Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of navigation systems and more particularly, to computational methods and systems for real-time route optimization. The present disclosure relates to a system and method for generating optimized routes for navigation.

BACKGROUND
[0002] The following description of the related art is intended to provide background information pertaining to the field of the present disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0003] In metropolitan areas, emergency vehicles such as ambulances, police cars, and fire trucks often experience significant delays due to heavy traffic congestion. These delays can be critical, particularly for ambulances and fire responders, where every second may determine life or death or the extent of damage to property. Traditional navigation systems rely primarily on static road maps and occasionally on traffic data, but they are insufficient for dynamically adapting to fast-changing traffic conditions in real time, especially in high-density urban settings.
[0004] Patent Application IN202341084579 relates to a Smart Traffic Management System for Ambulances (STMSA) designed to optimize ambulance routing and response in densely populated urban areas. The system utilizes real-time data from ambulances, traffic sensors, and road cameras to dynamically generate optimal routes, ensuring prompt and efficient emergency medical services. A central control unit processes incoming data and factors in patient conditions, traffic congestion, and road closures to provide route recommendations to ambulance drivers. The system also interfaces with a dynamic road signage system, providing real-time instructions to other road users to facilitate the passage of ambulances. Machine learning algorithms continuously refine route predictions based on historical data, enhancing accuracy and efficiency.
[0005] Patent document CN106781462B discloses an automobile congestion dispersal and separation system based on car networking technology, designed to alleviate traffic congestion, particularly in tourist attractions. The system includes a roadside unit, a vehicle intelligent terminal, and a scenic spot cloud server. The roadside unit integrates a digital signal processor, weigh inductor, signal acquisition amplification module, digital high-definition camera, and wireless Wi-Fi transceiver A. The vehicle intelligent terminal includes a wireless Wi-Fi transceiver B, embedded processor, Global Positioning System (GPS) module, and Global System for Mobile Communications (GSM) module. By leveraging GPS for location tracking and GSM for communication, the system enables real-time traffic condition data acquisition, transmission, and dissemination. The system dynamically disperses and redirects vehicle flow to reduce congestion pressure in scenic areas.
[0006] Patent document TWI811988B discloses a system and method for planning emergency traffic routes through base station algorithms and notifying vehicles to give way in advance. The base station continuously receives mobile network signals from mobile devices within the signal range and generates multiple measurement data accordingly. The cloud server receives disaster relief after determining the current location of the car and the target location. The cloud server receives and analyzes the measurement data of the base station based on the base station between the current location and the target location to locate the location of the mobile device. The cloud server compares the location with a map, planning a fastest path for the disaster relief vehicle to reach the target location, and the cloud server sends at least one radio frequency notification to the mobile device on the fastest path through the base station to notify the mobile device to give way.
[0007] Patent document CN117315962A discloses a priority passing method of emergency vehicles, an intelligent traffic system and a cloud platform, where the priority passing method of the emergency vehicles includes acquiring destination information and real-time GPS information from a vehicle-mounted terminal of an emergency vehicle, and updating an optimal driving route in real time. The method includes acquiring real-time road condition information of each road section to be reached and real-time lamp state information of a signal lamp on the optimal driving route. The method includes analyzing the real-time position information, the optimal driving route, the real-time road condition information and the real-time lamp state information, and acquiring guidance strategy information of preferential traffic. The method includes sending the signal control information to the signal control platform, so that the signal control platform adjusts the state of signal lamps of all the intersections to be reached on the optimal driving route according to the signal control information. The method includes sending the recommended speed information to the vehicle-mounted terminal of the emergency vehicle through the corresponding road side equipment so as to enable the vehicle-mounted terminal to output the recommended speed information.
[0008] However, the conventional systems and methods are infrastructure-intensive, cost-prohibitive, or ineffective in unstructured traffic scenarios. There is, therefore, a need in the art to provide a system and method thereof that can overcome the shortcomings of the existing prior arts.

