KR20220109526A - System and method for predicting danger during driving of vehicle - Google Patents

Abstract

Disclosed are a vehicle driving risk prediction system and a method thereof, capable of preventing a secondary accident by quickly predicting the risk of collision between obstacles (surrounding vehicles, people, etc.) around a vehicle. The vehicle driving risk prediction system according to an embodiment of the present invention, as a vehicle driving risk prediction system for predicting the driving risk of a driving vehicle, comprises: an image acquisition unit configured to sequentially acquire images of objects located around the vehicle; a moving object extraction unit configured to extract moving objects by analyzing the images; a motion vector calculation unit configured to calculate a motion vector profile corresponding to a motion vector according to time for each of the moving objects; an accident risk prediction unit configured to predict an accident risk of the vehicle by an artificial neural network based on the motion vector profile calculated for the moving objects; and a risk notification unit configured to notify a driver of the vehicle of the accident risk when predicting the accident risk.

Description

Vehicle driving risk prediction system and method {System and method for predicting danger during driving of vehicle}

The present invention is for predicting vehicle driving risk, and more specifically, a vehicle driving risk prediction system and method capable of rapidly predicting the risk of a collision accident between obstacles (neighboring cars or people, etc.) around the vehicle to prevent secondary accidents is about

Research is being conducted on a technology to use sensors such as stereo cameras and lidar sensors in vehicles to recognize and analyze surrounding objects and use them to detect obstacles. The prior art is mainly focused on predicting the risk of collision between a vehicle and a surrounding vehicle, and research on the risk prediction of a secondary accident caused by a collision between surrounding vehicles or between a surrounding vehicle and a person is insufficient.

In addition, in order to predict the risk of a traffic accident in real time while driving a vehicle, it is necessary to quickly predict the risk of an accident from data collected through image sensors such as stereo cameras and lidar sensors. It takes a long time to predict the risk of an accident, so it is difficult to apply it to the actual driving environment.

An object of the present invention is to provide a vehicle driving risk prediction system and method capable of preventing a secondary accident by rapidly predicting the risk of a collision accident between obstacles (neighboring cars or people, etc.) around the vehicle.

A vehicle driving risk prediction system according to an embodiment of the present invention is a vehicle driving risk prediction system for predicting a driving risk of a vehicle in motion, and is configured to sequentially acquire images for objects located around the vehicle image acquisition unit; a moving object extraction unit configured to extract moving objects by analyzing the images; a motion vector calculator configured to calculate a motion vector profile corresponding to a motion vector according to time for each of the moving objects; an accident risk prediction unit configured to predict the accident risk of the vehicle by an artificial neural network based on the motion vector profile calculated for the moving objects; and a risk notification unit configured to notify the driver of the vehicle of the risk of an accident when predicting the risk of an accident.

The accident risk prediction unit may include: a differential motion that is a difference value between a first motion vector profile calculated for a first moving object among the moving objects and a second motion vector profile calculated for a second moving object among the moving objects calculate a vector profile; Based on the first motion vector profile, the second motion vector profile, and the differential motion vector profile, a collision probability, a collision expected position, and a collision between the first moving object and the second moving object by the artificial neural network predicting collision risk information including a subsequent movement path; And based on the predicted collision risk information and the driving information of the vehicle, including information related to braking control of the vehicle, and information related to direction change of the vehicle, to determine a countermeasure for preventing an accident of the vehicle can be

The accident risk prediction unit is configured to: collect surrounding environment information including information related to a road on which the vehicle is traveling, and information related to weather at a point in time when the vehicle is driving; processing the images to analyze moving object related information including a type of the first moving object, a type of the second moving object, and estimated weights or vehicle types of the first and second moving objects; processing the images to calculate a first direction profile indicating direction information according to time of the first moving object and a second direction profile indicating direction information according to time of the second moving object; And based on the surrounding environment information, the moving object related information, the first direction profile and the second direction profile, it may be configured to predict the collision risk information by the artificial neural network.

