KR102192881B1 - System and method for analyzing service of driver - Google Patents

Abstract

The present invention relates to a driving service analysis system of a driver which calculates each of drowsy driving information and dangerous driving information based on coordinate information, driving information, gyro information, and biometric information when the driver drives a vehicle of a customer, and alarms the corresponding driver when the corresponding drowsy driving or dangerous driving occurs, thereby promoting safe driving; and a method thereof. The driving service analysis system of the driver comprises: a driving history DB wherein coordinate information, driving information, gyro information, and biometric information acquired from an electronic device of a customer are stored; a driving analysis unit calculating each of the drowsy driving information and dangerous driving information based on the coordinate information, driving information, gyro information, and biometric information stored in the driving history DB; and an alarm processing unit processing an alarm so that a corresponding driver recognizes when the drowsy driving or the dangerous driving occurs.

Description

Driver's operation service analysis system and its method {SYSTEM AND METHOD FOR ANALYZING SERVICE OF DRIVER}

The present invention relates to a driving service analysis system of a driver and a method thereof, and more particularly, drowsy driving information and dangerous driving information based on coordinate information, driving information, gyro information and biometric information when the driver drives a customer's vehicle. Each is calculated, and when a corresponding drowsy driving or dangerous driving occurs, an alarm is processed to the corresponding driver, and thus a driving service analysis system and a method of the driver for promoting safe driving.

Recently, such as a proxy driving service or a call taxi, when a customer calls a driver through a telephone or the like, a specific driver is matched and a service that provides a proxy driving service or a taxi service to the customer is actively used.

In addition, in recent years, as various smart devices such as smart phones and tablet PCs are popular, a service that assists customers so that they can easily call a driver using their own terminal is spreading.

In the service to assist the driver's call using the customer's terminal, when a predetermined application for calling the driver is installed on the customer's terminal, and the customer executes the application and presses a button for calling the driver, When the customer's terminal transmits a driver call request signal to a predetermined management server, and the management server uploads a driver recruitment message corresponding to the driver call request signal to a driver recruitment web page, a plurality of Drivers connect to the management server using their own driver's terminal, view the driver recruitment message uploaded on the webpage, and transmit a matching request message for the customer to the management server. , The management server may be operated in a form of matching the customer and the driver with each other.

This driver matching service has a very convenient service characteristic in that the customer can call the driver anytime, anywhere using his terminal, and the driver can match the customer he wants to provide driving service. have.

However, the existing driver matching service is often operated to the extent that a specific driver is matched simply in response to a customer's call request, so if the matched driver is a driver who does not have insurance, when a traffic accident occurs. There is a strong possibility that customers will not receive compensation and suffer great damage.

Therefore, as a customer transmits a call request to a predetermined driver using his or her terminal, when there is a driver who transmits a matching request for the call request, the driver is normally insured After confirming that is correct, a study on a technique that can minimize damage to customers is required by introducing a system that matches the driver with the customer when the driver is normally insured.

0001) Korean Patent Registration No. 10-1702469 (2017. 01. 26.) (Device and method for supporting driver matching based on insurance information inquiry) Republic of Korea Patent Publication No. 2015-066985 (2015. 06. 17.) (Agent operation brokerage system and method) Republic of Korea Patent Registration No. 10-0831397 (2008. 05. 15.) (Agent driving service system)

Accordingly, the technical problem of the present invention is focused on this point, and an object of the present invention is to provide information on drowsy driving and dangerous driving based on coordinate information, driving information, gyro information, and biometric information when a driver drives a customer's vehicle. It is to provide a driving service analysis system implemented to promote safe driving by calculating each and processing an alarm to the corresponding driver when a corresponding drowsy driving or dangerous driving occurs.

Another object of the present invention is to provide a method for analyzing a driving service of a driver using the system for analyzing the driving service of the driver.

In order to realize the object of the present invention, a driving service analysis system for a driver according to an embodiment includes: a driving history DB in which coordinate information, driving information, gyro information, and biometric information obtained from the driver's electronic device are stored; A driving analysis unit for calculating drowsy driving information and dangerous driving information, respectively, based on coordinate information, driving information, gyro information, and biometric information stored in the driving history DB; And an alarm processing unit configured to perform an alarm to be recognized by a corresponding driver when the drowsy driving or the dangerous driving occurs.

