DESC:FIELD OF THE INVENTION
[0001] The present disclosure relates generally to methods and systems for estimating accident zone and generating accident alerts.

BACKGROUND
[0002] Accidents on highways and crossroads are often caused by several factors. These include overspeeding, road curvature, and the topography of roads. These factors contribute to dangerous driving conditions. This is particularly true in high-risk high-risk zone. Sharp turns, intersections, and areas with poor visibility or adverse weather are some of these high-risk zone. These factors frequently lead to accidents, which not only cause physical harm but also result in economic losses, traffic congestion, and psychological trauma for those involved. Given the increasing number of vehicles on the road and the growing complexity of traffic systems, ensuring road safety has become a crucial priority for governments and transportation authorities worldwide. The primary goal of accident prevention is to enhance safety by identifying potential risks and alerting individuals, as well as intervening to prevent or minimize the impact of accidents.

[0003] With advancements in technology, various techniques have been developed to mitigate risks in high-traffic and high-risk areas. These techniques include traffic monitoring, predictive analytics, and automated alert systems. Further, several existing patents in this field have explored systems designed to prevent accidents, with an emphasis on user alerts and data collection. For example, one existing system maps traffic accidents, stores historical and real-time accident data, and alerts users based on their location and accident type. This system uses a central server to store various accident-related information and provides users with advisory notifications based on their geocoded location and accident category. This system supports different categories of users, such as drivers, motorcyclists, bicyclists, and pedestrians, and analyzes accident data accordingly. Another existing system focuses on alerting users to prevent traffic accidents by mapping and storing relevant accident data, offering customizable notifications based on user type. This system includes data on vehicle types, motorcyclists, bicyclists, and pedestrians. This system aims to improve the accuracy and validity of notifications through user engagement panels and ratings, enabling real-time updates and rewards for valid contributions to accident-related data.

[0004] However, the existing techniques have limitations. For example, the existing techniques primarily focus on alerting users based on their current geocoded location, after they have already entered a potential accident zone. Furthermore, the accident data collected is subject to user ratings, which may not always be reliable. A key element missing from existing techniques is the concept of a Prevention Point (PP), where users are alerted before they reach high-risk areas. This early warning allows for proactive measures and accident avoidance, rather than just reacting after entering the accident zone.

[0005] Therefore, there remains a need for an improved solution that addresses the aforementioned challenges in the field of accident prevention.

SUMMARY
[0006] 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 essential inventive concepts of the invention nor is it intended for determining the scope of the invention.

[0007] According to an embodiment of the present disclosure, a system is disclosed. The system comprises a transceiver configured to receive real-time location data and corresponding timestamps from at least one device installed in a vehicle. The system further comprises a processor coupled to the transceiver. The processor is configured to determine at least one parameter associated with the vehicle based on the received real-time location data and corresponding timestamps. The processor is further configured to determine a Prevention Point (PP) for the vehicle based on the determined at least one parameter and a predefined time. The processor is furthermore configured to determine a Scanning Distance (SD) to be covered by the vehicle from the PP within the predefined time. Further, the processor is configured to identify potential accident zone within the SD based on at least one of road data associated with a road and historical accident data associated with the road.

[0008] In another embodiment, a device for installation in a vehicle is disclosed. The device comprises a processor configured to determine real-time location data and corresponding timestamps associated with the vehicle. The processor is further configured to transmit the determined real-time location data and corresponding timestamps to a command center. The processor is further configured to receive at least one accident alert from the command center in response to transmitting the real-time location data and corresponding timestamps.

[0009] In yet another embodiment, a method is disclosed. The method includes receiving real-time location data and corresponding timestamps from at least one device installed in a vehicle. The method further includes determining at least one parameter associated with the vehicle based on the received real-time location data and corresponding timestamps. The method furthermore includes determining a Prevention Point (PP) for the vehicle based on the determined at least one parameter and a predefined time. The method also includes determining a Scanning Distance (SD) to be covered by the vehicle from the PP within the predefined time. The method furthermore includes identifying potential accident zone within the SD based on at least one of road data associated with a road and historical accident data associated with the road.

[0010] In yet another embodiment, a method is disclosed. The method comprises determining real-time location data and corresponding timestamps associated with the vehicle. The method further comprises transmitting the determined real-time location data and corresponding timestamps to a command center. The method further comprises receiving at least one accident alert from the command center in response to transmitting the real-time location data and corresponding timestamps.