A vehicle driving risk prediction method according to an embodiment of the present invention includes: a vehicle driving risk prediction method for predicting a driving risk of a vehicle in motion, sequentially by an image acquisition unit for objects located around the vehicle acquiring images; extracting moving objects by analyzing the images by the moving object extracting unit; calculating, by a motion vector calculator, a motion vector profile corresponding to a motion vector according to time for each of the moving objects; Predicting the accident risk of the vehicle by an artificial neural network based on the motion vector profile calculated for the moving objects by the accident risk prediction unit; and notifying, by the risk notification unit, the risk of an accident to the driver of the vehicle when the risk of an accident is predicted.

The predicting of the accident risk may include: a difference value between a first motion vector profile calculated for a first moving object among the moving objects and a second motion vector profile calculated for a second moving object among the moving objects calculating a differential motion vector profile that is Based on the first motion vector profile, the second motion vector profile, and the differential motion vector profile, a collision probability, a collision expected position, and a collision between the first moving object and the second moving object by the artificial neural network predicting collision risk information including a subsequent movement path; And based on the predicted collision risk information and driving information of the vehicle, determining a countermeasure for preventing an accident of the vehicle, including information related to braking control of the vehicle, and information related to direction change of the vehicle may include.

The predicting of the accident risk may include: collecting surrounding environment information including information related to a road on which the vehicle is traveling and information related to weather at a point in time when the vehicle is driving while the vehicle is traveling; processing the images to analyze the moving object related information including the type of the first moving object, the type of the second moving object, and estimated weights or vehicle types of the first moving object and the second moving object; ; processing the images to calculate a first direction profile indicating direction information according to time of the first moving object and a second direction profile indicating direction information according to time of the second moving object; and predicting the collision risk information by the artificial neural network based on the surrounding environment information, the moving object related information, the first direction profile, and the second direction profile.

According to an embodiment of the present invention, there is provided a computer-readable recording medium in which a program for executing the vehicle driving risk prediction method is recorded.

According to an embodiment of the present invention, there is provided a vehicle driving risk prediction system and method capable of preventing a secondary accident by rapidly predicting the risk of a collision accident between obstacles (neighboring cars or people, etc.) around the vehicle.

1 is a configuration diagram of a vehicle driving risk prediction system according to an embodiment of the present invention.
2 is a configuration diagram of an accident risk prediction unit constituting a vehicle driving risk prediction system according to an embodiment of the present invention.
3 is a flowchart of a vehicle driving risk prediction method according to an embodiment of the present invention.
4 is a conceptual diagram for explaining a vehicle driving risk prediction method according to an embodiment of the present invention.
5 is a flowchart illustrating step S140 of FIG. 3 according to an embodiment of the present invention.
6 is a flowchart illustrating step S140 of FIG. 3 according to another embodiment of the present invention.
7 is a conceptual diagram for explaining step S147 of FIG. 6 .

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and common knowledge in the technical field to which the present invention pertains It is provided to fully inform those who have the scope of the invention, and the present invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout.

In the present specification, when a part "includes" a certain component, this means that other components may be further included rather than excluding other components unless otherwise stated. As used herein, '~ unit' is a unit for processing at least one function or operation, and may refer to, for example, software, FPGA, or hardware component. A function provided by '~ unit' may be performed separately by a plurality of components, or may be integrated with other additional components. The term '~' in the present specification is not necessarily limited to software or hardware, and may be configured to reside in an addressable storage medium, or may be configured to reproduce one or more processors. Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.

1 is a configuration diagram of a vehicle driving risk prediction system according to an embodiment of the present invention. Referring to FIG. 1 , the vehicle driving risk prediction system 100 according to an embodiment of the present invention is for predicting the driving risk of a driving vehicle and detecting and informing an obstacle for a vehicle blind spot or surrounding situation, and image acquisition unit 110 , moving object extraction unit 120 , motion vector calculation unit 130 , accident risk prediction unit 140 , artificial neural network 150 , artificial neural network learning unit 160 , and risk notification unit 170 ) may include.

The image acquisition unit 110 may be mounted on a vehicle. The image acquisition unit 110 may sequentially acquire images of objects (eg, cars, people, surrounding structures, etc.) located around the driving vehicle. The image acquisition unit 110 may include a device for acquiring a 3D image, for example, a 3D camera such as a stereo camera, a lidar sensor, or the like.

The moving object extracting unit 120 may analyze the images obtained by the image obtaining unit 110 to extract moving objects such as surrounding cars and people. In an embodiment, the moving object extractor 120 may extract moving objects using a deep learning algorithm and/or an algorithm for extracting an object from an image.

