KR102357985B1 - System and method for sharing safe operation information based on location information of mobile communities - Google Patents

Abstract

According to an embodiment of the present invention, a mobile community location-based safe driving information sharing system comprises: a driver mobile that accesses a vehicle CAN network of a driving vehicle to collect vehicle driving and control information of the driving vehicle and measures and provides acceleration and inclination information during driving of the driving vehicle; and an artificial intelligence server that analyzes a driving risk rate of the corresponding vehicle based on the vehicle driving and control information and the acceleration and inclination information provided by the driver mobile and then provides the driving risk rate of the corresponding vehicle to a driver mobile of a neighboring vehicle located within a preset radius of the corresponding vehicle.

Description

Mobile community location-based safe driving information sharing system and method {System and method for sharing safe operation information based on location information of mobile communities}

The present invention relates to a mobile community location-based safe driving sharing system and method that can prevent secondary accidents by receiving dangerous data that can be detected in a vehicle when moving a vehicle in a region in real time from a vehicle and a mobile device and sharing it with a driver in a hazardous area.

As the vehicle is widely distributed and used, various driving assistance devices that help in driving the vehicle have been developed and used. In particular, in recent years, dangerous driving such as reckless driving and drowsy driving has emerged as a major social problem, and various driving assistance devices have been developed to prevent such dangerous driving.

Dangerous driving prevention devices according to the prior art are generally installed in a vehicle, such as GPS (global positioning system) information, acceleration information from a driving information collection terminal (eg, a navigation device capable of collecting driving information, a smart phone, etc.) It collects driving information, analyzes the collected driving information, and analyzes the driving pattern of the vehicle to determine whether dangerous driving is present. Since these conventional technologies depend only on the driving information collection terminal installed in a specific vehicle, there is a problem that the surrounding environment, the location of the surrounding vehicle, the speed of the surrounding vehicle, the path of the surrounding vehicle, etc. cannot be considered at all. In particular, in the prior art, when it is determined that the vehicle is currently driving dangerously as a result of analyzing the driving pattern of a specific vehicle, a dangerous driving warning can be provided only to the driver who is driving the vehicle, and the vehicle is considered a dangerous driving vehicle. There is a problem in that it cannot provide any warning related to dangerous driving to nearby vehicles that may be damaged by the

Laid-open Patent Publication No. 10-2018-0017906

An object of the present invention is to provide a mobile community location-based safe driving information sharing system and method that can solve the conventional problems.

The mobile community location-based safe driving information sharing system according to an embodiment of the present invention for solving the above problems executes the vehicle safe driving monitoring platform and then connects to the vehicle CAN network of the driving vehicle to drive and control the driving vehicle a driver mobile that collects information and measures and provides information on acceleration and inclination when the driving vehicle is driven; And after analyzing the driving risk rate of the corresponding vehicle based on the vehicle driving and control information, acceleration, and inclination information provided by the driver mobile, driving the corresponding vehicle with the driver's mobile of the adjacent vehicle located within a preset radius from the corresponding vehicle and an artificial intelligence server that provides a risk rate, wherein the artificial intelligence server collects road condition information and road condition information in which the driving vehicle is located from an external terminal, and then safety driving information according to the road condition information and the road condition information and the driving risk rate of the driving vehicle is calculated by comparing and judging steering information, braking information, and inclination information in the location information of the driving vehicle collected for a preset time, and when the calculated increase in driving risk exceeds a preset value , setting the position at which the driving vehicle travels as a dangerous section from the time of exceeding, transmits the dangerous section message or alarm to the driver's mobile located at the rear of the driving vehicle, and transmits the inclination information based on the road condition information After correction, the corrected slope value is applied to calculate the driving risk rate, and if the dangerous section is duplicated in a plurality of preceding vehicles, 'Caution' and 'Danger' according to the number of overlapping preceding vehicles And it is characterized in that it provides a classification of the risk section as 'very dangerous'.

In one embodiment, the driver mobile is characterized in that the real-time inclination information, acceleration information, and GPS information of the driving vehicle are transmitted to the artificial intelligence server.

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In one embodiment, when the artificial intelligence server reaches a preset position from the dangerous section, it is characterized in that it transmits a safety warning message to the mobile terminal for each preset distance unit.