[0011] To further clarify the advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are 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 to its scope. The invention will be described and explained with additional specificity and detail in the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS
[0012] These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
[0013] FIG. 1 illustrates an environment for estimating accident zone and generating accident alerts, according to an embodiment of the present disclosure;
[0014] FIG. 2 illustrates a block diagram of a system for estimating accident zone and generating accident alerts, according to an embodiment of the present disclosure;
[0015] FIG. 3 illustrates an exemplary scenario for estimating accident zone and generating accident alerts, according to an embodiment of the present disclosure;
[0016] FIG. 4 illustrates a block diagram of a device for installation in a vehicle, according to an embodiment of the present disclosure;
[0017] FIG. 5 illustrates a flowchart depicting a method for estimating accident zone and generating accident alerts, according to an embodiment of the present disclosure; and
[0018] FIG. 6 illustrates a flowchart depicting a method for receiving accident alerts, according to an embodiment of the present disclosure;

[0019] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION
[0020] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the various embodiments, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

[0021] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the invention and are not intended to be restrictive thereof.

[0022] Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0023] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by “comprises... a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

[0024] The present disclosure discloses techniques for estimating accident zone and generating accident alerts. In an embodiment, the disclosed techniques provide a system that estimates potential accident zone and generates accident alerts based on the estimated potential accident zone. The disclosed techniques also provide a device that can be installed in a vehicle and receive accident alerts from the system.

[0025] Example embodiments of the present inventive concepts will be described below in detail with reference to the accompanying drawings.

[0026] It should be noted that the terms “compact device” and “device” have been interchangeably throughout the specification and drawings. Further, it should be noted that the terms “command center” and “system” have been interchangeably throughout the specification and drawings.

[0027] FIG. 1 illustrates an environment 100 for estimating accident zone and generating alerts, according to an embodiment of the present disclosure. As shown, the environment 100 includes a plurality of compact devices (also referred to as the devices 101), such as a first compact device 101A (also referred to as a first device 101A), a second compact device 101B (also referred to as a second device 101B), and a third compact device 101C (also referred to as a third device 101C). In an embodiment, the first compact device 101A may be installed in a first vehicle (not shown). Similarly, the second compact device 101B may be installed in a second vehicle (not shown). The third compact device 101C may be installed in a third vehicle (not shown) and so on. In an alternate embodiment, more than one compact device, such as the first device 101A and the second device 101B may be installed in a single vehicle. The devices 101 may be connected to a system 103 through a wireless communication network 105. In an embodiment, the system 103 may be configured to estimate the accident zone and generate the accident alerts. In some examples, the wireless communication network 105 may be a Long Term Evolution (LTE) network, an LTE-Advanced (LTE-A) network, an LTE-A Pro network, a New Radio (NR) network, a Fifth-Generation (5G) network, a Sixth-Generation (6G) network, a 6G-pro network or similar networks. In some examples, the wireless communication network 105 may support enhanced broadband communications, ultra-reliable (e.g., mission-critical) communications, low latency communications, communications with low-cost and low-complexity devices, or any combination thereof.

[0028] Further, the system 103 may be capable of executing Artificial Intelligence (AI) models. The system 103 may include or may be referred to as a mobile device, a wireless device, a remote device, a handheld device, a subscriber device, or some other suitable terminology, where the “device” may also be referred to as a unit, a station, a terminal, or a client, among other examples. The system 103 may also include or may be referred to as a personal electronic device, such as a cellular phone, a Personal Digital Assistant (PDA), a tablet computer, a laptop computer, or a personal computer. In some examples, the system 103 may include or be referred to as an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or A Machine-Type Communications (MTC) device, among other examples, which may be implemented in various objects such as appliances, or vehicles, meters, among other examples. The system 103 described herein may be able to communicate with various types of devices, such as other edge devices that may sometimes act as relays. The system 103 and working thereof have been further explained in reference to FIG. 2.

[0029] FIG. 2 illustrates a block diagram of the system 103 for estimating accident zone and generating accident alerts, according to an embodiment of the present disclosure.