In one embodiment, the driving service analysis system of the driver may further include a driving quality DB for storing the drowsy driving information and the dangerous driving information calculated by the driving analysis unit for each driver.

In one embodiment, the coordinate information and the driving information may be obtained from a driver's smartphone, and the gyro information and the biometric information may be obtained from a driver's wearable device.

In one embodiment, the driving analysis unit includes a drowsiness driving detection module that calculates the drowsiness driving information based on at least one of a heart rate, an electrocardiogram, body temperature, brain waves, a field of view, an eye blink, a head movement, and an expression as a drowsy variable. can do.

In an embodiment, the drowsy driving detection module may determine the drowsy driving state of the driver based on a sum value obtained by summing state data of the drowsiness variables.

In one embodiment, the driving analysis unit calculates the dangerous driving information based on at least one of speeding, long-term speeding, sudden speeding, sudden braking, sudden start, sudden stop, overtaking, rotation, sudden deceleration, and course change as a risk variable. It may include a dangerous operation detection module.

In one embodiment, the dangerous driving detection module may determine the dangerous driving state of the operator based on a sum value obtained by summing state data of the dangerous variables.

In one embodiment, the driving information may include location information of a vehicle driven by a driver, which is tracked and stored in real time based on GPS information; Vehicle motion information including movement information on the x, y, and z axes calculated based on the gyro information, and acceleration, deceleration, inclination, and vibration; Body movement information including movements of a driver's head, neck, waist, and hands calculated based on the gyro information; And internal and external state information of the driver including heart rate, electrocardiogram, body temperature, brain waves, and sweat calculated based on a vital signal.

In order to realize another object of the present invention, a method for analyzing a driving service of a driver according to an embodiment includes obtaining coordinate information, driving information, gyro information, and biometric information from an electronic device of the driver and storing it in a driving history DB. ; Calculating drowsy driving information and dangerous driving information based on the coordinate information, the driving information, the gyro information, and the biometric information; And processing an alarm so that a corresponding driver recognizes when the drowsy driving or the dangerous driving occurs.

In one embodiment, the drowsiness driving information may be calculated by defining at least one of a heart rate, an electrocardiogram, body temperature, brain waves, a field of view, a blink, a head movement, and an expression input as the biometric information as a sleepiness variable. Here, step (ii) includes: (ii-1) dividing each of the drowsiness variables into five stages and mapping them to a drowsiness conversion value; (ii-2) obtaining drowsy driving discrimination data by arithmetic mean processing the drowsiness conversion values of each drowsiness variable; And (ii-3) determining the intensity of occurrence of the drowsy driving based on the drowsy driving determination data.

In one embodiment, the dangerous driving information is defined as a risk variable by defining at least one of speeding, long-term speeding, rapid speeding, sudden braking, sudden start, sudden stop, overtaking, rotation, rapid deceleration, and course change input as the driving information. Can be calculated. Here, step (ii) includes the steps of (ii-4) dividing each of the risk variables into five steps and mapping them to risk conversion values; (ii-5) obtaining dangerous driving discrimination data by arithmetic mean processing the risk conversion values of each risk variable; And (ii-6) determining the intensity of occurrence of dangerous driving based on the dangerous driving determination data.

According to the driver's driving service analysis system and its method, when the driver drives the customer's vehicle, each of the drowsy driving information and the dangerous driving information is calculated based on coordinate information, driving information, gyro information, and biometric information, and corresponding drowsiness. When driving or dangerous driving occurs, it is possible to promote safe driving by alarming the corresponding driver.

1 is a block diagram illustrating a system for analyzing a driving service of a driver according to an embodiment of the present invention.
FIG. 2 is a configuration diagram illustrating an electronic device employed in a wearable device of a driver in the driving service analysis system of a driver shown in FIG. 1.
3 is a configuration diagram illustrating a driving analysis unit shown in FIG. 1.
4 is a flowchart illustrating a method for analyzing a driving service of a driver according to an embodiment of the present invention.
FIG. 5 is a flowchart illustrating steps of calculating the drowsy driving information and dangerous driving information shown in FIG. 4.

