JP2020194263A - Accident analysis device, accident analysis method, and program - Google Patents

Abstract

To provide a technique of making it possible to quickly perform matching between an accident state and a previous accident case example.SOLUTION: An accident analysis device comprises: an acquisition unit for acquiring vehicle data measured by a sensor provided in an accident vehicle and image data photographed by a camera provided in the accident vehicle; an analysis unit for analyzing a state of an accident caused by the accident vehicle based on the acquired vehicle data and image data; and a retrieval unit for retrieving information on a previous accident case example corresponding to the state of the accident caused by the accident vehicle by comparing the state of the accident caused by the accident vehicle analyzed by the analysis unit and accident case example data that associates the state of the accident with the information on the previous accident case example.SELECTED DRAWING: Figure 2

Description

The present invention relates to an accident analyzer, an accident analysis method and a program.

Currently, an in-vehicle camera called a drive recorder is provided that can constantly record, for example, an image of a traveling direction or the like while the automobile is traveling. By attaching a drive recorder to the car in which the driver drives, the driver can use it for explaining the situation at the time of an accident and estimating the cause of the accident.

JP-A-2018-65680

When a traffic accident occurs, the insurance company accurately grasps the accident situation by organizing the accident situation, and calculates the error rate by collating the accident situation with the past accident cases. Conventionally, these operations have been performed manually and have been laborious.

Therefore, an object of the present invention is to provide a technique capable of promptly collating an accident situation with a past accident case.

The accident analyzer according to one aspect of the present invention includes an acquisition unit that acquires vehicle data measured by a sensor included in the accident vehicle and video data taken by a camera included in the accident vehicle, and acquired vehicle data and video data. Based on the above, the analysis department that analyzes the situation of the accident caused by the accident vehicle, the situation of the accident caused by the accident vehicle analyzed by the analysis department, and the information on the accident situation and the past accident case are associated with each other. It has a search unit for searching information on past accident cases corresponding to the situation of the accident caused by the accident vehicle by comparing with the accident case data.

According to the present invention, it is possible to provide a technique capable of promptly collating an accident situation with a past accident case.

It is a figure which shows an example of the accident analysis system which concerns on this embodiment. It is a flowchart which shows the outline of the processing procedure when the accident analyzer analyzes the situation of an accident. It is a figure which shows an example of the information which shows the situation of an accident output by an accident analyzer. It is a figure which shows the hardware configuration example of the accident analyzer. It is a figure which shows the functional block composition example of the accident analyzer. It is a figure for demonstrating the processing procedure when the accident analyzer detects a peripheral object and specifies coordinates. It is a figure for demonstrating the processing procedure when the accident analyzer estimates the relative positional relationship between a vehicle and a peripheral object. It is a figure for demonstrating the processing procedure when the accident analyzer estimates the absolute position of a vehicle and a peripheral object. It is a figure for demonstrating the process of estimating the absolute position of a vehicle. It is a figure which shows an example of the image generated by the accident analyzer. It is a figure for demonstrating the collision direction. It is a figure for demonstrating the process of determining a contact object. It is a figure for demonstrating the contact part. It is a figure for demonstrating the process of specifying the positional relationship between a pedestrian and a pedestrian crossing / safety zone. It is a figure for demonstrating the traveling direction of a vehicle and another vehicle. It is a figure which shows the specific content about the situation of an accident. It is a figure which shows an example of the accident case DB.

Embodiments of the present invention will be described with reference to the accompanying drawings. In each figure, those having the same reference numerals have the same or similar configurations.

<System configuration>
FIG. 1 is a diagram showing an example of an accident analysis system according to the present embodiment. The accident analysis system 1 shown in FIG. 1 includes an accident analysis device 10 and a terminal 20. The accident analyzer 10 and the terminal 20 are connected to each other via a wireless or wired communication network N, and can communicate with each other.

The accident analysis device 10 was photographed by the vehicle data measured by the sensor of the vehicle C (which may be referred to as "own vehicle" for convenience in the following description), which is the accident vehicle, and the camera D of the vehicle C. It has a function of acquiring video data and analyzing the situation of an accident caused by vehicle C based on the acquired vehicle data and video data. Further, the accident analyzer 10 associates the accident situation obtained by the analysis with the accident situation that occurred in the past and the information on the past accident case (hereinafter referred to as "accident case"). By comparing with the database, it has a function to search for accident cases corresponding to the situation of the accident caused by vehicle C. Further, the accident analysis device 10 maps the position of the vehicle C obtained by analyzing the situation of the accident and the position of another vehicle on the map data, and thereby shows an image showing the situation of the accident caused by the vehicle C. Has the function of generating. The image showing the situation of the accident caused by the vehicle C may be any image, and may be, for example, a bird's-eye view or a moving image.