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In a mobile community location-based safe driving information sharing method according to an embodiment of the present invention for solving the above problems, after executing the vehicle safe driving monitoring platform in the driver's mobile, it connects to the vehicle CAN network of the driving vehicle and the vehicle of the driving vehicle collecting driving and control information, and measuring and providing acceleration and inclination information when the driving vehicle is driving; After analyzing the driving risk rate of the corresponding vehicle based on the vehicle driving and control information, acceleration, and inclination information provided by the driver mobile in the AI server, the corresponding vehicle and the driver mobile of an adjacent vehicle located within a preset radius and providing the driving risk rate of the vehicle to the driver's mobile, wherein the providing to the driver's mobile includes, in the artificial intelligence server, after collecting road condition information and road condition information in which the driving vehicle is located from an external terminal, Calculating the driving risk rate of the driving vehicle by comparing and determining the safe driving information according to the road condition information and the road condition information with the location information, steering information, braking information, and inclination information of the driving vehicle collected for a preset time ; and when the calculated increase in driving risk rate exceeds a preset value, the dangerous section message or alarm is transmitted to the driver's mobile located at the rear of the driving vehicle by setting the location at which the driving vehicle travels as a dangerous section from the time it exceeds the preset value. Comprising the step of transmitting, calculating the driving risk rate of the driving vehicle comprises correcting the inclination information based on the road condition information, then calculating the driving risk rate using the corrected inclination value, and the risk section includes a plurality of and classifying the dangerous section into 'caution', 'danger', and 'very dangerous' according to the number of overlapping preceding vehicles.

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In an embodiment, the step of measuring and providing the acceleration and inclination information during driving of the driving vehicle is characterized in that the step of transmitting real-time inclination information, acceleration information, and GPS information of the driving vehicle to the artificial intelligence server.

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In one embodiment, the step of transmitting the danger section message or alarm comprises transmitting a safety warning message to the mobile terminal for each preset distance unit when a preset position is reached from the dangerous section. .

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The mobile community location-based safe driving information sharing system according to an embodiment of the present invention predicts a black ice slip accident in winter that a driver cannot recognize in advance, a slip accident in heavy rain during the rainy season, and a front blind spot accident in an unpredictable situation. Information on various risk factors of the road situation ahead that the driver cannot predict, such as accidents, can be alerted to the driver through the preceding vehicle. There is an advantage of being able to actively defend or prevent a secondary accident to the driver following in the section.

1 is a network configuration diagram of a mobile community location-based safe driving information sharing system according to an embodiment of the present invention.
FIG. 2 is a device configuration diagram of the driver's mobile shown in FIG. 1 .
3 is a device configuration diagram of the artificial intelligence server shown in FIG.
FIG. 4 is an exemplary view of an execution screen of a vehicle monitoring service application executed in the mobile terminal shown in FIG. 1 .
5 is a flowchart illustrating a mobile community location-based safe driving information sharing method according to an embodiment of the present invention.
6 is a flowchart illustrating the process S720 shown in FIG. 5 in more detail.
7 illustrates an example computing environment in which one or more embodiments disclosed herein may be implemented.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement them. However, the present invention may be embodied in several different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part is "connected" with another part, this includes not only the case of being "directly connected" but also the case of being "electrically connected" with another element interposed therebetween. . In addition, when a part "includes" a certain component, it means that other components may be further included, rather than excluding other components, unless otherwise stated, and one or more other features However, it is to be understood that the existence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded in advance.

The terms "about", "substantially", etc. to the extent used throughout the specification are used in or close to the numerical value when manufacturing and material tolerances inherent in the stated meaning are presented, and are intended to enhance the understanding of the present invention. To help, precise or absolute figures are used to prevent unfair use by unconscionable infringers of the stated disclosure. As used throughout the specification of the present invention, the term "step of (to)" or "step of" does not mean "step for".

In this specification, a "part" includes a unit realized by hardware, a unit realized by software, and a unit realized using both. In addition, one unit may be implemented using two or more hardware, and two or more units may be implemented by one hardware. Meanwhile, '~ unit' is not limited to software or hardware, and '~ unit' may be configured to be in an addressable storage medium or to reproduce one or more processors. Thus, as an example, '~' denotes components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, and procedures. , subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. The functions provided in the components and '~ units' may be combined into a smaller number of components and '~ units' or further separated into additional components and '~ units'. In addition, components and '~ units' may be implemented to play one or more CPUs in a device or secure multimedia card.

Some of the operations or functions described as being performed by the terminal, apparatus, or device in the present specification may be performed instead of by a server connected to the terminal, apparatus, or device. Similarly, some of the operations or functions described as being performed by the server may also be performed in a terminal, apparatus, or device connected to the server.