Hereinafter, various embodiments of the present document will be described with reference to the accompanying drawings. The examples and terms used therein are not intended to limit the technology described in this document to a specific embodiment, and should be understood to include various modifications, equivalents, and/or substitutes of the corresponding embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar elements. Singular expressions may include plural expressions unless the context clearly indicates otherwise. In this document, expressions such as "A or B" or "at least one of A and/or B" may include all possible combinations of items listed together. Expressions such as "first," "second," "first," or "second," can modify the corresponding elements regardless of their order or importance, and to distinguish one element from another It is used only and does not limit the components. When it is mentioned that a certain (eg, first) component is “(functionally or communicatively) connected” or “connected” to another (eg, second) component, the certain component is It may be directly connected to the component, or may be connected through another component (eg, a third component).

In this document, "configured to (configured to)" means "suitable for," "having the ability to," "to," or changed to, depending on the situation, for example, in hardware or software. ," "made to," "can do," or "designed to" can be used interchangeably. In some situations, the expression "a device configured to" may mean that the device "can" along with other devices or parts. For example, the phrase “a processor configured (or configured) to perform A, B, and C” means a dedicated processor (eg, an embedded processor) for performing the operation, or by executing one or more software programs stored in a memory device. , May mean a general-purpose processor (eg, CPU or application processor) capable of performing corresponding operations.

Electronic devices according to various embodiments of the present document include, for example, smart phones, tablet PCs, mobile phones, video phones, e-book readers, desktop PCs, laptop PCs, netbook computers, workstations, servers, PDAs, and PMPs. It may include at least one of (portable multimedia player), MP3 player, medical device, camera, or wearable device. Wearable devices include accessory types (e.g. watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or headmounted-devices (HMD)), fabric or clothing integrals (e.g. electronic clothing), and body attachments. It may include at least one of a type (eg, a skin pad or a tattoo), or a bio-implantable circuit In some embodiments, the electronic device is, for example, on-board diagnostics, a television, a DVD (digital video disk) Player, audio, refrigerator, air conditioner, vacuum cleaner, oven, microwave oven, washing machine, air purifier, set-top box, home automation control panel, security control panel, media box (e.g. Samsung HomeSyncTM, Apple TVTM) , Or Google TVTM), a game console (eg, Xbox TM, PlayStation TM), an electronic dictionary, an electronic key, a camcorder, or an electronic frame.

In another embodiment, the electronic device includes various medical devices (e.g., various portable medical measuring devices (blood glucose meter, heart rate meter, blood pressure meter, or body temperature meter, etc.), magnetic resonance angiography (MRA), magnetic resonance imaging (MRI), CT (computed tomography), camera, or ultrasound), navigation device, global navigation satellite system (GNSS), event data recorder (EDR), flight data recorder (FDR), automobile infotainment device, marine electronic equipment (E.g., navigation devices for ships, gyro compasses, etc.), avionics, security devices, vehicle head units, industrial or home robots, drones, ATMs of financial institutions, POS (points) of stores of sales), or IoT devices (eg, light bulbs, various sensors, sprinkler devices, fire alarms, temperature controllers, street lights, toasters, exercise equipment, hot water tanks, heaters, boilers, etc.). According to some embodiments, the electronic device is a piece of furniture, a building/structure or a vehicle, an electronic board, an electronic signature receiving device, a projector, or various measuring devices (e.g., water, electricity, Gas, or a radio wave measuring device, etc.). In various embodiments, the electronic device may be flexible or may be a combination of two or more of the aforementioned various devices. The electronic device according to the embodiment of the present document is not limited to the above-described devices. In this document, the term user may refer to a person using an electronic device or a device (eg, an artificial intelligence electronic device) using an electronic device.

1 is a block diagram illustrating a system for analyzing a driving service of a driver according to an embodiment of the present invention.

Referring to FIG. 1, a driving service analysis system 300 of a driver includes a driving history DB 310, a driving analysis unit 320, a driving quality DB 330, and an alarm processing unit 340, and the customer's vehicle ( 110) analyzes the driving service of the driver, alarms drowsy driving or alarms dangerous driving according to the analysis result, and stores and manages the analyzed driving service for each driver. In this document, a driver is a person who is acting as an agent for driving a customer who owns a vehicle, and has to pay a fixed cost for hiring a driver, to handle other tasks while driving, or to park a vehicle when entering or leaving a foreign country. When you go to a department store or mart where anxiety or traffic is complicated, you can provide a service for individual customers who go on busy work and business trips.