OBJECTS OF THE PRESENT DISCLOSURE
[0009] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as listed herein below.
[0010] It is another object of the present disclosure to provide a system and method for generating optimized routes for navigation that receives data associated with the emergency vehicle, where the data includes a navigation route associated with the emergency vehicle.
[0011] It is another object of the present disclosure to provide a system that receives information associated with vehicles along the navigation route of the emergency vehicle.
[0012] It is another object of the present disclosure to provide a system that simultaneously generates an optimized route for the navigation of the emergency vehicle along the navigation route based on the received data and the received information.
[0013] It is another object of the present disclosure to provide a system that generates routing instructions to the vehicles to facilitate clearance of the navigation route based on the optimized route.

SUMMARY
[0014] In an aspect, the present disclosure relates to a system for generating optimized routes. The system includes a processor communicatively coupled to an Electronic Control Unit (ECU) of an emergency vehicle. A memory operatively coupled with the processor, wherein said memory stores instructions which, when executed by the processor, cause the processor to receive data associated with the emergency vehicle, wherein the data comprises a navigation route associated with the emergency vehicle. The processor receives information associated with one or more vehicles along the navigation route of the emergency vehicle. The processor simultaneously generates an optimized route for the navigation of the emergency vehicle along the navigation route based on the received data and the received information. The processor generates one or more routing instructions to the one or more vehicles to facilitate clearance of the navigation route based on the optimized route.
[0015] In an embodiment, the processor may be configured to predict via a machine learning engine, one or more navigation routes associated with the emergency vehicle based on the received data and the optimized route.
[0016] In an embodiment, prior to receiving the data, the processor may be configured to authenticate the emergency vehicle via an interface.
[0017] In an embodiment, the information may include at least one of a speed parameter associated with the one or more vehicles, a location associated with the one or more vehicles.
[0018] In an embodiment, the data may include one or more parameters associated with the emergency vehicle and one or more location co-ordinates associated with the emergency vehicle.
[0019] In an embodiment, the processor may be configured to determine a bounding box to identify the one or more vehicles along the navigation route of the emergency vehicle. The processor, in response to a determination that one or more co-ordinates associated with the location of the one or more vehicles are located within the bounding box, may be configured to generate the one or more routing instructions to the one or more vehicles to facilitate clearance of the navigation route.
[0020] In an embodiment, the processor may be configured to determine variation in the received information and simultaneously generate one or more navigation paths associated with the emergency vehicle.
[0021] In an embodiment, the processor may be configured to dynamically switch between a Websocket communication protocol and a Hypertext Transfer Protocol Application Programming Interface (HTTP API) call to receive data associated with the emergency vehicle and information associated with one or more vehicles.
[0022] In an aspect, the present disclosure relates to a method for generating optimized routes. The method includes receiving, by a processor, associated with a system, data associated with the emergency vehicle, where the data includes a navigation route associated with the emergency vehicle. The method includes receiving, by the processor, information associated with one or more vehicles along the navigation route of the emergency vehicle. The method includes simultaneously generating, by the processor, an optimized route for the navigation of the emergency vehicle along the navigation route based on the received data and the received information. The method includes generating, by the processor, one or more routing instructions to the one or more vehicles to facilitate clearance of the navigation route based on the optimized route.
[0023] In an embodiment, the method may include predicting, by the processor, via a machine learning engine, one or more navigation routes associated with the emergency vehicle based on the received data and the optimized route.
[0024] In an embodiment, the method may include authenticating, by the processor, the emergency vehicle via an interface, prior to receiving the data,
[0025] In an embodiment, the information may include at least one of a speed parameter associated with the one or more vehicles, a location associated with the one or more vehicles.
[0026] In an embodiment, the data may include one or more parameters associated with the emergency vehicle and one or more location co-ordinates associated with the emergency vehicle.