The accident analysis device 10 may be composed of one or a plurality of physical information processing devices or the like, or may be configured by using a virtual information processing device operating on a hypervisor. However, it may be configured using a cloud server.

The sensor included in the vehicle C may be, for example, a GPS receiver, a vehicle speed sensor, an acceleration sensor, a geomagnetic sensor, a throttle sensor and / or a winker detection sensor. The vehicle data measured by the sensor included in the vehicle C is, for example, the latitude and longitude data indicating the current position of the vehicle C, the vehicle speed of the vehicle C, the acceleration of the vehicle C, and the direction in which the vehicle C is facing (for example, north). The angle when is 0 degree), the throttle opening degree (accelerator opening degree), the operating condition of the winker, and the like. Not limited to this, the vehicle C may further include other sensors. Further, the sensors listed above may be sensors included in the vehicle C or sensors included in the camera D. For example, the GPS receiver, the acceleration sensor, and the geomagnetic sensor may be sensors included in the camera D instead of the vehicle C.

The camera D is attached to the front portion or the windshield of the vehicle C so that at least an image of the traveling direction of the vehicle C can be captured. The camera D may be a drive recorder. Further, the camera D may be capable of photographing the side surface direction and the rear of the vehicle C.

The terminal 20 is a terminal operated by, for example, an operator of an insurance company, and displays an accident situation analyzed by the accident analysis device 10, an accident case corresponding to the accident state, and an image generated by the accident analysis device 10. As the terminal 20, any information processing device such as a personal computer (PC), a notebook PC, a tablet terminal, or a smartphone can be used as long as it is an information processing device provided with a display.

FIG. 2 is a flowchart showing an outline of a processing procedure when the accident analyzer 10 analyzes the accident situation. First, the accident analysis device 10 acquires the vehicle data of the vehicle C that caused the accident and the video data at the time of the accident (S10, S11). The vehicle data and video data are recorded in a non-temporary storage medium such as an SD card or a USB memory, and can be taken into the accident analysis device 10 by connecting the recording medium to the accident analysis device 10. Good. Alternatively, the communication function provided in the vehicle C or the camera D may be used to transmit the radio signal to the accident analyzer 10.

Subsequently, the accident analysis device 10 analyzes the video data at the time of the accident for each frame, so that one or more peripheral objects (other vehicles, people) existing around the vehicle C shown in the image for each frame , Signs, road structures, etc.), and further, the coordinates indicating the position where the specified peripheral object is reflected in the image are specified (S12). Subsequently, the accident analyzer 10 estimates the relative positional relationship between the vehicle C and the peripheral objects existing around the vehicle C based on the coordinates of the specified peripheral object (S13).

Subsequently, the accident analysis device 10 estimates the absolute position of the vehicle C by using the position information of the vehicle C acquired by the GPS sensor included in the vehicle C and / or the video data at the time of the accident. Further, the accident analyzer 10 is based on the estimated absolute position of the vehicle C and the relative positional relationship between the vehicle C and the peripheral objects existing around the vehicle C estimated in the processing procedure of step S13. The absolute position of the peripheral objects existing around the vehicle C is estimated (S14).

Subsequently, the accident analysis device 10 maps the calculated absolute positions of the vehicle C and the peripheral objects existing around the vehicle C to the map data to show an image (including a bird's-eye view and a moving image) showing the accident situation. Is generated (S15).

Subsequently, the accident analyzer 10 causes the vehicle C to use another vehicle, an obstacle, or the like based on the acceleration in the front-rear direction and the left-right direction generated in the vehicle C at the moment of the collision acquired by the acceleration sensor provided in the vehicle C. The direction of collision (for example, collision from the front) is estimated (S16).

Subsequently, the accident analysis device 10 includes vehicle data of vehicle C, video data at the time of an accident, absolute positions of vehicle C and peripheral objects existing around vehicle C estimated in the processing procedure of step S14, and step S16. Information indicating the situation of the accident caused by the vehicle C is analyzed by using the collision direction of the vehicle C estimated by the processing procedure of the above and the map data (S17). The information includes a plurality of items (may be called tags), and the situation of the accident is specified by the combination of each item. Subsequently, the accident analyzer 10 searches the accident case database using each item as a key to acquire an accident case corresponding to the situation of the accident caused by the vehicle C (S18).

The order of the processing procedures described above can be arbitrarily changed as long as there is no contradiction in the processing. For example, the processing procedure of step S15 may be after any of the processing procedures of step S16, step S17, or step S18.