The driving history DB 310 stores coordinate information, driving information, gyro information, and biometric information obtained from the electronic device of the driver who operates the customer's vehicle 110. The coordinate information and the driving information may be obtained from a smartphone held by the driver, and the gyro information and the biometric information may be obtained from a wearable device of the driver. In particular, an electronic device equipped with a sensor capable of measuring biometric information of a driver may be disposed in the wearable device. The driver can measure information related to the body using the electronic device and can grasp the state of his or her body.

The driving analysis unit 320 calculates drowsy driving information and dangerous driving information based on coordinate information, driving information, gyro information, and biometric information stored in the driving history DB 310 or provided in real time.

The driving quality DB 330 stores the drowsy driving information and the dangerous driving information calculated by the driving analysis unit 320 for each driver.

The alarm processing unit 340 processes an alarm so that a corresponding operator can recognize when a drowsy driving or dangerous driving occurs.

FIG. 2 is a block diagram illustrating an electronic device employed in a wearable device of a driver in the driving service analysis system of a driver shown in FIG. 1.

Referring to FIG. 2, the electronic device 101 in the network environment 200 is described. The electronic device 101 may include a bus 110, a processor 120, a memory 130, an input/output interface 150, a display 160, and a communication interface 170. In some embodiments, the electronic device 101 may omit at least one of the constituent elements or may additionally include other constituent elements. The bus 110 may include a circuit that connects the components 110 to 170 to each other and transmits communication (eg, control message or data) between the components. The processor 120 may include one or more of a central processing unit, an application processor, and a communication processor (CP). The processor 120 may control at least one other component of the electronic device 101 and/or perform an operation or data processing related to communication.

The memory 130 may include volatile and/or nonvolatile memory. The memory 130 may store commands or data related to at least one other component of the electronic device 101. The memory 130 may store software and/or a program 140. The program 140 may include a kernel 141, middleware 143, an application programming interface (API) 145, and/or an application program (or “application”) 147, and the like. At least a portion of the kernel 141, the middleware 143, or the API 145 may be referred to as an operating system. The kernel 141 is a system resource (eg, bus 110) used to execute an operation or function implemented in other programs (eg, middleware 143, API 145, or application program 147). , The processor 120, or the memory 130, etc.) can be controlled or managed. In addition, the kernel 141 may provide an interface for controlling or managing system resources by accessing individual components of the electronic device 101 from the middleware 143, the API 145, or the application program 147. have.

The middleware 143 may serve as an intermediary so that the API 145 or the application program 147 communicates with the kernel 141 to exchange data. In addition, the middleware 143 may process one or more job requests received from the application program 147 according to priority. For example, the middleware 143 can use the system resources of the electronic device 101 (for example, the bus 110, the processor 120, or the memory 130) for at least one of the application programs 147. Priority can be assigned and the one or more work requests can be processed. The API 145 is an interface for the application 147 to control functions provided by the kernel 141 or the middleware 143, and at least one interface or function for file control, window control, image processing, or character control. (Example: command) can be included. The input/output interface 150 transmits commands or data input from a user or another external device to other component(s) of the electronic device 101, or is received from other component(s) of the electronic device 101. Commands or data can be output to users or other external devices.

The display 160 may include a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, or a microelectromechanical system (MEMS) display, or an electronic paper display. The display 160 may display various types of content (eg, text, images, videos, icons, and/or symbols) to the user. The display 160 may include a touch screen and may receive a touch, gesture, proximity, or hovering input using an electronic pen or a part of a user's body. The communication interface 170 may establish communication between the electronic device 101 and an external device. For example, the communication interface 170 may be connected to the network 200 through wireless communication or wired communication to communicate with an external device.

Wireless communication is LTE, LTE-A (LTE Advance), CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), or GSM (Global System for Mobile Communications) Cellular communication using at least one of, for example, may be included. According to an embodiment, wireless communication is, WiFi (wireless fidelity), LiFi (light fidelity), Bluetooth, Bluetooth low power (BLE), Zigbee, near field communication (NFC), magnetic secure transmission (Magnetic Secure Transmission). , Radio frequency (RF), and may include at least one of the body area network (BAN). According to an embodiment, wireless communication may include GNSS. The GNSS may be a Global Positioning System (GPS), a Global Navigation Satellite System (Glonass), a Beidou Navigation Satellite System (hereinafter “Beidou”), or a Galileo, the European global satellite-based navigation system. Wired communication may include at least one of universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), power line communication, or plain old telephone service (POTS).