[0027] In an embodiment, the method may include determining, by the processor, a bounding box for identifying the one or more vehicles along the navigation route of the emergency vehicle. The method may include, in response to a determination that one or more co-ordinates associated with the location of the one or more vehicles are located within the bounding box, generating, by the processor, the one or more routing instructions to the one or more vehicles for facilitating clearance of the navigation route.
[0028] In an aspect, the present disclosure relates to a system for generating optimized routes. The system includes a processor communicatively coupled to a server. A memory operatively coupled with the processor, where said memory stores instructions which, when executed by the processor, cause the processor to receive data from one or more emergency vehicles through the server, where the server dynamically switches between one or more communication protocols to enable communication between the emergency vehicle and one or more vehicles. The processor receives information associated with one or more vehicles along a navigation route of the emergency vehicle through the server. The processor simultaneously generates an optimized route for the navigation of the emergency vehicle along the navigation route based on the received data and the received information. The server is configured to notify the one or more vehicles about the emergency vehicle via a real-time alert mechanism. A frontend module configured to display real-time location of the emergency vehicle and the optimized navigation route on one or more user devices and receive notifications to reroute the emergency vehicle. The processor predicts via a machine learning engine, one or more navigation routes associated with the emergency vehicle based on the received data and the optimized route.

BRIEF DESCRIPTION OF THE DRAWINGS
[0029] The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that the invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.
[0030] FIG. 1 illustrates an exemplary network architecture of the proposed system for generating optimized routes for navigation, in accordance with an embodiment of the present disclosure.
[0031] FIG. 2 illustrates an exemplary block diagram of the proposed system, in accordance with an embodiment of the present disclosure.
[0032] FIG. 3 illustrates an exemplary high-level flow diagram of the proposed system for generating optimized routes for navigation, in accordance with an embodiment of the present disclosure.
[0033] FIG. 4 illustrates an exemplary block diagram of various interfaces used by the proposed system for generating optimized routes for navigation,
[0034] FIG. 5 illustrates a flow diagram of a method implemented by the proposed system for generating optimized routes for navigation, in accordance with an embodiment of the present disclosure.
[0035] FIG. 6 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION
[0036] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0037] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth.
[0038] The present disclosure relates to a system that enhances emergency vehicle response times by leveraging real-time communication through communication protocols, a backend server, and advanced route optimization algorithms. The system enables dynamic interaction between emergency vehicles, the central server, and nearby civilian vehicles, ensuring minimal delays during emergency situations through the backend server. The system uses communication protocols for real-time bi-directional communication. The system receives continuous location, speed, and status updates from emergency vehicles and processes data using traffic-aware route optimization algorithms. The system sends optimized navigation paths to the emergency vehicle in real time. The system sends alerts to nearby civilian vehicles using the communication protocols, prompting them to clear the path. The system handles additional Application Programming Interface (API) requests for services such as vehicle authentication, historical traffic data retrieval, and live traffic updates.
[0039] Various embodiments of the present disclosure will be explained in detail with respect to FIGs. 1-6.
[0040] FIG. 1 illustrates an exemplary network architecture of the proposed system 102 for generating optimized routes for navigation, in accordance with an embodiment of the present disclosure.
[0041] In an embodiment, referring to FIG. 1, the system 102 may be connected to a network 104, which may be further connected to at least one computing device 108-1, 108-2, … 108-N (collectively referred as computing device 108, or computing devices 108 herein) associated with one or more users 106-1, 106-2, … 106-N (collectively referred as user 106, herein). The users 106 may be positioned within a vehicle and may request the system 102 through the computing device 108 to generate an optimized route during the navigation of the vehicle.
[0042] In an exemplary embodiment, the computing device 108 may include, but not be limited to, a computer enabled device, a mobile phone, a smartphone, a tablet, a laptop, a display device, a surveillance camera, an automatic teller machine, and a point of sale, a kiosk, and a smart doorbell, a smart home device, a Augmented Reality/Virtual Reality/Mixed Reality (AR/VR/MR), an imaging device, a display projector, a Remote Detection Service (Detection Device) enabled devices such as iBeacon technologies, or some combination thereof. A person of ordinary skill in the art will understand that the at least one computing device 108 may be individually referred to as a computing device and collectively referred to as computing devices 108.
