed in claim 1, wherein the edge device camera components (41) are responsible for capturing real time video of passengers and providing with necessary details to the processing unit (40).
5. The system as claimed in claim 1, wherein the processing unit apply computer vision model (43) to the acquired video footage to compute the numbers of vehicle at a given point and provide with real-time result by processing it at the edge.
6. The system as claimed in claim 1, wherein the estimated parameters are then sent to a cloud server to be used in a real-time system for route guidance mechanism internet or Wi-Fi (42). All the components of the device are given the power supply to operate (44).
7. The system as claimed in claim 1, wherein te edge-based node (10) first computes the number of vehicles in a fuel station at any given instance of time by processing the video footage captured from the camera by applying certain computer vision algorithms.
8. The system as claimed in claim 1, wherein the edge device sends the computed result to the Edge gateway (20) through Wi-Fi communication and from there it is sends to the cloud server through internet.
9. The system as claimed in claim 1, wherein the cloud server (30) then stores the data into the database (50) and also interact with multiple users to guide them for most optimal fuel station location thorough a user interface or mobile application (40).

10. The system as claimed in claim 1, wherein system helps users in making optimal decision for saving time and regulate traffic flow; and the system possibly is expanded to for monitoring vehicle density at other places such as parking lots; and the system possibly is integrated to the vehicles with connected system to provide them real-time updates. (built-in feature); and this system is possibly used for predictive analytics to anticipate future vehicle density trends or traffic conditions.