The network 200 may include at least one of a telecommunication network computer network (eg, LAN or WAN), the Internet, or a telephone network.

3 is a configuration diagram illustrating the operation analysis unit 320 shown in FIG. 1.

1 and 3, the driving analysis unit 320 includes a drowsy driving detection module 322 and a dangerous driving detection module 324, and coordinate information stored in the driving history DB 310 or provided in real time , Drowsy driving information and dangerous driving information are calculated based on driving information, gyro information, and biometric information, respectively. The term "module" used in this document includes a unit composed of hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic blocks, parts, or circuits. The "module" may be an integrally configured part or a minimum unit or a part of which performs one or more functions. "Modules" can be implemented mechanically or electronically, for example, known or future development, application-specific integrated circuit (ASIC) chips, field-programmable gate arrays (FPGAs), or It may include a programmable logic device.

The sleepy driving detection module 322 calculates sleep driving information based on at least one of heart rate, electrocardiogram, body temperature, brain wave, field of view focus, eye blink, head movement, and facial expression as a sleepy variable. The drowsy driving detection module 322 may determine the drowsy driving state based on a sum value obtained by summing the state data of the drowsiness variables.

Specifically, in order to determine the drowsy driving, the drowsy driving detection module 322 divides the drowsiness variables of each of the heart rate, electrocardiogram, body temperature, brain waves, field of view, blinking, head movement and facial expression into 5 steps, and is based on the drowsiness conversion value. Map it. In other words, the severity is the drowsiness conversion value 5, the boundary is the drowsiness conversion value 4, the attention is the drowsiness conversion value 3, the attention is the drowsiness conversion value 2, and the normal is the drowsiness conversion value 1, respectively. This is shown in Table 1 below.

[Table 1]

The drowsy driving detection module 322 calculates drowsy driving discrimination data by arithmetic average of the drowsiness conversion values of the drowsiness variables stored in the driving history DB 310 or the drowsiness variables provided in real time as shown in Equation 1 below. Here, it is preferable that the drowsiness conversion value of each drowsiness variable is detected at the same time.

[Equation 1]

Based on the drowsy driving discrimination data of Equation 1, the intensity of the drowsy driving can be determined. For example, if the drowsy driving discrimination data is 8 or less, driving is normal, if the drowsy driving discrimination data is 9 to 16, interest in drowsy driving, if the drowsy driving discrimination data is 17 to 24, pay attention to drowsy driving, and if the drowsy driving discrimination data is 25 to 32 If the drowsy driving boundary and the drowsy driving discrimination data are 33 to 40, the drowsy driving can be determined as serious.

In the present embodiment, although the intensity of occurrence of the drowsy driving is determined linearly in the drowsy driving determination data, the intensity of occurrence of the drowsy driving may be determined nonlinearly in the drowsy driving determination data.

Based on the drowsy driving discrimination data determined by the drowsy driving detection module 322, the alarm processing unit 340 (shown in FIG. 1) alarms the driver of the drowsy driving, and the drowsy driving determination data is the driving quality DB 330 ) (Shown in Fig. 1) is stored for each operator.

Meanwhile, the dangerous driving detection module 324 calculates dangerous driving information based on at least one of speeding, long-term speeding, rapid speeding, sudden braking, sudden start, sudden stop, overtaking, rotation, sudden deceleration, and course change as a risk variable. . The dangerous driving detection module 324 may determine a dangerous driving state based on a sum value obtained by summing the state data of the dangerous variables.

Specifically, the dangerous driving detection module 324 is a risk variable of speeding, long-term speeding, rapid speeding, sudden braking, sudden start, sudden stop, overtaking, rotation, rapid deceleration, and course change in 5 steps to determine dangerous driving. Classify and map to risk conversion value. That is, severity is a risk conversion value of 5, boundary is a risk conversion value of 4, caution is a risk conversion value of 3, interest is a risk conversion value of 2, and normal is a risk conversion value of 1, respectively. This is shown in Table 2 below.

[Table 2]

The dangerous driving detection module 324 calculates dangerous driving discrimination data by arithmetic-averaging the conversion values of each of the dangerous variables stored in the driving history DB 310 or the dangerous variables provided in real time as shown in Equation 2 below. Here, it is preferable that the risk conversion value of each risk variable is detected at the same time.