[0030] As shown in FIG. 2, the system 103 may include a memory 202, at least one processor 204 (herein referred to as the processor 204), a transceiver 206, and an Input/ Output (I/O) interface 208. In an exemplary embodiment, the at least one processor 204 may be operatively coupled to the transceiver 206, the I/O interface 208, and the memory 202.

[0031] In one embodiment, the at least one processor 204 may be operatively coupled to the memory 202 for processing, executing, or performing a set of operations. The at least one processor 204 may include at least one data processor for executing processes in a Virtual Storage Area Network. In another embodiment, the at least one processor 204 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. In one embodiment, the processor 204 may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or both. In another embodiment, the at least one processor 204 may be one or more general processors, digital signal processors, application-specific integrated circuits, field-programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The at least one processor 204 may execute a software program, such as code generated manually (i.e., programmed) to perform one or more operations disclosed in the present disclosure.

[0032] The at least one processor 204 may be disposed in communication with one or more I/O devices, such as the edge device 101, via the I/O interface 208. The I/O interface 208 may employ communication Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE), WiMax, or the like, etc.

[0033] In an embodiment, the at least one processor 204 may be disposed in communication with a communication network via a network interface. In an embodiment, the network interface may be the I/O interface 208. The network interface may connect to the communication network to enable connection of the system 103 with the outside environment and/or device/system. The network interface may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11/b/g/n/x, etc. The communication network may include, without limitation, a direct interconnection, Local Area Network (LAN), Wide Area Network (WAN), wireless network (e.g., using Wireless Application Protocol (WAP)), the Internet, etc. Using the network interface and the communication network, the system 103 may communicate with other devices. The network interface may employ connection protocols including, but not limited to, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), TCP/IP, token ring, IEEE 802.11/b/g/n/x, etc.

[0034] In an embodiment, the processor 204 may use at least one artificial intelligence (AI) model. A function associated with AI may be performed through the non-volatile memory, the volatile memory, and the processor 204. Accordingly, the processor 204 may include one or a plurality of processors. At this time, one or a plurality of processors may be a general purpose processor, such as a Central Processing Unit (CPU), an Application Processor (AP), or the like, a graphics-only processing unit such as a Graphics Processing Unit (GPU), a Visual Processing Unit (VPU), and/or an AI-dedicated processor such as a Neural Processing Unit (NPU). The one or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or Artificial Intelligence (AI) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning.

[0035] In an embodiment, the processor 204 may be configured to perform the functions of the system 103, as described throughout the specification.

[0036] Furthermore, the memory 202 may include any non-transitory computer-readable medium 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.

[0037] The memory 202 is communicatively coupled with the processor 204 to store bitstreams or processing instructions for completing the process. Further, the memory 202 may include an operating system 210 for performing one or more tasks of the system 103, as performed by a generic operating system in the communications domain or the standalone device. In an embodiment, the memory 202 may comprise a database 212 configured to store the information as required by the processor 204 to perform one or more functions for estimating the accident zone and generating accident alerts, as discussed throughout the disclosure.

[0038] The memory 202 may be operable to store instructions executable by the processor 204. The functions, acts, or tasks illustrated in the figures or described may be performed by the processor 204 for executing the instructions stored in the memory 202. The functions, acts, or tasks are independent of the particular type of instruction set, storage media, processor, or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro-code, and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing, and the like.

[0039] For the sake of brevity, the architecture, and standard operations of the memory 202 and the processor 204 are not discussed in detail. In one embodiment, the memory 202 may be configured to store the information as required by the processor 204 to perform the methods described herein.

[0040] The transceiver 206 may be configured to receive real-time location data and corresponding timestamps from at least one device installed in a vehicle. In an embodiment, the at least one device may correspond to any one of the devices 101, such as the first device 101A. Accordingly, the transceiver 206 may receive the real-time location data and its corresponding timestamps from the first device 101A. Further, the real-time location data may indicate the real-time location of the vehicle, such as the first vehicle. The timestamps may indicate a time associated with the real-time location of the vehicle. For example, the real-time location data and corresponding timestamps may indicate that the vehicle is at “ABC” location at 10.00 PM and at “XYZ” location at 10.05 PM.

[0041] Accordingly, the real-time location data may be used to identify potential accident zone in accordance with the techniques described in the following paragraphs.