The motion vector calculating unit 130 may calculate a motion vector profile corresponding to a motion vector according to time for each of the moving objects extracted by the moving object extracting unit 120 . The motion vector profile may indicate change information of motion vectors sequentially at regular time intervals. The motion vector may include movement direction information and movement speed (speed) information of a moving object moving around the vehicle.

The accident risk prediction unit 140 uses an artificial neural network 150 based on the motion vector profile calculated for the moving objects located around the driving vehicle according to the collision accident between the moving objects. It is possible to predict the accident risk (secondary accident risk) of the vehicle.

The artificial neural network learning unit 160 may learn an artificial neural network for predicting an accident risk of a vehicle by using the learning data. The data for learning of the artificial neural network 150 may include, for example, data corresponding to a collision simulation between vehicles and an actual traffic accident. The artificial neural network learning unit 160 may learn the artificial neural network 150 before and/or after driving the vehicle, based on the collected learning data.

The learning data used for learning of the artificial neural network 150 includes input variables including road-related information, weather-related information, and moving object-related information, whether or not a collision occurs, a collision location, and a moving object after a collision (vehicles that collided). ) may include output variables including the movement path. Such learning data may be collected from information about traffic accidents in the past or may be generated by a simulation device simulating a traffic accident.

In an embodiment, the artificial neural network learning unit 160 is a surrounding environment including road-related information (eg, road slope, road type, speed limit, etc.) and weather-related information (rainy information, snowfall information, snow load, etc.) Information, the type of the moving object (car, truck, bus, motorcycle, person, etc.), the weight of the moving object, or information related to the moving object, including the vehicle model (eg, make, vehicle model, etc.), at the time of the moving object. Input variables including a direction profile corresponding to the direction information along The artificial neural network 150 may be trained by learning the weights between the output variables.

The artificial neural network 150 is a fully connected layer artificial neural network in which an input layer, one or more hidden layers, and an output layer are connected by weights, for example, a convolutional (convolutional) that extracts a feature map by convolutional processing. ) may be implemented as an artificial neural network, but is not limited thereto.

The risk notification unit 170 may notify the driver of the accident risk when the accident risk is predicted by the accident risk prediction unit 140 . The danger notification unit 170 may notify the driver of the risk of an accident through a warning sound, a warning sound, or a warning lamp, or may notify the driver of the risk of an accident through a vehicle navigation system, a display screen, or the like.

Also, the danger notification unit 170 is configured to surround (eg, rearward) the moving object according to the collision probability of the surrounding moving objects (such as surrounding vehicles or people), the expected collision location, and the predicted movement path of the moving objects after the collision. It is possible to automatically control the vehicle to prevent secondary collisions, such as allowing a vehicle in motion to change lanes, or guide the driver to control the vehicle.

2 is a configuration diagram of an accident risk prediction unit constituting a vehicle driving risk prediction system according to an embodiment of the present invention. 1 and 2 , the accident risk prediction unit 140 includes a differential motion vector calculation unit 141 , a surrounding environment information collection unit 142 , a moving object information analysis unit 143 , and a direction profile calculation unit 144 . ), and a collision risk information prediction unit 145 .

The differential motion vector calculator 141 calculates a first motion vector profile calculated for a first moving object among moving objects moving around the vehicle and a second motion vector calculated for a second moving object among the moving objects. A differential motion vector profile corresponding to a difference value between the profiles (a result of a vector difference operation) may be calculated.

The surrounding environment information collection unit 142 provides information related to the road on which the vehicle is traveling (eg, the slope of the road (uphill road, downhill road, flat ground, inclination angle, etc.), tunnel, road type (straight road, curved road, intersection, It is possible to collect surrounding environment information, including information related to the weather at the time the vehicle is driving, such as highways, etc.), speed limit, etc. (rainy weather information, snowfall information, snow load, etc.) at the time the vehicle is driving. The unit 142 may collect surrounding environment information from one or more servers (a meteorological agency server, an electronic map server, etc.) or a navigation system that provide information related to the driving environment of the vehicle.