[Equation 2]

Based on the data for determining dangerous driving in Equation 2, the intensity of occurrence of dangerous driving can be determined. For example, if the dangerous driving discrimination data is 10 or less, driving is normal, if the dangerous driving discrimination data is 11 to 20, you are interested in dangerous driving, if the dangerous driving discrimination data is 21 to 30, the dangerous driving discrimination data is 31 to 40. If the dangerous driving boundary and dangerous driving determination data are 41 to 50, it can be determined as dangerous driving serious.

In the present embodiment, the intensity of occurrence of dangerous driving is determined linearly in the dangerous driving determination data, but the intensity of occurrence of dangerous driving may be determined nonlinearly in the dangerous driving determination data.

Based on the dangerous driving determination data determined by the dangerous driving detection module 324, the alarm processing unit 340 (shown in FIG. 1) alarms the dangerous driving to the operator, and the dangerous driving determination data is a driving quality DB 330 ) (Shown in Fig. 1) is stored for each operator.

4 is a flowchart illustrating a method for analyzing a driving service of a driver according to an embodiment of the present invention.

1 to 4, coordinate information, driving information, gyro information, and biometric information are acquired from the driver's electronic device and stored in the driving history DB 310 (step S110).

The driving analysis unit 320 is based on coordinate information, driving information, gyro information, and biometric information acquired in real time or on the basis of coordinate information stored in the driving history DB 310, driving information, gyro information, and biometric information. Information and dangerous driving information are calculated (step S120). The drowsy driving information may be calculated by the drowsy driving detection module 322 (shown in FIG. 3), and the dangerous driving information may be calculated by the dangerous driving detection module 324 (shown in FIG. 3).

FIG. 5 is a flowchart illustrating steps of calculating the drowsy driving information and dangerous driving information shown in FIG. 4.

Referring to FIG. 5, each of the drowsiness variables is divided into five steps and mapped to a drowsiness conversion value (step S210). For example, as a drowsiness variable, the heart rate is divided into 5 stages, and the heart rate acquired in real time or the heart rate stored in the driving history DB 310 is checked among the 5 stages and mapped to the drowsiness conversion value corresponding to the stage. do. The drowsiness conversion value corresponding to the heart rate may be mapped to any one of 5 corresponding to severity, 4 corresponding to boundary, 3 corresponding to attention, 2 corresponding to interest, and 1 corresponding to normal. In this way, as drowsiness variables, ECG, body temperature, EEG, visual field focus, blinking, head movement and facial expression are mapped to the drowsiness conversion value.

The drowsiness conversion values of each of the drowsiness variables are arithmetically averaged as shown in Equation 1 above to obtain drowsy driving discrimination data (step S220).

Then, the intensity of occurrence of the drowsy driving is determined based on the drowsy driving determination data (step S230). For example, if the drowsy driving discrimination data is 8 or less, driving is normal, if the drowsy driving discrimination data is 9 to 16, interest in drowsy driving, if the drowsy driving discrimination data is 17 to 24, pay attention to drowsy driving, and if the drowsy driving discrimination data is 25 to 32 If the drowsy driving boundary and the drowsy driving discrimination data are 33 to 40, the drowsy driving can be determined as serious.

Each of the risk variables is divided into five steps and mapped to a risk conversion value (step S240). For example, as a risk variable, speeding is classified into five stages, and the risk conversion value corresponding to the corresponding stage by checking which stage of the five stages is the speeding data acquired in real time or the speeding data stored in the driving history DB 310. Maps to The risk conversion value corresponding to speeding may be mapped to any one of 5 corresponding to severity, 4 corresponding to boundary, 3 corresponding to attention, 2 corresponding to interest, and 1 corresponding to normal. In this way, long-term speeding, rapid speeding, sudden braking, sudden start, sudden stop, overtaking, turning, sudden deceleration, and course change as risk variables are mapped to risk conversion values.

Risk conversion values of each of the risk variables are arithmetically averaged as shown in Equation 2 above to obtain dangerous driving discrimination data (step S250).

Subsequently, the intensity of occurrence of dangerous driving is determined based on the above-described dangerous driving determination data (step S260). For example, if the dangerous driving discrimination data is 10 or less, driving is normal, if the dangerous driving discrimination data is 11 to 20, you are interested in dangerous driving, if the dangerous driving discrimination data is 21 to 30, the dangerous driving discrimination data is 31 to 40. If the dangerous driving boundary and dangerous driving determination data are 41 to 50, it can be determined as dangerous driving serious.