[0043] In an exemplary embodiment, the network 104 may include, but not be limited to, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. In an exemplary embodiment, the network 104 may include, but not be limited to, a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof.
[0044] In an embodiment, the system 102 may receive data associated with an emergency vehicle, where the data may include at least a current location, speed, and a predetermined navigation route of the emergency vehicle. The system 102 may receive real-time information associated with one or more vehicles present along or in proximity to the navigation route of the emergency vehicle, including their location, speed, and heading. The system 102 dynamically and simultaneously generates an optimized route for the navigation of the emergency vehicle based on the received emergency vehicle data, the received information from surrounding vehicles, real-time traffic conditions, road constraints, and congestion levels. The system 102 may generate and transmit one or more routing instructions to the one or more nearby vehicles along the navigation path to facilitate the clearance of the route, where such instructions may be based on the optimized route and intended to minimize response time. The system 102 improves the response time of emergency vehicles by using communication protocols for collecting real-time communication. Backend servers configured with the system 102 may send and receive the live location of emergency vehicles using advanced algorithms. By using advanced algorithms the system 102 may provide optimised routes and enable Vehicle-to-vehicle (V2V) communication which helps nearby vehicles make way for emergency vehicles.
[0045] FIG. 2 illustrates an exemplary block diagram of the proposed system, in accordance with an embodiment of the present disclosure.
[0046] In an aspect, referring to FIG. 2, the system 102 may include one or more processor(s) 202. The one or more processor(s) 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) 202 may be configured to fetch and execute computer-readable instructions stored in a memory 204 of the system 102. The memory 204 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 204 may include any non-transitory storage device including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.
[0047] The system 102 may include an interface(s) 206. The interface(s) 206 may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication to/from the system 102. The interface(s) 206 may also provide a communication pathway for one or more components of the system 102. Examples of such components include, but are not limited to, processing unit/engine(s) 208, a local database 210, a data ingestion engine 212, and a machine learning engine 214.
[0048] In an embodiment, the processing unit/engine(s) 208 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 208. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 208 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 208 may include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 208. In such examples, the system 102 may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system 102 and the processing resource. In other examples, the processing engine(s) 208 may be implemented by electronic circuitry.
[0049] In an embodiment, the processor 202 may receive data associated with an emergency vehicle through the data ingestion engine 212, where the data may include a navigation route associated with the emergency vehicle. The processor 202 may receive information associated with one or more vehicles along the navigation route of the emergency vehicle using the data ingestion engine 212. The processor 202 may record the data and the information in the database 210. The information may include but not limited to a speed parameter associated with the one or more vehicles, a location associated with the one or more vehicles. Further, the data may include one or more parameters associated with the emergency vehicle and one or more location co-ordinates associated with the emergency vehicle.
[0050] In an embodiment, prior to receiving the data, the processor 202 may be configured to authenticate the emergency vehicle via an interface. The processor 202 may dynamically switch between a Websocket communication protocol and a Hypertext Transfer Protocol Application Programming Interface (HTTP API) call to receive data associated with the emergency vehicle and information associated with one or more vehicles.
[0051] In an embodiment, the processor 202 may use a frontend module configured to display real-time location of the emergency vehicle and the optimized navigation route on one or more user devices and receive notifications to reroute the emergency vehicle. The frontend module may allow a user to login the vehicle as an emergency service vehicle or as a regular vehicle. Once logged in as emergency service vehicle, the user may input their destination. The front end module may establish the WebSocket communication protocol with a backend configured with the processor 202, continuously sending the vehicle’s current location, speed, and status. Further, an interface may display the optimized route received from the backend server, updating in real-time if traffic conditions change. When nearby normal vehicles are alerted, the emergency vehicle driver can see which areas are clearing up. The front end module also includes additional features like traffic reports, historical route data, and real-time navigation through API calls. This ensures emergency responders can follow the quickest and safest path to their destination. The backend server may be built using Django and handle all real-time communication, route optimization, and data processing. This manages the connections of the WebSockets, allowing two-way instant communication between the vehicle and the server. When the vehicle sends its location, the backend server of the processor 202 may analyse traffic conditions and uses route optimization algorithms to find the fastest, and least congested path.