In this specification, some of the operations or functions described as mapping or matching with the terminal means mapping or matching the terminal's unique number or personal identification information, which is the identification data of the terminal. can be interpreted as

Hereinafter, a mobile community location-based safe driving information sharing system and method according to an embodiment of the present invention will be described in more detail based on the accompanying drawings.

1 is a network configuration diagram of a mobile community location-based safe driving information sharing system according to an embodiment of the present invention, FIG. 2 is a device configuration diagram of the driver's mobile shown in FIG. 1, and FIG. It is a device configuration diagram of an artificial intelligence server, and FIG. 4 is an exemplary view of an execution screen of a vehicle monitoring service application executed in the mobile terminal shown in FIG. 1 .

First, as shown in FIG. 1 , the mobile community location-based safe driving information sharing system 100 according to an embodiment of the present invention includes a driver mobile 200 and an artificial intelligence server 300 .

The driver mobile 200 and the artificial intelligence server 300 communicate through a network.

Here, the network means a connection structure in which information exchange is possible between each node, such as a plurality of terminals and servers, and an example of such a network includes an RF, 3rd Generation Partnership Project (3GPP) network, Long Term Evolution (LTE) network, 5th Generation Partnership Project (5GPP) network, World Interoperability for Microwave Access (WIMAX) network, Internet, Local Area Network (LAN), Wireless LAN (Wireless Local Area Network), WAN ( Wide Area Network), PAN (Personal Area Network), Bluetooth (Bluetooth) network, NFC network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, etc. are included, but are not limited thereto.

The driver mobile 200 is a terminal in which an application supporting a vehicle information monitoring service is installed, and may access the above-described artificial intelligence server 300 through the corresponding application.

The application may access the CAN (Cantroller Area Network) network of the vehicle, collect vehicle driving-related information from systems inside the vehicle, and transmit the collected information to the artificial intelligence server 300 to be described later.

For reference, CAN (Controller Area Network) was first developed for vehicle networks by Bosch in 1985, and CAN provides an economical and stable network in which several CAN devices can communicate with each other.

The advantage of such a network is that the electronic control units (ECUs) have only a single CAN interface, rather than having analog and digital inputs for each device in the system. Thus, the overall cost and weight of the vehicle could also be reduced. Each device on the network has a CAN controller chip, which is very intelligent, and every device on the network checks every message being sent. Each device decides whether the message is relevant or needs to be filtered.

Also, since all messages have priorities, when two nodes transmit a message at the same time, the message with the higher priority is transmitted first and the message with the lower priority is delayed. An internal system of a vehicle, for example, On-Board Diagnostics, or OBD is a term used in the automobile industry to refer to a device that diagnoses itself and reports the results.

Recently produced automobiles are equipped with sensors for various measurement and control, and these devices are controlled by ECU (Electronic Control Unit). The original purpose of ECU development was to precisely control the core functions of the engine, such as ignition timing, fuel injection, variable valve timing, idling, and limit value setting. It even plays a role in controlling all parts of the vehicle, such as the system and steering system.

On the other hand, vehicle information collected through the CAN network in the vehicle information monitoring application of the present application is limited information, and among the information provided to be allowed by the automobile manufacturer among the CAN networks in the vehicle, the vehicle's steering information, braking information, speed information, and corresponding information It may be status information of measuring devices, and the like.

In addition, the application may execute a navigation program to measure and display information on vehicle movement information and vehicle movement (slope) to the user, and may provide it to the artificial intelligence server 300 .

In addition, the application supports the black box function that shoots the front, and traffic law violation situations (eg, lane violation, signal violation, center line violation, tail biting, loading violation, non-compliance with direction signals, etc.) After generating a traffic law violation report message for any one of shoulder violation, designated lane violation, interruption, etc.), it may be selectively transmitted to the artificial intelligence server 300 . In addition, by storing/recording only relevant information (eg, location information, G-sensor information, speed, etc.) related to violations of traffic laws of the vehicle in front, only images in which violations are found can be efficiently managed.

The application can report the traffic law violation date, time, place, GPS, vehicle number, and violation confirmation of the vehicle in front online to the server of the relevant institution, and at least one or more information of whether the relevant institution notifies the result and the name of the video file can also be recorded/saved.

Referring to FIG. 2 , the driver mobile 200 includes a communication unit 210 , a storage unit 220 , a display unit 230 , an input unit 240 , an output unit 250 , and a control measurement unit 260 . Not all of the components shown in FIG. 2 are essential components, and the traffic law violation report and monitoring service application may be executed by more components than the components shown in FIG. 2, and vehicle monitoring by fewer components A service application may be executed.