Referring again to FIG. 4, following step S120, the drowsy driving detection module 322 is a sleepy operation based on at least one of heart rate, electrocardiogram, body temperature, brain waves, field of view, blinking, head movement, and facial expression as a drowsy variable. It is checked whether it has occurred (step S130). For example, the drowsy driving detection module 322 may determine the drowsy driving state based on a sum value obtained by summing the state data of the drowsiness variables.

If it is checked that the drowsiness operation occurs in step S130, the alarm processing unit 340 processes the alarm so that the corresponding driver recognizes the drowsiness operation (step S140).

The drowsy driving detection module 322 updates the driving quality information of the corresponding driver, which has been alarmed in response to the drowsy driving, to the driving quality DB 330 (step S150).

After checking that the drowsy driving has not occurred in step S130 or performing step S150, the dangerous driving detection module 324 of the driving analysis unit 320 is a dangerous variable as speeding, long-term speeding, rapid speeding, sudden braking, sudden start, sudden stop. , Overtaking, turning, rapid deceleration, and checking whether or not dangerous driving has occurred based on at least one of a course change (step S160). For example, the dangerous driving detection module 324 may determine the dangerous driving state based on a sum value obtained by summing the state data of the dangerous variables.

If it is checked that the dangerous operation does not occur in step S160, the feedback is fed back to step S110, and if it is checked that the dangerous operation occurs in step S160, the alarm processing unit 340 processes an alarm so that the operator recognizes the dangerous operation (step S160). .

Following step S160, the driving quality information of the corresponding driver, which has been subjected to a dangerous driving alarm, is updated to the driving quality DB 330 (step S170), and then fed back to step S110.

As described above, according to the present invention, when a driver drives a customer's vehicle, each of the drowsy driving information and the dangerous driving information is calculated based on coordinate information, driving information, gyro information, and biometric information, and corresponding drowsy driving Alternatively, when a dangerous driving occurs, by processing an alarm to the operator, safe driving can be promoted.

Although the above has been described with reference to examples, it is understood that those skilled in the art may variously modify and change the present invention without departing from the spirit and scope of the present invention described in the following claims. You can understand.

300: operation service analysis system 310: operation history DB
320: operation analysis unit 322: drowsy operation detection module
324: dangerous operation detection module 330: operation quality DB
340: alarm processing unit

Claims (13)