[0042] Further, the received real-time location data and corresponding timestamps may be stored in the memory 202 for further processing.

[0043] FIG. 2 is further explained in conjunction with FIG. 3. FIG. 3 illustrates an exemplary scenario 300 for estimating accident zone and generating accident alerts, according to an embodiment of the present disclosure. As shown in FIG. 3, a plurality of vehicles (also referred to as the vehicles 301), such as vehicle 1 301A, vehicle 2 301B, and vehicle 3 301C. The plurality of vehicles are travelling on different routes on a road. For example, the vehicle 1 301A is travelling on route 1, the vehicle 2 301B is travelling on route 2, and the vehicle 3 301C is travelling on route 3. Accordingly, in an embodiment, the system 103 is configured to identify the potential accident zone for at least one vehicle among the vehicles 301.

[0044] Referring back to FIG. 2, the system 103 may determine at least one parameter associated with the vehicle. In an embodiment, the system 103 may determine the at least one parameter based on the received real-time location data and corresponding timestamps. For example, the at least one parameter may include but is not limited to a speed of the vehicle, a direction of the vehicle, and a route of the vehicle. For example, the system 103 may determine that the speed of the vehicle 1 301A is 80 Kilometres per Hour (Kmph) and that the vehicle 1 is travelling on the route 1. Similarly, the system 103 may determine that the speed of the vehicle 2 301B is 100 kmph and the vehicle 1 is travelling on the route 1. In an embodiment, the system 103 may be configured to determine the at least one parameter using techniques known to a person skilled in the art.

[0045] Further, the system 103 may be configured to determine a Prevention Point (PP) for the vehicle. In an embodiment, the system 103 may determine the PP based on the determined at least one parameter and a predefined time. In an embodiment, the predefined time may be configurable and may be configured by the system 103, such as 20 seconds, 30 seconds, etc. In an embodiment, the system 103 may determine the PP using a pre-loaded road network data and historical accident data associated with the road. In an embodiment, the pre-loaded road network data may include but is not limited to information related to curvature of the road, information related to junctions on the road, information related to traffic on the road, and information related to condition of the road, and elevation of the road. For example, the pre-loaded road network data may indicate that the curvature of the road is 120 degrees, the road has 2 cross junctions, and the road has multiple potholes. Further, the historical accident data may include but is not limited to data associated with previous accidents that occurred on the road within a predefined historical time period, such as for the last 5 years. It should be noted that the predefined historical time period may be configurable and may be configured by the system 103. Accordingly, the system 103 may use the pre-loaded road network data, the historical accident data, the at least one parameter, and the predefined time. For example, the system 103 may determine a distance that the vehicle can travel in 20 seconds at 80 kmph on the road with 100 degree curvature. The location at the said distance may be considered as the PP for the vehicle, such as PP1 303A for the vehicle 1 301A, PP2 303B for the vehicle 1 301B, and PP3 303C for the vehicle 1 301C.

[0046] The system 103 may further be configured to determine a Scanning Distance (SD) to be covered by the vehicle from the PP within the predefined time. For example, the system 103 may determine the SD covered by the vehicle 1 301A from the PP 303A within 20 seconds, such as 350 meters (m).

[0047] The system 103 may then be configured to identify potential accident zone within the SD. In an embodiment, the system 103 may identify the potential accident zone based on at least one of the pre-loaded road network data and the historical accident data. For example, the system 103 may use the pre-loaded road network data in the same direction of movement of the vehicle. Then, the system 103 may evaluate various road conditions based on the pre-loaded road network data, and the historical accident data. For example, the system 103 may determine if the road curvature is less than 120 degrees, whether there are nearby crossroads or junctions, and if the current location of the vehicle is near any areas with a history of accidents. The system 103 may then determine if it is safe for the vehicle to cover the SD within the predefined time and at the speed of the vehicle under the evaluated road conditions. Accordingly, the system 103 may identify the potential accident zone, such as zone 305.

[0048] In a further embodiment, the system 103 may identify the potential accident zone using at least one AI model. The at least one AI model may predict high-risk accident areas with great accuracy by analyzing the historical accident data and the pre-loaded road network data. Additionally, integrating AI-driven insights with Geographical Information Systems (GIS) improves spatial analysis, allowing for more precise risk assessments and the development of strategies to reduce the occurrence of accidents.