The driver's mobile 200 includes a smart phone, a portable terminal, a mobile terminal, a foldable terminal, a personal digital assistant (PDA), and a portable terminal (PMP). Multimedia Player) terminal, telematics terminal, navigation terminal, personal computer, notebook computer, slate PC, tablet PC, ultrabook, wearable device ( Wearable Device, for example, watch-type terminal (Smartwatch), glass-type terminal (Smart Glass), HMD (Head Mounted Display), etc.), Wibro (Wibro) terminal, IPTV (Internet Protocol Television) terminal, smart TV, digital It may be a broadcasting terminal, an AVN (Audio Video Navigation) terminal, an A/V (Audio/Video) system, a flexible terminal, and the like.

The communication unit 210 communicates with any internal component or at least one external terminal through a wired/wireless communication network. In this case, the external arbitrary terminal may include a server (not shown), another terminal (not shown), and the like. Here, as wireless Internet technologies, wireless LAN (WLAN), DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband: Wibro), Wimax (World Interoperability for Microwave Access: Wimax), HSDPA (High Speed Downlink Packet Access) ), High Speed Uplink Packet Access (HSUPA), IEEE 802.16, Long Term Evolution (LTE), Long Term Evolution-Advanced (LTE-A), Wireless Mobile Broadband Service (WMBS), etc. In this case, the communication unit 210 transmits and receives data according to at least one wireless Internet technology within a range including Internet technologies not listed above. In addition, short-range communication technologies include Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, and Near Field Communication (NFC). , Ultra Sound Communication (USC), Visible Light Communication (VLC), Wi-Fi (Wi-Fi), Wi-Fi Direct (Wi-Fi Direct), etc. may be included. In addition, the wired communication technology may include Power Line Communication (PLC), USB communication, Ethernet, serial communication, optical/coaxial cable, and the like.

Also, the communication unit 210 may mutually transmit information with an arbitrary terminal through a Universal Serial Bus (USB).

In addition, the communication unit 210 is a technology standard or communication method for mobile communication (eg, GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), CDMA2000 (Code Division Multi Access 2000), EV -Enhanced Voice-Data Optimized or Enhanced Voice-Data Only (DO), Wideband CDMA (WCDMA), High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), Long Term Evolution (LTE), LTE-A (Long Term Evolution-Advanced, etc.) transmits and receives radio signals to and from the base station, the server, and the other terminal on a mobile communication network constructed according to the same.

In addition, the communication unit 210 receives data related to a vehicle monitoring service application provided from the artificial intelligence server 300 that is connected through wired/wireless communication under the control of the control measuring unit 260 to be described later.

Next, the storage unit 220 stores various user interfaces (UIs), graphic user interfaces (GUIs), and the like.

In addition, the storage unit 220 stores data and programs necessary for the vehicle monitoring service application to operate.

That is, the storage unit 220 may store a plurality of application programs (application programs or applications) driven in the vehicle monitoring service application, data for operation of the traffic law violation report and monitoring service application, and commands. have. At least some of these applications may be downloaded from an external server through wireless communication. In addition, at least some of these application programs may be installed from the time of shipment for a basic function of the vehicle monitoring service application. Meanwhile, the application program may be stored in the storage unit 220 and driven to perform an operation (or function) of the vehicle monitoring service application by the control measurement unit 260 .

In addition, the storage unit 220 is a flash memory type (Flash Memory Type), a hard disk type (Hard Disk Type), a multimedia card micro type (Multimedia Card Micro Type), a card type memory (eg, SD or XD) memory, etc.), magnetic memory, magnetic disk, optical disk, RAM (Random Access Memory: RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory: ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), It may include at least one storage medium among Programmable Read-Only Memory (PROM).

In addition, the storage unit 220 stores the vehicle monitoring service application received under the control of the control measuring unit 260, data related to the application, and the like.

The display unit (or display unit) 230 may display various contents such as various menu screens using the user interface and/or graphic user interface stored in the storage unit 220 under the control of the control measuring unit 260 . can

Here, the content displayed on the display unit 230 includes various text or image data (including various information data) and a menu screen including data such as icons, list menus, and combo boxes. Also, the display unit 230 may be a touch screen.

In addition, the display unit 230 includes a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), and a flexible display. (Flexible Display), a three-dimensional display (3D Display), an e-ink display (e-ink display), may include at least one of LED (Light Emitting Diode).