The moving object information analysis unit 143 processes the images acquired by the image acquisition unit 110 to process the first and second types of moving objects (large car, medium-sized car, slope, truck, bus, motorcycle, person, etc.) , information related to the moving object including estimated weights of the first and second moving objects or vehicle types (eg, manufacturers, vehicle models, etc.) may be analyzed. The moving object information analysis unit 143 may analyze the moving object related information by extracting the size of the moving object, the manufacturer symbol, and the like from the image.

The surrounding environment information and the moving object-related information may be analyzed in advance several seconds or several tens of seconds or more before the risk of collision of the moving objects occurs while the vehicle is driving. Therefore, at the time of collision risk of actual moving objects, only the motion vector profile in the collision risk situation is additionally input to the artificial neural network 150 together with the previously analyzed surrounding environment information and moving object-related information, almost simultaneously with or from the moment. The accident risk prediction result may be output from the artificial neural network 150 in real time within a few seconds.

The direction profile calculating unit 144 processes the images obtained by the image obtaining unit 110 to obtain a first direction profile indicating direction information according to time of the first moving object, and direction information according to time of the second moving object. It is possible to calculate a second direction profile representing .

The collision risk information prediction unit 145 includes the motion vector profiles of the first and second moving objects calculated by the motion vector calculation unit 130 , the differential motion vector profile calculated by the differential motion vector calculation unit 141 , and the surrounding area. The surrounding environment information collected by the environment information collecting unit 142, the moving object related information analyzed by the moving object information analyzing unit 143, and the first and second moving objects calculated by the direction profile calculating unit 144 Based on the direction profile of , collision risk information including a collision probability of a first moving object and a second moving object, an expected collision location, and a movement path after the collision may be predicted by the artificial neural network 150 .

In addition, the collision risk information prediction unit 145 may include vehicle-related information and vehicle direction change-related information in consideration of the predicted braking distance of the vehicle based on the predicted collision risk information (the probability of occurrence of a collision accident) and the driving information of the vehicle. It is possible to determine response measures to prevent accidents of vehicles, including (whether to change lanes and which lanes to change).

The vehicle driving risk prediction system according to an embodiment of the present invention does not perform an operation for simulating a vehicle collision while driving a vehicle, and the collision probability at high speed by an artificial neural network based on information that can be quickly collected through image processing, It is possible to predict the expected location of the collision and the path of movement after the collision, and it is possible to predict the risk of an accident in real time while the vehicle is driving.

3 is a flowchart of a vehicle driving risk prediction method according to an embodiment of the present invention. 4 is a conceptual diagram illustrating a vehicle driving risk prediction method according to an embodiment of the present invention. 1 to 4 , in the method for predicting vehicle driving risk according to an embodiment of the present invention, an object positioned around a driving vehicle 10 by the image acquisition unit 110 first to predict vehicle driving risk Images (eg, a car, a person, a surrounding structure, etc.) may be sequentially acquired ( S110 ).

The moving object extracting unit 120 may analyze the images obtained by the image obtaining unit 110 to extract moving objects 20 and 30 such as nearby cars and people ( S120 ). In an embodiment, the moving object extraction unit 120 may extract the moving objects 20 and 30 using a deep learning algorithm or an image object extraction algorithm.

The motion vector calculating unit 130 may calculate a motion vector profile corresponding to the motion vectors 24 and 34 according to time for the moving objects 20 and 30 extracted by the moving object extracting unit 120, respectively. There is (S130). The motion vectors 24 and 34 may include movement direction information and movement speed information of the moving objects 20 and 30 moving around the vehicle.

In an embodiment, the motion vectors 24, 34 of the moving objects 20, 30 are measured in the vehicle 10 from the motion vectors 22, 32 of the moving objects 20, 30 measured in the vehicle 10, respectively. ) may be a vector obtained by subtracting the motion vector 12 of . The motion vector 12 of the vehicle 10 may be a vector calculated based on the moving direction and the moving speed of the vehicle, which may be collected by various sensors or image sensors of the vehicle 10 .

The accident risk prediction unit 140 uses an artificial neural network 150 based on the motion vector profile calculated for the moving objects located around the driving vehicle according to the collision accident between the moving objects. It is possible to predict the accident risk (secondary accident risk) of the vehicle, and the expected collision location 40 (S140).

The artificial neural network learning unit 160 may learn an artificial neural network for predicting an accident risk of a vehicle by using the learning data. The data for learning of the artificial neural network 150 may include, for example, data corresponding to a collision simulation between vehicles and an actual traffic accident.