[0052] In an embodiment, the emergency vehicle may connect to the backend server using the WebSockets communication protocol, for sending data continuously. The backend server may process the data and use route optimization algorithms to determine the best route instantly. The one or more vehicles may receive alerts via the WebSockets communication protocol, allowing them to clear the way for emergency responders. The backend server may manage WebSocket connections and handle API requests for tasks like vehicle authentication, traffic updates, and historical data retrieval.
[0053] In an embodiment, the processor 202 may determine a bounding box to identify the one or more vehicles along the navigation route of the emergency vehicle. The processor 202, in response to a determination that one or more co-ordinates associated with the location of the one or more vehicles are located within the bounding box, may generate the one or more routing instructions to the one or more vehicles to facilitate clearance of the navigation route. The processor 202 may also determine variation in the received information and simultaneously generate one or more navigation paths associated with the emergency vehicle.
[0054] In an embodiment, the processor 202 may simultaneously generate an optimized route for the navigation of the emergency vehicle along the navigation route based on the received data and the received information using backend server.
[0055] In an embodiment, the processor 202 may generate one or more routing instructions to the one or more vehicles to facilitate clearance of the navigation route based on the optimized route using the backend server.
[0056] In an embodiment, the processor 202 may predict via the machine learning engine 214, one or more navigation routes associated with the emergency vehicle based on the received data and the optimized route. By making use of Hypertext Transfer Protocol (HTTP) requests and communication protocols, these systems have become more scalable and can be adapted into the current system in a simple and efficient way as the system 102 does not require any extra hardware to be placed/changed. Using API’s and communication protocols in parallel with each other aligns with the concepts of parallel processing and synchronous programming allows the system 102 function more efficiently and effectively.
[0057] In an embodiment, the processor 202 may not only optimize navigation routes but also may predictively clear the navigation path/route by interfacing with traffic sign control systems before the emergency vehicle arrives. This closed-loop integration may allow the backend server to continuously learn and adapt future routes using real-time traffic flow, enabling machine learning or predictive modeling enhancements. Hence, the system 102 provides enhanced route prediction with adaptive learning based on real-world feedback.
[0058] FIG. 3 illustrates an exemplary high-level flow diagram of the proposed system for generating optimized routes for navigation, in accordance with an embodiment of the present disclosure.
[0059] As illustrated in FIG. 3, in an embodiment, at step 302, an interface may record data associated with the emergency vehicle. The data may include but not limited to one or more parameters associated with the emergency vehicle and one or more location co-ordinates associated with the emergency vehicle. At step 304, a backend server may receive the data from the interface. At step 306, the backend server may receive location updates from the emergency vehicle. At step 308, the backend server may process the data through a machine learning engine 214. The machine learning engine 214 may predict one or more navigation routes associated with the emergency vehicle based on the received data. At step 310, the predicted one or more navigation routes may be provided to a traffic sign control integration module, which may clear the path for the emergency vehicle based on the predicted one or more navigation routes. The traffic sign control integration module may facilitate signal change by sending a request to a traffic infrastructure. At step 312, the traffic infrastructure may further send the traffic data to the backend server for further processing, which may help in predicting future navigation routes associated with the emergency vehicle.
[0060] FIG. 4 illustrates an exemplary block diagram of various interfaces used by the proposed system for generating optimized routes for navigation, in accordance with an embodiment of the present disclosure.