In addition, the display unit 230 displays an execution result screen of the vehicle monitoring service application under the control of the control measurement unit 260 .

Next, the input unit 240 may support an input interface for inputting the user's voice and touch information.

The output unit 250 outputs audio information included in a signal processed by a predetermined signal by the control measurement unit 250 . Here, the output unit 250 may include a receiver, a speaker, a buzzer, and the like. The output unit 250 outputs a guide voice (with or without result processing) generated by the control and measurement unit 260 .

In addition, the output unit 250 outputs the voice information (or sound effect) corresponding to the execution result screen of the traffic law violation report and monitoring service application by the control and measurement unit 260 .

Next, the control unit (controller, or microcontroller unit (MCU) 260) executes the overall execution control function of the vehicle monitoring service application.

The control measuring unit 260 may include RAM, ROM, CPU, GPU, and a bus, and the RAM, ROM, CPU, GPU, etc. may be connected to each other through a bus. The CPU may access the storage unit 220 and perform booting using the O/S stored in the storage unit 220 , and use various programs, contents, data, etc. stored in the storage unit 220 . Thus, various operations can be performed.

In addition, the control measuring unit 260 may include a tilt sensor and an acceleration sensor, and may be configured to provide information measured by each sensor to the artificial intelligence server 300 to be described later through the communication unit.

In addition, the control and measurement unit 260 executes the black box program, and based on the image captured by the mobile camera, traffic law violation situations (eg, lane violation, signal violation, center line violation, tail biting, loading violation, After generating a traffic law violation report message for any one of non-compliance with turn signals, shoulder violation, designated lane violation, interruption, etc.), it may be selectively transmitted to the artificial intelligence server 300 through the communication unit.

In addition, by storing/recording only relevant information (eg, location information, G-sensor information, speed, etc.) related to violations of traffic laws of the vehicle in front, only images in which violations are found can be efficiently managed.

In addition, the date, time, location, GPS, vehicle number, and confirmation of violation of the traffic laws of the vehicle in front can be reported online to the server of the relevant institution, and at least one or more of the result notification and the name of the video file can be received from the relevant institution. You can also record/save it.

More specifically, when the vehicle monitoring service application is executed according to a user selection (or user touch/voice input), the control measurement unit 260 displays an execution result screen on the display unit.

In addition, the control measurement unit 260 supports the selective control function so that the CAN network and the mobile 200 of the vehicle are selectively plugged in/out, and when the vehicle is driven, information related to vehicle operation is received from the CAN network and is transmitted through the communication unit. It may be a configuration provided by the artificial intelligence server 300 .

In addition, the control measuring unit 260 controls the operation of the display unit so that the navigation program is executed on the display unit.

In addition, the control measurement unit 260 controls the operation of the display unit so that the information received from the artificial intelligence server is executed on the display unit 230 and the output unit 250 .

Next, the artificial intelligence server 300 collects vehicle information (vehicle location, movement direction, vehicle provision related data, vehicle inclination data) from at least one mobile 200 in which the vehicle driving monitoring application is installed, and the collected vehicle information Based on the analysis and judgment of safe driving status, accident occurrence, and accident probability, it provides information on the safe operation of vehicles adjacent to mobile and mobile and other mobiles located within a preset radius and whether an accident has occurred. can

In addition, the artificial intelligence server 300 may provide location information of a vehicle determined to be a safe driving risk to a terminal located within a preset radius from the corresponding dangerous vehicle.

In addition, the artificial intelligence server 300 collects road condition information and road condition information in which the driving vehicle is located from an external terminal, and then collects the road condition information and safe driving information according to the road condition information for a preset time. The driving risk rate of the driving vehicle is calculated by comparing and judging the location information, steering information, braking information, and inclination information of the driving vehicle. It may be configured to transmit the dangerous section message or alarm to the driver's mobile located at the rear of the driving vehicle by setting the driving position as a dangerous section.

In addition, the artificial intelligence server 300 may be configured to correct the inclination information based on the road condition information and then apply the corrected inclination value to calculate the driving risk rate.

In addition, the artificial intelligence server 300 may be configured to transmit a safety warning message to the mobile terminal for each preset distance unit when a preset position is reached from the dangerous section.

The artificial intelligence server 300 ranks the dangerous section as 'caution', 'dangerous' and 'very dangerous' according to the number of overlapping preceding vehicles when the dangerous section is duplicated in a plurality of preceding vehicles. It may be a configuration provided by classification.