The risk notification unit 170 may notify the driver of the accident risk when the accident risk is predicted by the accident risk prediction unit 140 ( S150 ). The danger notification unit 170 may notify the risk of an accident through a warning sound, a warning sound, or a warning light, or may notify the risk of an accident through a vehicle navigation system, a display screen, or the like.

5 is a flowchart illustrating step S140 of FIG. 3 according to an embodiment of the present invention. 1 to 5 , the differential motion vector calculating unit 141 includes a first motion vector profile calculated for a first moving object among moving objects moving around a vehicle, and a second moving object among the moving objects. A differential motion vector profile that is a difference value between the second motion vector profiles calculated for the object may be calculated ( S141 ).

The collision risk information prediction unit 145 is based on the motion vector profiles of the first and second moving objects calculated by the motion vector calculation unit 130 and the differential motion vector profile calculated by the differential motion vector calculation unit 141 . , collision risk information including the collision probability of the first moving object 20 and the second moving object 30, the expected collision location 40, and the movement path after the collision can be predicted by the artificial neural network 150 (S142).

The collision risk information prediction unit 145 includes, based on the predicted collision risk information and the driving information of the vehicle, information related to braking control of the vehicle, and information related to direction change of the vehicle (whether or not to change the lane and the lane to be changed), It is possible to determine a countermeasure for preventing an accident of the vehicle (S143).

6 is a flowchart illustrating step S140 of FIG. 3 according to another embodiment of the present invention. 7 is a conceptual diagram for explaining step S147 of FIG. 6 . 1 to 7 , the surrounding environment information collecting unit 142 provides information related to the road on which the vehicle is traveling (eg, the slope of the road (uphill road, downhill road, flat ground, inclination angle, etc.), tunnel, road type, etc.) (Straight road, curved road, intersection, highway, etc.), speed limit, etc., information about the surrounding environment including information related to the weather at the time the vehicle is driving (rainy information, snowfall information, snow load, etc.) can be collected (S144).

The moving object information analysis unit 143 processes the images acquired by the image acquisition unit 110 to process the first and second types of moving objects (large car, medium-sized car, slope, truck, bus, motorcycle, person, etc.) , moving object related information including estimated weights or vehicle types (eg, manufacturers, vehicle models, etc.) of the first and second moving objects may be analyzed ( S145 ).

The direction profile calculating unit 144 processes the images obtained by the image obtaining unit 110 to obtain a first direction profile indicating direction information according to time of the first moving object, and direction information according to time of the second moving object. It is possible to calculate a second direction profile representing (S146).

The collision risk information prediction unit 145 includes the motion vector profiles of the first and second moving objects calculated by the motion vector calculation unit 130 , the differential motion vector profile calculated by the differential motion vector calculation unit 141 , and the surrounding area. The surrounding environment information collected by the environment information collecting unit 142, the moving object related information analyzed by the moving object information analyzing unit 143, and the first and second moving objects calculated by the direction profile calculating unit 144 Based on the direction profile of , collision risk information including the collision probability of the first moving object and the second moving object, the expected collision location, and the movement path after the collision may be predicted by the artificial neural network 150 ( S147 ).

According to the vehicle driving risk prediction system and method according to the embodiment of the present invention as described above, it is possible to quickly predict the risk of a collision accident between obstacles (neighboring cars or people, etc.) around the vehicle in real time while the vehicle is driving, A secondary accident of a vehicle following a collision between obstacles can be effectively prevented.

At least some of the configurations of the above-described embodiments may be implemented as a hardware component, a software component, and/or a combination of the hardware component and the software component. For example, the apparatus, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). Array), a Programmable Logic Unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, may be implemented using one or more general purpose or special purpose computers.

The processing device may run an operating system and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, the processing device is sometimes described as being used, but one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It will be understood that this may include

For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a Parallel Processor. The software may include a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device.

The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or device, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software.

Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CDROMs and DVDs, and ROM, RAM, and flash memory. Hardware devices specifically configured to store and execute program instructions, such as, etc. are included. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with reference to the limited embodiments and drawings, various modifications and variations are possible by those skilled in the art from the above description. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result. Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

10: vehicle
20, 30: moving object
100: vehicle driving risk prediction system
110: image acquisition unit
120: moving object extraction unit
130: motion vector calculator
140: accident risk prediction unit
141: differential motion vector calculating unit
142: surrounding environment information collection unit
143: moving object information analysis unit
144: direction profile calculation unit
145: collision risk information prediction unit
150: artificial neural network
160: artificial neural network learning unit
170: danger notification unit

Claims (7)

As a vehicle driving risk prediction system for predicting the driving risk of a vehicle in motion,
an image acquisition unit configured to sequentially acquire images for objects located around the vehicle;
a moving object extraction unit configured to extract moving objects by analyzing the images;
a motion vector calculator configured to calculate a motion vector profile corresponding to a motion vector according to time for each of the moving objects;
an accident risk prediction unit configured to predict the accident risk of the vehicle by an artificial neural network based on the motion vector profile calculated for the moving objects; and
A vehicle driving risk prediction system comprising a risk notification unit configured to inform the driver of the accident risk when predicting the accident risk. According to claim 1,
The accident risk prediction unit:
calculating a differential motion vector profile that is a difference value between a first motion vector profile calculated for a first moving object among the moving objects and a second motion vector profile calculated for a second moving object among the moving objects;
Based on the first motion vector profile, the second motion vector profile, and the differential motion vector profile, a collision probability, a collision expected position, and a collision between the first moving object and the second moving object by the artificial neural network predicting collision risk information including a subsequent movement path; and
based on the predicted collision risk information and driving information of the vehicle, including information related to braking control of the vehicle, and information related to direction change of the vehicle, configured to determine a countermeasure for preventing an accident of the vehicle , vehicle driving risk prediction system. 3. The method of claim 2,
The accident risk prediction unit:
collecting surrounding environment information including information related to a road on which the vehicle is traveling, and information related to weather at a time when the vehicle is traveling when the vehicle is traveling;
processing the images to analyze moving object related information including a type of the first moving object, a type of the second moving object, and estimated weights or vehicle types of the first and second moving objects;
processing the images to calculate a first direction profile indicating direction information according to time of the first moving object and a second direction profile indicating direction information according to time of the second moving object; and
and predicting the collision risk information by the artificial neural network based on the surrounding environment information, the moving object related information, the first direction profile, and the second direction profile. As a vehicle driving risk prediction method for predicting the driving risk of a vehicle in motion,
acquiring images sequentially for objects located around the vehicle by an image acquisition unit;
extracting moving objects by analyzing the images by the moving object extracting unit;
calculating, by a motion vector calculator, a motion vector profile corresponding to a motion vector according to time for each of the moving objects;
predicting the accident risk of the vehicle by an artificial neural network based on the motion vector profile calculated for the moving objects by the accident risk prediction unit; and
Comprising the step of notifying, by the risk notification unit, the risk of an accident to the driver of the vehicle when predicting the risk of an accident, the vehicle driving risk prediction method. 5. The method of claim 4,
Predicting the accident risk includes:
calculating a differential motion vector profile that is a difference value between a first motion vector profile calculated for a first moving object among the moving objects and a second motion vector profile calculated for a second moving object among the moving objects ;
Based on the first motion vector profile, the second motion vector profile, and the differential motion vector profile, a collision probability, a collision expected position, and a collision between the first moving object and the second moving object by the artificial neural network predicting collision risk information including a subsequent movement path; and
Based on the predicted collision risk information and driving information of the vehicle, determining a countermeasure for preventing an accident of the vehicle, including information related to braking control of the vehicle, and information related to direction change of the vehicle Including, a method of predicting vehicle driving risk. 6. The method of claim 5,
Predicting the accident risk includes:
collecting surrounding environment information including information related to a road on which the vehicle is traveling and information related to weather at a time when the vehicle is driving when the vehicle is driving;
processing the images to analyze the moving object related information including the type of the first moving object, the type of the second moving object, and estimated weights or vehicle types of the first and second moving objects; ;
processing the images to calculate a first direction profile indicating direction information according to time of the first moving object and a second direction profile indicating direction information according to time of the second moving object; and
and predicting the collision risk information by the artificial neural network based on the surrounding environment information, the moving object-related information, the first direction profile, and the second direction profile. A computer-readable recording medium in which a program for executing the vehicle driving risk prediction method of any one of claims 4 to 6 is recorded.

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