[0049] The system 103 may then be configured to generate at least one accident alert based on the identification of the potential accident zone. The system 103 may then be configured to provide the at least one accident alert to the at least one device, such as the device 101A. In a further embodiment, the system 103 may be configured to provide a recommendation to the at least one device based on the generated at least one accident alert and the pre-loaded road network data. The recommendation may include but is not limited to a reduction in the speed of the vehicle and a distance to the identified potential accident zone. For example, the system 103 may recommend the first device 101A to reduce the speed of the vehicle as the road is bumpy. In an embodiment, the system 103 may be configured to provide the recommendation in one of a text message, an audio message, and a video message.

[0050] Referring back to FIG. 2, each of the devices 101 may be installed in a vehicle. The devices 101 and working thereof have been further explained in reference to FIG. 4.

[0051] FIG. 4 illustrates a block diagram of the device for installation in the vehicle, according to an embodiment of the present disclosure. In an exemplary embodiment, the first device 101A (also referred to as the device 101A) has been explained in FIG. 4. However, it should be noted that the structure of each of the devices 101 is the same as the structure of the first device 101A.

[0052] As shown, the device 101A may include a memory 402, at least one processor 404 (herein referred to as the processor 404), a location unit 406, and an Input/ Output (I/O) interface 408. In an exemplary embodiment, the at least one processor 404 may be operatively coupled to the memory 402, the location unit 406, and I/O interface 408.

[0053] In one embodiment, the at least one processor 404 may be operatively coupled to the memory 402 for processing, executing, or performing a set of operations. The at least one processor 404 may include at least one data processor for executing processes in a Virtual Storage Area Network. In another embodiment, the at least one processor 404 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. In one embodiment, the processor 404 may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or both. In another embodiment, the at least one processor 404 may be one or more general processors, digital signal processors, application-specific integrated circuits, field-programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The at least one processor 404 may execute a software program, such as code generated manually (i.e., programmed) to perform one or more operations disclosed in the present disclosure.

[0054] The at least one processor 404 may be disposed in communication with one or more I/O devices, such as the user devices 170, via the I/O interface 408. The I/O interface 408 may employ communication Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE), WiMax, or the like, etc.

[0055] In an embodiment, the at least one processor 404 may be disposed in communication with a communication network via a network interface. In an embodiment, the network interface may be the I/O interface 408. The network interface may connect to the communication network to enable connection of the device 101A with the outside environment and/or device/system. The network interface may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11/b/g/n/x, etc. The communication network may include, without limitation, a direct interconnection, Local Area Network (LAN), Wide Area Network (WAN), wireless network (e.g., using Wireless Application Protocol (WAP)), the Internet, etc. Using the network interface and the communication network, the device 101A may communicate with other devices. The network interface may employ connection protocols including, but not limited to, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), TCP/IP, token ring, IEEE 802.11/b/g/n/x, etc.

[0056] In an embodiment, the processor 404 may be configured to perform the functions of the device 101A, as described throughout the disclosure.

[0057] Furthermore, the memory 402 may include any non-transitory computer-readable medium 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.

[0058] The memory 402 is communicatively coupled with the processor 204 to store bitstreams or processing instructions for completing the process. Further, the memory 402 may include an operating system 410 for performing one or more tasks of the device 101A, as performed by a generic operating system in the communications domain or the standalone device. In an embodiment, the memory 402 may comprise a database 412 configured to store the information as required by the processor 204 to perform one or more functions for receiving accident alerts, as discussed throughout the disclosure.

[0059] The memory 402 may be operable to store instructions executable by the processor 404. The functions, acts, or tasks illustrated in the figures or described may be performed by the processor 404 for executing the instructions stored in the memory 402. The functions, acts, or tasks are independent of the particular type of instruction set, storage media, processor, or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro-code, and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing, and the like.

[0060] For the sake of brevity, the architecture, and standard operations of the memory 402 and the processor 404 are not discussed in detail. In one embodiment, the memory 402 may be configured to store the information as required by the processor 404 to perform the methods described herein.

[0061] In an embodiment, the device 101A may determine the real-time location data and corresponding timestamps associated with the vehicle. In particular, the device 101A may determine the real-time location data and corresponding timestamps using the location unit 406, such as a Global Positioning System (GPS). It should be noted that the real-time location data and timestamps have been explained in reference to FIG. 2. Hence, the same is not explained again for the sake of the brevity of the disclosure.