More specifically, the artificial intelligence server 300 includes an information collection unit 310 , an information analysis unit 320 , a risk determination unit 330 , and a transmission unit 340 .

The information collection unit 310 may be configured to collect vehicle information (vehicle location, movement direction, vehicle provision related data, vehicle inclination data) of the driving vehicle from the driver mobile 200 executing the vehicle driving monitoring application.

In addition, the information collection unit 310 may be configured to collect regional road traffic information and road condition information (such as a slope of a road) from an external server or an external terminal.

In addition, the information collection unit 310 detects traffic law violation situations (eg, lane violation, signal violation, center line violation, tail biting, loading violation, non-compliance with a turn signal light, shoulder violation, designated lane violation) from the driver mobile 200 , interruption, etc.) may be configured to collect a traffic law violation report message for any one.

Next, the information analysis unit 320, based on the road condition information and the road condition information in which the corresponding driving vehicle is located, collected from the external terminal, provides the safety driving information of the driving vehicle according to the road condition information and the road condition information and preset It may be a configuration for analyzing steering information, braking information, and inclination information at the location of the driving vehicle collected over time.

Next, the risk determination unit 330 compares and determines the safety driving information according to the road condition information and the road condition information, and steering information, braking information, and inclination information in the location information of the driving vehicle collected for a preset time. Thus, the driving risk rate of the driving vehicle is calculated, and when the increase in the calculated driving risk rate exceeds a preset value, the location at which the driving vehicle travels from the time point exceeding the preset value is set as a dangerous section.

In this case, when the dangerous section is duplicated in a plurality of preceding vehicles, the dangerous section is classified into 'caution', 'danger' and 'very dangerous' according to the number of overlapping preceding vehicles. .

The risk determination unit 330 corrects the inclination information provided by the driver mobile 200 based on the road condition information, and then calculates the driving risk rate using the corrected inclination value. Here, the risk rate is expressed numerically.

For example, when the slope information is provided while climbing the hill (slope 5) of the point A of the vehicle, the risk determination unit 330 compares the sameness with the slope of the preset road condition information to determine the slope if the slope is the same. It can also be corrected to 0'.

That is, the risk determination unit 330 based on the above four pieces of information (vehicle location, movement direction, vehicle braking-related data, and short-time inclination data of the vehicle) based on the movement direction of the driving vehicle of the corresponding driver mobile. By arranging the vehicle driving data, it is possible to set the radius of 500m before the arrival of the vehicle driving data generation location as a dangerous section.

For example, it can be seen that the risk determination unit 330 calculates the risk of the driving vehicle based on sudden braking, sudden stopping, and irregularly measured inclination.

For reference, the risk determination unit 330 disclosed in the present invention may employ a deep learning learning algorithm, and the deep learning learning algorithm is a Deep Belief Network, Autoencoder, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Q-Network and the like, and the deep learning learning algorithm listed in the present invention is only an example, and is not limited thereto. In addition, the risk determination unit 330 may adopt not only a deep learning algorithm but also a machine learning algorithm other than deep learning learning algorithms such as SVM and k-means square that can grasp shape information, and it is dynamic to calculate the amount of change per unit time. It is also possible to adopt the formula for calculating the amount of change

Next, when the calculated increase in the driving risk rate exceeds a preset value, the transmission unit 340 sets the position at which the driving vehicle travels as a dangerous section from the time when it exceeds the driving risk ratio of the driver located at the rear of the driving vehicle. to transmit the danger section message or warning message or alarm.

In addition, when the driving vehicle arrives at a preset location from the dangerous section, the transmitter 340 may transmit a safety warning message for each preset distance unit, and continuously provide an alarm sound as the dangerous section is adjacent to or You can change the length of the alarm sound.

5 is a flowchart illustrating a mobile community location-based safe driving information sharing method according to an embodiment of the present invention, and FIG. 6 is a flowchart illustrating the process S720 illustrated in FIG. 5 in more detail.

5 and 6, the mobile community location-based safe driving information sharing method (S700) according to an embodiment of the present invention first executes the vehicle safe driving monitoring platform in the driver's mobile, and then the vehicle CAN of the driving vehicle By accessing the network, vehicle driving and control information of the driving vehicle is collected, and acceleration and inclination information are measured and provided during driving of the driving vehicle (S710).