[0061] As illustrated in FIG. 4, in an embodiment, a Vehicle to Vehicle (V2V) server 402 may be developed used that helps to make full stack web applications. Using the V2V server 402, the system 102 may determine the most optimal route between two points and alert all emergency vehicles present in the determined route. The V2V server 402 may be paired with a Structured Query Language (SQL) database 404 to store information of different vehicles and their respective locations. The V2V 402 server may receive SQL queries from the SQL database 404 and send SQL results to the SQL database 404. An administrator module 406 may determine the status of the V2V server and generate administrator commands to control the V2V server 402. Further, the V2V server 402 may include an API that receives data associated with emergency vehicles 406. The V2V server 402 may receive information associated with one or more vehicles 408 along the navigation route of the emergency vehicle. The V2V server 402 may simultaneously generate an optimized route for the navigation of the emergency vehicle 406 along the navigation route based on the received data and the received information. The V2V server 402 may generate one or more routing instructions to the one or more vehicles 408 to facilitate clearance of the navigation route based on the optimized route. Furthermore, the V2V server 402 may predict via a machine learning engine 214, one or more navigation routes associated with the emergency vehicle 406 based on the received data and the optimized route. The V2V server 402 may send one or more emergency alerts to the emergency vehicle 406 through a Javascript Object Notation (JSON) alert mechanism. The V2V server 402 may send one or more routing instructions to the one or more vehicles 408 to facilitate clearance of the navigation route based on the optimized route.
[0062] In an embodiment, the backend server may allow the user to login, register and add location of the emergency vehicle, which is a functionality provided to both ordinary and emergency vehicles. The login Application Programming Interface (API) may check if the input credentials are present in the SQL database or not, and if they match. If they do match, a token may be returned and stored in the local storage of the backend server to indicate that the user has logged in. The input parameters may include but not limited to, an email and password, both of which may be provided in a JSON format. The register API may be used to create a new user. The credentials, i.e, email id, password, name, vehicle number and type of vehicles may be provided in the JSON format. Once the input data has been verified and has passed the minimum requirements, the user's data is added to the SQL database. Along with this, a web socket consumer may also be created for the user. The add location, one or more input parameters such as but not limited to vehicle ID and its co-ordinates may be provided to the backend server. An emergency application may be used only by the users registered under emergency services. The backend server may convert these co-ordinates to determine the most optimal route using a library. Once the optimal route has been determined, all the one or more vehicles within that navigation route may be identified through a bounding box search method and then alerted of the incoming emergency service vehicle. In order to alert the one or more vehicles, an alert message along with current location of the the emergency vehicles may be sent using web sockets. This allows for users to make decisions are the emergency vehicles location is being updated in real time. The system 102 makes use of the internet as a primary medium of communication, the use of which does not require any external hardware to be placed in geographical locations. Current systems like LoRa usually tend to use an onboard unit and multiple road side units to communicate information. By making use of HTTP requests and web sockets, the system 102 may be adapted in a simple and efficient way without requiring any extra hardware. Using API’s and web sockets in parallel with each other aligns with the concepts of parallel processing and synchronous programming. Further, optimizing the navigation route taken by an ambulance or any kind of emergency service vehicle can impact the lives of humans. To actualize this concept, the backend server and internet may be used. Hence, the system 102 may combine advanced backend architecture with a relatively simple front end, integrating many technologies, like but not limited to Django, MySQL, Redis, WebSockets and a ReactJS based frontend. The system 102 architecture allows seamless communication between ambulances and nearby vehicles, reducing delays and enabling faster emergency assistance.