[0062] Further, the device 101A may transmit the determined real-time location data and corresponding timestamps to a command center.

[0063] Further, the device 101A may receive at least one accident alert from the command center in response to transmitting the real-time location data and corresponding timestamps. It should be noted that the at least one accident alert may correspond to the accident alert discussed in reference to FIG. 2.

[0064] In a further embodiment, the device 101A may comprise a display unit (not shown) and may display the at least one accident alert on the display unit. The device 101A may display the at least one accident alert in one of the text message, the audio message, and the video message. In an embodiment, the device 101A may provide the recommendation to the user in a pre-configured language configured by the user or geographical location based official language.

[0065] In an embodiment, the at least one accident alert may include a recommendation. Alternatively, the device 101A may also receive the recommendation separately from the at least one accident alert. Further, the device 101A may be configured to record user response to the received recommendation. For example, let us assume that the received recommendation suggests reducing the speed of the vehicle. Accordingly, the device 101A may be connected to a speedometer of the vehicle and may record if the speed of the vehicle was reduced. Accordingly, the device 101A may include one or more interfaces to connect with one or more sensors of the vehicle to record the user response. Then, the device 101A may estimate a negligence factor based on the user response. It should be noted that the recommendation may correspond to the recommendation discussed in reference to FIG. 2.

[0066] FIG. 5 illustrates a flowchart depicting a method for estimating accident zone and generating accident alerts, according to an embodiment of the present disclosure. The method 500 may be performed by the system 103. In another embodiment, the method 500 may be performed by the processor 204 of the system 103.

[0067] At step 502, the method 500 may include receiving real-time location data and corresponding timestamps from at least one device installed in a vehicle.

[0068] At step 504, the method 500 may include determining at least one parameter associated with the vehicle based on the received real-time location data and corresponding timestamps.

[0069] At step 506, the method 500 may include determining a Prevention Point (PP) for the vehicle based on the determined at least one parameter and a predefined time.

[0070] At step 508, the method 500 may include determining a Scanning Distance (SD) to be covered by the vehicle from the PP within the predefined time.

[0071] At step 510, the method 500 may include identifying potential accident zone within the SD based on at least one of the pre-loaded road network data associated with the road and historical accident data associated with the road.

[0072] While the above-discussed steps in FIG. 5 are shown and described in a particular sequence, the steps may occur in variations to the sequence in accordance with various embodiments. Further, a detailed description related to the various steps of FIG. 5 is already covered in the description related to FIGS. 1-3 and is omitted herein for the sake of brevity.

[0073] FIG. 6 illustrates a flowchart depicting a method for receiving accident alerts, according to an embodiment of the present disclosure. The method 600 may be performed by any one of the devices 101. In another embodiment, the method 600 may be performed by the processor 404 of the device 101A.

[0074] At step 602, the method 600 may include determining real-time location data and corresponding timestamps associated with the vehicle.

[0075] At step 604, the method 600 may include transmitting the determined real-time location data and corresponding timestamps to a command center, such as the system 103.

[0076] At step 606, the method 600 may include receiving at least one accident alert from the command center in response to transmitting the real-time location data and corresponding timestamps.

[0077] While the above-discussed steps in FIG. 6 are shown and described in a particular sequence, the steps may occur in variations to the sequence in accordance with various embodiments. Further, a detailed description related to the various steps of FIG. 6 is already covered in the description related to FIGS. 1 and 4 and is omitted herein for the sake of brevity.

[0078] Accordingly, the present disclosure provides various advantages. For example, the disclosed techniques help in improving safety in accident-prone areas by estimating prevention points and implementing an effective alert system. The disclosed techniques help in reducing the risk of accidents on highways, at crossroads, and on curved roads by accurately identifying prevention points. The disclosed techniques combine these prevention points in high-risk zone with an advanced alert system that delivers both visual and voice messages to warn drivers.

[0079] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

[0080] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein.