Thereafter, after analyzing the driving risk rate of the corresponding vehicle based on the vehicle driving and control information, acceleration and inclination information provided by the driver mobile in the artificial intelligence server 300 , the adjacent vehicle located within a preset radius from the corresponding vehicle The driving risk rate of the corresponding vehicle is provided to the driver's mobile by the driver's mobile (S720).

The step S710 includes transmitting real-time inclination information, acceleration information, and GPS information of the driving vehicle to the artificial intelligence server.

In the S720 process, the artificial intelligence server 300 collects road condition information and road condition information in which the driving vehicle is located from an external terminal, and then includes safety driving information according to the road condition information and the road condition information and preset The driving risk rate of the driving vehicle is calculated by comparing and judging the location information, steering information, braking information, and inclination information of the driving vehicle collected over time (S721), and when the calculated increase in driving risk rate exceeds a preset value, It may be a step of transmitting (S722) the dangerous section message or alarm to the driver's mobile located at the rear of the driving vehicle by setting the location where the driving vehicle travels from the time exceeding the dangerous section.

In this case, the step S721 may include correcting the inclination information based on the road condition information, and then calculating the driving risk rate using the corrected inclination value.

In addition, if the dangerous section is duplicated in a plurality of preceding vehicles, classifying the dangerous section into 'caution', 'danger' and 'very dangerous' according to the number of overlapping preceding vehicles. can

The step S722 may be a process of transmitting a safety warning message to the mobile terminal for each preset distance unit when a preset position is reached from the dangerous section.

Therefore, if the mobile community location-based safe driving information sharing system according to an embodiment of the present invention is used, predictions such as black ice slip accident in winter, slipping accident in heavy rain in the rainy season, and front blind spot accident that the driver cannot recognize in advance It draws attention to the driver through the risk notification community data generated through the preceding vehicle with information on various risk factors of the forward road situation that the driver cannot predict, such as a rear-end collision between sudden braking in an impossible situation, and a rear-end collision accident with high-concentration haze. There is an advantage in that it is possible to actively defend or prevent a secondary accident to the driver following the driving section by changing the driving method such as the driving speed of the vehicle, defensive driving, and bypass search.

7 is a diagram illustrating an example computing environment in which one or more embodiments disclosed herein may be implemented, and is an illustration of a system 1000 including a computing device 1100 configured to implement one or more embodiments described above. shows For example, computing device 1100 may be a personal computer, server computer, handheld or laptop device, mobile device (mobile phone, PDA, media player, etc.), multiprocessor system, consumer electronics, minicomputer, mainframe computer, distributed computing environments including any of the aforementioned systems or devices, and the like.

The computing device 1100 may include at least one processing unit 1110 and a memory 1120 . Here, the processing unit 1110 may include, for example, a central processing unit (CPU), a graphic processing unit (GPU), a microprocessor, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGA), etc. and may have a plurality of cores. The memory 1120 may be a volatile memory (eg, RAM, etc.), a non-volatile memory (eg, ROM, flash memory, etc.), or a combination thereof. Additionally, computing device 1100 may include additional storage 1130 . Storage 1130 includes, but is not limited to, magnetic storage, optical storage, and the like. The storage 1130 may store computer readable instructions for implementing one or more embodiments disclosed herein, and other computer readable instructions for implementing an operating system, an application program, and the like. Computer readable instructions stored in storage 1130 may be loaded into memory 1120 for execution by processing unit 1110 .

Computing device 1100 may also include input device(s) 1140 and output device(s) 1150 . Here, the input device(s) 1140 may include, for example, a keyboard, mouse, pen, voice input device, touch input device, infrared camera, video input device, or any other input device, or the like. Further, the output device(s) 1150 may include, for example, one or more displays, speakers, printers, or any other output device, or the like. Also, the computing device 1100 may use an input device or an output device included in another computing device as the input device(s) 1140 or the output device(s) 1150 . Computing device 1100 may also include communication connection(s) 1160 that enable computing device 1100 to communicate with another device (eg, computing device 1300 ). Here, communication connection(s) 1160 may be a modem, network interface card (NIC), integrated network interface, radio frequency transmitter/receiver, infrared port, USB connection, or other for connecting computing device 1100 to another computing device. It may include interfaces. Also, the communication connection(s) 1160 may include a wired connection or a wireless connection. Each component of the above-described computing device 1100 may be connected by various interconnections such as a bus (eg, peripheral component interconnection (PCI), USB, firmware (IEEE 1394), optical bus structure, etc.) and may be interconnected by a network 1200 . As used herein, terms such as “component,” “system,” and the like generally refer to a computer-related entity that is hardware, a combination of hardware and software, software, or software in execution.