[0063] In an exemplary embodiment, the system 102 may be implemented by hospitals, fire departments, and police services to notify nearby vehicles about approaching emergency vehicles, reducing response time and saving lives. Traffic authorities may integrate this system 102 into smart city infrastructure to manage traffic flow during emergencies, clearing routes for ambulances and other emergency vehicles. Logistics and transportation companies may use this system 102 to track their fleet in real time and prioritize vehicles on critical routes. Further various services may integrate this system to reroute drivers away from emergency routes, reducing congestion and delays. Insurance providers may use this system 102 to promote safe driving practices, potentially offering discounts to users who comply with emergency vehicle notifications. Further, governments may implement this system 102 as part of public safety initiatives to ensure emergency vehicles have unimpeded access to critical locations. Highway authorities may use the system 102 to prioritize toll clearance for emergency vehicles, allowing smooth passage during critical situations. In disaster-prone areas, this system 102 may be deployed to assist rescue and relief operations by notifying vehicles to clear paths for emergency response teams.
[0064] FIG. 5 illustrates a flow diagram of a method implemented by the proposed system for generating optimized routes for navigation, in accordance with an embodiment of the present disclosure.
[0065] As illustrated in FIG. 5, in an embodiment, at step 502, the method may include receiving, by a system 102, data associated with the emergency vehicle, where the data may include a navigation route associated with the emergency vehicle. At step 504, the method may include receiving, by the system 102, information associated with one or more vehicles along the navigation route of the emergency vehicle. At step 506, the method may include simultaneously generating, by the system 102, an optimized route for the navigation of the emergency vehicle along the navigation route based on the received data and the received information. At step 508, the method may include generating, by the system 102, one or more routing instructions to the one or more vehicles to facilitate clearance of the navigation route based on the optimized route.
[0066] FIG. 6 illustrates an exemplary computer system 600 in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.
[0067] As shown in FIG. 6, the computer system 600 may include an external storage device 610, a bus 620, a main memory 630, a read only memory 640, a mass storage device 650, a communication port 660, and a processor 670. A person skilled in the art will appreciate that the computer system 600 may include more than one processor and communication ports. The processor 670 may include various modules associated with embodiments of the present invention. The communication port 660 may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port 660 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects. The memory 630 may be a Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory 640 may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for the processor 670. The mass storage 650 may be any current or future mass storage solution, which may be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays).
[0068] The bus 620 may communicatively couple the processor(s) 670 with the other memory, storage and communication blocks. The bus 620 may be, e.g. a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects processor 670 to a software system.
[0069] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to the bus 620 to support direct operator interaction with the computer system 600. Other operator and administrative interfaces may be provided through network connections connected through the communication port 660. The external storage device 610 may be any kind of external hard-drives, floppy drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc-Re-Writable (CD-RW), Digital Video Disk-Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0070] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0071] The present disclosure uses standard internet protocols (over existing infrastructure, removing the need for special-purpose devices.
[0072] The present disclosure provides dynamic switching between communication protocols and which enables efficient communication, and reduces server load, and enhances responsiveness.
[0073] The present disclosure uses a combination of a backend server, a Structures Query Language (SQL) database, an interface, and a frontend which is be used for different city infrastructures or emergency systems.
[0074] The present disclosure uses software and Global Positioning System (GPS) data, making the proposed system unique and more adoptable across vehicle fleets with smart devices.
[0075] The present disclosure provides live location updates to all nearby vehicles, enabling proactive behavioral changes by nearby drivers (e.g., lane clearing), which is a practical and life-saving innovation.
, Claims:1. A system (106) for generating optimized routes, comprising:
a processor (202) communicatively coupled to an Electronic Control Unit (ECU) of an emergency vehicle;
a memory (204) operatively coupled with the processor (202), wherein said memory (204) stores instructions which, when executed by the processor (202), cause the processor (202) to:
receive data associated with the emergency vehicle, wherein the data comprises a navigation route associated with the emergency vehicle;
receive information associated with one or more vehicles along the navigation route of the emergency vehicle;
simultaneously generate an optimized route for the navigation of the emergency vehicle along the navigation route based on the received data and the received information; and
generate one or more routing instructions to the one or more vehicles to facilitate clearance of the navigation route based on the optimized route.