[0081] Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

[0082] Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
,CLAIMS:1. A system (103), comprising:
a transceiver (206) configured to receive real-time location data and corresponding timestamps from at least one device installed in a vehicle;
a processor (204) coupled to the transceiver (206) and configured to:
determine at least one parameter associated with the vehicle based on the received real-time location data and corresponding timestamps;
determine a Prevention Point (PP) for the vehicle based on the determined at least one parameter and a predefined time;
determine a Scanning Distance (SD) to be covered by the vehicle from the PP within the predefined time; and
identify potential accident zone within the SD based on at least one of pre-loaded road network data associated with a road and historical accident data associated with the road.

2. The system (103) as claimed in claim 1, wherein the processor (204) is further configured to:
generate at least one accident alert based on the identification of the potential accident zone; and
provide the at least one accident alert to the at least one device.

3. The system (103) as claimed in claim 2, wherein the processor (204) is further configured to:
provide a recommendation to the at least one device based on the generated at least one accident alert and the pre-loaded road network data, wherein the recommendation includes a reduction in a speed of the vehicle, and a distance to the identified potential accident zone; and
wherein the recommendation is provided in one of a text message, an audio message, and a video message.

4. The system (103) as claimed in claim 1, wherein the processor (204) is configured to identify the potential accident zone using at least one Artificial Intelligence (AI) model.

5. The system (103) as claimed in claim 1, wherein the pre-loaded road network data includes at least one of information related to curvature of the road, information related to junctions on the road, information related to traffic on the road, information related to condition of the road, and elevation of the road; and
wherein the at least one parameter includes a speed of the vehicle, a direction of the vehicle, and a route of the vehicle.

6. The system (103) as claimed in claim 1, wherein the processor (204) is configured to determine the PP using the pre-loaded road network data and the historical accident data associated with the road.

7. A device (101A) for installation in a vehicle, the device (101A) comprising:
a processor (404) configured to:
determine real-time location data and corresponding timestamps associated with the vehicle;
transmit the determined real-time location data and corresponding timestamps to a command center;
receive at least one accident alert from the command center in response to transmitting the real-time location data and corresponding timestamps.

8. The device (101A) as claimed in claim 7, wherein the processor (404) is further configured to:
record user response to the at least one accident alert, when the at least one accident alert includes a recommendation; and
estimate a negligence factor based on the user response.

9. A method (500) comprising:
receiving (502) real-time location data and corresponding timestamps from at least one device installed in a vehicle;
determining (504) at least one parameter associated with the vehicle based on the received real-time location data and corresponding timestamps;
determining (506) a Prevention Point (PP) for the vehicle based on the determined at least one parameter and a predefined time;
determining (508) a Scanning Distance (SD) to be covered by the vehicle from the PP within the predefined time; and
identifying (510) potential accident zone within the SD based on at least one of pre-loaded road network data associated with a road and historical accident data associated with the road.

10. The method (500) as claimed in claim 9, further comprising:
generating at least one accident alert based on the identification of the potential accident zone; and
providing the at least one accident alert to the at least one device.

11. The method (500) as claimed in claim 9, further comprising:
providing a recommendation to the at least one device based on the generated at least one accident alert and the pre-loaded road network data, wherein the recommendation includes a reduction in a speed of the vehicle and a distance to the identified potential accident zone; and
wherein the recommendation is provided in one of a text message, an audio message, and a video message.

12. The method (500) as claimed in claim 9, wherein identifying the potential accident zone comprises:
identifying the potential accident zone using at least one Artificial Intelligence (AI) model.

13. The method (500) as claimed in claim 9, wherein the pre-loaded road network data includes at least one of information related to curvature of the road, information related to junctions on the road, information related to traffic on the road, information related to condition of the road, and elevation of the road; and
wherein the at least one parameter includes a speed of the vehicle, a direction of the vehicle, and a route of the vehicle.

14. The method (500) as claimed in claim 9, wherein determining the PP comprises:
determining the PP using the pre-loaded road network data and the historical accident data associated with the road.
15. A method (600) comprising:
determining (602) real-time location data and corresponding timestamps associated with the vehicle;
transmitting (604) the determined real-time location data and corresponding timestamps to a command center; and
receiving (606) at least one accident alert from the command center in response to transmitting the real-time location data and corresponding timestamps.

16. The method (600) as claimed in claim 15, further comprising:
recording user response to the at least one accident alert when the at least one accident alert includes a recommendation; and
estimating a negligence factor based on the user response.