As such, those skilled in the art to which the present invention pertains will understand that the present invention may be embodied in other specific forms without changing the technical spirit or essential characteristics thereof. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The scope of the present invention is indicated by the following claims rather than the above detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

100: Mobile community location-based safe driving information sharing system
200: driver mobile
210: communication department
220: storage
230: display unit
240: input unit
250: output unit
260: control instrumentation unit
300: artificial intelligence server
310: information collection unit
320: information analysis unit
330: risk determination unit
340: transmission unit

Claims (12)

After executing the vehicle safe driving monitoring platform, connecting to the vehicle CAN network of the driving vehicle, collecting vehicle driving and control information of the driving vehicle, and measuring and providing information on acceleration and inclination during driving of the driving vehicle; and
After analyzing the driving risk rate of the corresponding vehicle based on the vehicle driving and control information, acceleration and inclination information provided by the driver mobile, the driving risk rate of the corresponding vehicle with the driver's mobile of the adjacent vehicle located within a preset radius from the corresponding vehicle Including an artificial intelligence server that provides
The artificial intelligence server
After collecting road condition information and road condition information in which the driving vehicle is located from an external terminal, steering in the safe driving information according to the road condition information and the road condition information and the location information of the driving vehicle collected for a preset time Comparing and determining information, braking information, and inclination information to calculate a driving risk rate of the driving vehicle,
When the calculated increase in the driving risk rate exceeds a preset value, the dangerous section message or alarm is transmitted to the driver's mobile located at the rear of the driving vehicle by setting the driving vehicle's driving position as a dangerous section from the time when it exceeds the preset value. do,
After correcting the inclination information based on the road condition information, the corrected inclination value is applied to calculate the driving risk rate,
When the risk section is duplicated in a plurality of preceding vehicles, a mobile community location-based providing classifying the risk section into 'caution', 'danger' and 'very dangerous' according to the number of overlapping preceding vehicles Safe driving information sharing system.
According to claim 1,
The driver mobile
Mobile community location-based safe driving information sharing system, characterized in that the real-time tilt information, acceleration information, and GPS information of the driving vehicle are transmitted to the artificial intelligence server.
delete delete According to claim 1,
The artificial intelligence server
Mobile community location-based safe driving information sharing system, characterized in that when a preset position is reached from the dangerous section, a safety warning message is transmitted to the mobile terminal for each preset distance unit.
delete After executing the vehicle safe driving monitoring platform on the driver's mobile, accessing the vehicle CAN network of the driving vehicle to collect vehicle driving and control information of the driving vehicle, and measuring and providing information on acceleration and inclination during driving of the driving vehicle ;
After analyzing the driving risk rate of the corresponding vehicle based on the vehicle driving and control information, acceleration, and inclination information provided by the driver mobile in the AI server, the corresponding vehicle and the driver mobile of an adjacent vehicle located within a preset radius Comprising the step of providing the driving risk rate of the vehicle to the driver mobile,
The step of providing the driver mobile
In the artificial intelligence server, after collecting road condition information and road condition information in which the corresponding driving vehicle is located from an external terminal, the driving vehicle collected for a preset time and safe driving information according to the road condition information and the road condition information calculating a driving risk rate of the driving vehicle by comparing and judging location information, steering information, braking information, and inclination information; and
When the calculated increase in the driving risk rate exceeds a preset value, the dangerous section message or alarm is transmitted to the driver's mobile located at the rear of the driving vehicle by setting the driving vehicle's driving position as a dangerous section from the time when it exceeds the preset value. comprising the steps of
The step of calculating the driving risk of the driving vehicle is
After correcting the inclination information based on the road condition information, the driving risk ratio is calculated using the corrected inclination value. Mobile community location-based safe driving information sharing method comprising the step of classifying the risk section as 'caution', 'danger' and 'very dangerous' according to the.
8. The method of claim 7,
The step of measuring and providing information on acceleration and inclination during driving of the driving vehicle
Mobile community location-based safe driving information sharing method, characterized in that the step of transmitting real-time inclination information, acceleration information, and GPS information of the driving vehicle to the artificial intelligence server.
delete delete 8. The method of claim 7,
The step of transmitting the danger section message or alarm is
When a preset location is reached from the dangerous section, the mobile community location-based safe driving information sharing method comprising the step of transmitting a safety warning message to the mobile terminal for each preset distance unit.
delete

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