2. The system (106) as claimed in claim 1, wherein the processor (202) is configured to predict via a machine learning engine (214), one or more navigation routes associated with the emergency vehicle based on the received data and the optimized route.
3. The system (106) as claimed in claim 1, wherein prior to receiving the data, the processor (202) is configured to authenticate the emergency vehicle via an interface.
4. The system (106) as claimed in claim 1, wherein the information comprises at least one of: a speed parameter associated with the one or more vehicles, a location associated with the one or more vehicles.
5. The system (106) as claimed in claim 1, wherein the data comprises one or more parameters associated with the emergency vehicle and one or more location co-ordinates associated with the emergency vehicle.
6. The system (106) as claimed in claim 4, wherein the processor (202) is configured to:
determine a bounding box to identify the one or more vehicles along the navigation route of the emergency vehicle; and
in response to a determination that one or more co-ordinates associated with the location of the one or more vehicles are located within the bounding box, generate the one or more routing instructions to the one or more vehicles to facilitate clearance of the navigation route.
7. The system (106) as claimed in claim 1, wherein the processor (202) is configured to determine variation in the received information and simultaneously generate one or more navigation paths associated with the emergency vehicle.
8. The system (106) as claimed in claim 1, wherein the processor (202) is configured to dynamically switch between a Websocket communication protocol and a Hypertext Transfer Protocol Application Programming Interface (HTTP API) call to receive data associated with the emergency vehicle and information associated with one or more vehicles.
9. A method (500) for generating optimized routes, the method (500) comprising:
receiving (502), by a processor (202), associated with a system (106), data associated with an emergency vehicle, wherein the data comprises a navigation route associated with the emergency vehicle;
receiving (504), by the processor (202), information associated with one or more vehicles along the navigation route of the emergency vehicle;
simultaneously generating (506), by the processor (202), an optimized route for the navigation of the emergency vehicle along the navigation route based on the received data and the received information; and
generating (508), by the processor (202), one or more routing instructions to the one or more vehicles to facilitate clearance of the navigation route based on the optimized route.
10. The method as claimed in claim 9, comprising predicting, by the processor (202), via a machine learning engine (214), one or more navigation routes associated with the emergency vehicle based on the received data and the optimized route.
11. The method as claimed in claim 9, comprising authenticating, by the processor (202), the emergency vehicle via an interface, prior to receiving the data,
12. The method as claimed in claim 9, wherein the information comprises at least one of: a speed parameter associated with the one or more vehicles, a location associated with the one or more vehicles.
13. The method as claimed in claim 9, wherein the data comprises one or more parameters associated with the emergency vehicle and one or more location co-ordinates associated with the emergency vehicle.
14. The method as claimed in claim 9, comprising:
determining, by the processor (202), a bounding box for identifying the one or more vehicles along the navigation route of the emergency vehicle; and
in response to a determination that one or more co-ordinates associated with the location of the one or more vehicles are located within the bounding box, generating, by the processor (202), the one or more routing instructions to the one or more vehicles for facilitating clearance of the navigation route.
15. A system (106) for generating optimized routes, comprising:
a processor (202) communicatively coupled to a server;
a memory (204) operatively coupled with the processor (202), wherein said memory (204) stores instructions which, when executed by the processor (202), cause the processor (202) to:
receive data from one or more emergency vehicles through the server, wherein the server dynamically switches between one or more communication protocols to enable communication between the emergency vehicle and one or more vehicles;
receive information associated with one or more vehicles along a navigation route of the emergency vehicle through the server;
simultaneously generate an optimized route for the navigation of the emergency vehicle along the navigation route based on the received data and the received information;
the server configured to notify the one or more vehicles about the emergency vehicle via a real-time alert mechanism;
a frontend module configured to display real-time location of the emergency vehicle and the optimized navigation route on one or more user devices and receive notifications to reroute the emergency vehicle; and
predict via a machine learning engine (214), one or more navigation routes associated with the emergency vehicle based on the received data and the optimized route.