A driving history DB in which coordinate information, driving information, gyro information and biometric information obtained from the driver's electronic device are stored;
A driving analysis unit for calculating drowsy driving information and dangerous driving information based on coordinate information, driving information, gyro information, and biometric information stored in the driving history DB; And
When the drowsy driving or the dangerous driving occurs, an alarm processing unit for processing an alarm to be recognized by a corresponding driver, wherein the driving analysis unit,
In order to determine drowsy driving, the drowsiness variables of heart rate, electrocardiogram, body temperature, brain waves, visual field focus, eye blink, head movement and facial expression are divided into 5 steps and mapped to the drowsiness conversion value, and the drowsiness variables stored in the driving history DB Or a drowsiness driving detection module for calculating drowsiness driving determination data by arithmetic average of each drowsiness conversion value provided in real time, and determining the intensity of drowsiness driving based on the drowsiness driving determination data; And
In order to determine dangerous driving, the risk variables of speeding, long speeding, rapid speeding, sudden braking, sudden start, sudden stop, overtaking, turn, sudden deceleration and course change are divided into five stages and mapped to the risk conversion value, and the above operation A dangerous driving detection module that calculates dangerous driving discrimination data by arithmetic average of the risk conversion values of the risk variables stored in the history DB or the risk variables provided in real time, and determines the intensity of dangerous driving based on the dangerous driving discrimination data. Include,
The drowsiness conversion value corresponding to each of the drowsiness variables is mapped to any one of 5 corresponding to severity, 4 corresponding to boundary, 3 corresponding to attention, 2 corresponding to interest, and 1 corresponding to normal, and the drowsy driving If the discrimination data is 8 or less, driving is normal, if the drowsy driving discrimination data is 9 to 16, interest in drowsy driving, if the drowsy driving discrimination data is 17 to 24, pay attention to drowsy driving, and if the drowsy driving discrimination data is 25 to 32, the sleepy driving boundary , If the drowsy driving determination data is 33 to 40, it is determined as drowsy driving serious,
The risk conversion value corresponding to each of the risk variables is mapped to any one of 5 corresponding to severity, 4 corresponding to boundary, 3 corresponding to attention, 2 corresponding to interest, and 1 corresponding to normal, and the dangerous driving If the discrimination data is less than 10, driving is normal, if the dangerous driving discrimination data is 11 to 20, interest in dangerous driving, if the dangerous driving discrimination data is 21 to 30, beware of dangerous driving, and if the dangerous driving discrimination data is 31 to 40, the dangerous driving boundary , If the dangerous driving determination data is 41 to 50, the driving service analysis system of the driver, characterized in that the dangerous driving is determined to be serious. The system of claim 1, further comprising a driving quality DB for storing the drowsy driving information and dangerous driving information calculated by the driving analysis unit for each driver. The system of claim 1, wherein the coordinate information and the driving information are obtained from a smartphone of a driver, and the gyro information and the biometric information are obtained from a wearable device of the driver. delete delete delete delete The method of claim 1, wherein the operation information,
Location information of a vehicle driven by a driver that is tracked and stored in real time based on GPS information;
Vehicle motion information including movement information on the x, y, and z axes calculated based on the gyro information, and acceleration, deceleration, inclination, and vibration;
Body movement information including movements of a driver's head, neck, waist, and hands calculated based on the gyro information; And
A driving service analysis system for a driver, characterized in that it includes internal and external status information of a driver including heart rate, electrocardiogram, body temperature, brain waves, and sweat calculated based on a vital signal. (i) obtaining coordinate information, driving information, gyro information, and biometric information from the driver's electronic device and storing it in a driving history DB;
(ii) calculating drowsy driving information and dangerous driving information based on the coordinate information, the driving information, the gyro information, and the biometric information;
(iii) processing an alarm so that the driver recognizes when the drowsy driving occurs;
(iv) updating the driving quality information of the corresponding driver for which the drowsy driving alarm has been processed;
(v) processing an alarm so that a corresponding operator recognizes when the dangerous operation occurs; And
(vi) including the step of updating the driving quality information of the corresponding operator that has been processed with a dangerous driving alarm, step (ii),
(ii-1) dividing the drowsiness variables of heart rate, electrocardiogram, body temperature, brain wave, visual field focus, eye blink, head movement, and facial expression into 5 stages to determine drowsy driving and mapping them to the drowsiness conversion value;
(ii-2) obtaining drowsy driving discrimination data by arithmetic mean processing the drowsiness conversion values of each of the drowsiness variables;
(ii-3) determining the intensity of occurrence of the drowsy driving based on the drowsy driving determination data;
(ii-4) To determine dangerous driving, the risk variables for speeding, long-term speeding, rapid speeding, sudden braking, sudden start, sudden stop, overtaking, rotation, sudden deceleration, and course change are classified into five stages and converted into risk conversion values. Mapping;
(ii-5) obtaining dangerous driving discrimination data by arithmetic mean processing the risk conversion values of each of the risk variables; And
(ii-6) including the step of determining the intensity of occurrence of dangerous driving based on the dangerous driving determination data,
The drowsiness conversion value corresponding to each of the drowsiness variables is mapped to any one of 5 corresponding to severity, 4 corresponding to boundary, 3 corresponding to attention, 2 corresponding to interest, and 1 corresponding to normal, and the drowsy driving If the discrimination data is 8 or less, driving is normal, if the drowsy driving discrimination data is 9 to 16, interest in drowsy driving, if the drowsy driving discrimination data is 17 to 24, pay attention to drowsy driving, and if the drowsy driving discrimination data is 25 to 32, the sleepy driving boundary , If the drowsy driving determination data is 33 to 40, it is determined as drowsy driving serious,
The risk conversion value corresponding to each of the risk variables is mapped to any one of 5 corresponding to severity, 4 corresponding to boundary, 3 corresponding to attention, 2 corresponding to interest, and 1 corresponding to normal, and the dangerous driving If the discrimination data is less than 10, driving is normal, if the dangerous driving discrimination data is 11 to 20, interest in dangerous driving, if the dangerous driving discrimination data is 21 to 30, beware of dangerous driving, and if the dangerous driving discrimination data is 31 to 40, the dangerous driving boundary , If the dangerous driving determination data is 41 to 50, the driving service analysis method of the driver, characterized in that the dangerous driving is determined to be serious.
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