FIG. 3 is a diagram showing an example of items included in the information indicating the situation of the accident. The information indicating the situation of the accident includes a plurality of items A to N as shown in FIG. Note that "A: object of contact (automobile, obstacle, etc.)" shown in FIG. 3 is information indicating an object that the vehicle C has contacted. “B: contact portion” indicates a portion where the vehicle C comes into contact with the object (for example, front surface, right side surface, left front bumper, etc.). "C: Road type" indicates the type of road (straight line, curve, intersection, T-junction, highway, etc.) on which vehicle C was traveling at the time of the accident. "D: Signal color of own vehicle and other vehicle" indicates the signal color of the vehicle C side and the signal color of the other party in an accident at an intersection. "E: Positional relationship between pedestrians and pedestrian crossings / safety zones" means that pedestrians had an accident on a pedestrian crossing or a safe zone, or pedestrians had an accident at a place other than a pedestrian crossing or a safe zone. Shows whether it was.

"F: Own vehicle / other vehicle traveling direction" indicates whether vehicle C and other vehicles were going straight, changing lanes, turning left, or turning right at the time of the accident. Indicates whether you were turning right or not. "G: lane position on the highway" indicates the lane (main lane, overtaking lane) in which vehicle C was traveling in the event of an accident on the highway. "H: Priority relationship at an intersection" indicates which of vehicle C and other vehicles should have given priority to the intersection and currency in the event of an accident at the intersection. "I: Presence or absence of suspension violation or signal ignoring violation" indicates whether or not vehicle C and another vehicle have committed a suspension violation or signal ignoring. "J: Whether or not the speed limit is being observed" indicates whether or not vehicle C and another vehicle are observing the speed limit immediately before the accident. "K: Speed before collision of own vehicle / other vehicle" indicates the vehicle speed of vehicle C and other vehicle immediately before the accident. "L: Presence or absence of obstacles on the road" indicates whether or not there are obstacles on the road on which the vehicle C was traveling at the time of the collision. "M: Open / close of the door to be collided" indicates whether or not the door of the other vehicle is opened at the time of the collision. "N: Presence / absence of blinker before collision" indicates whether or not vehicle C has operated the blinker before the collision.

<Hardware configuration>
FIG. 4 is a diagram showing a hardware configuration example of the accident analyzer 10. The accident analyzer 10 includes a processor 11 such as a CPU (Central Processing Unit) and a GPU (Graphical processing unit), a storage device 12 such as a memory, an HDD (Hard Disk Drive) and / or an SSD (Solid State Drive), and a wired or wireless device. It has a communication IF (Interface) 13 for communication, an input device 14 for accepting input operations, and an output device 15 for outputting information. The input device 14 is, for example, a keyboard, a touch panel, a mouse and / or a microphone. The output device 15 is, for example, a display and / or a speaker.

<Functional block configuration>
FIG. 5 is a diagram showing an example of a functional block configuration of the accident analyzer 10. The accident analysis device 10 includes a storage unit 100, an acquisition unit 101, an analysis unit 102, a generation unit 103, a search unit 104, and an output unit 105. The storage unit 100 can be realized by using the storage device 12 included in the accident analysis device 10. Further, in the acquisition unit 101, the analysis unit 102, the generation unit 103, the search unit 104, and the output unit 105, the processor 11 of the accident analysis device 10 executes the program stored in the storage device 12. It can be realized. In addition, the program can be stored in a storage medium. The storage medium in which the program is stored may be a non-transitory computer readable medium. The non-temporary storage medium is not particularly limited, but may be, for example, a storage medium such as a USB memory or a CD-ROM.

The storage unit 100 stores an accident case DB (DataBase) that associates an accident situation with an accident case, and a map data DB. The accident case DB may include information indicating the error rate. The accident case and the error rate corresponding to the situation of the accident that has occurred may be searchable using the information indicating the situation of the accident as a key. The map data DB includes various data such as road data, road width, direction of travel, road type, traffic signs (pause, no entry, etc.), speed limit, position of traffic lights, number of crossing roads at intersections, and the like. The storage unit 100 may be realized by an external server capable of communicating with the accident analysis device 10.

The acquisition unit 101 has a function of acquiring vehicle data measured by a sensor included in the vehicle C (accident vehicle) and video data captured by a camera included in the vehicle C.

The analysis unit 102 has a function of analyzing the situation of the accident caused by the vehicle C based on the vehicle data and the video data acquired by the acquisition unit 101. In addition, in the situation of the accident analyzed by the analysis unit 102, at least the color of the signal when the vehicle C passes through the intersection, the priority relationship when passing through the intersection, and the vehicle speed of the vehicle C exceed the speed limit. It may include whether or not.

Further, the vehicle data of the vehicle C includes at least information indicating the absolute position of the vehicle C measured by the GPS device included in the vehicle C, and the analysis unit 102 includes information indicating the absolute position of the vehicle C and information indicating the absolute position of the vehicle C. The absolute position of the other vehicle is estimated based on the information indicating the relative positional relationship between the vehicle C and the other vehicle, which is obtained by analyzing the image of the other vehicle in the video data. May be good. Further, the analysis unit 102 may estimate the absolute positions of the vehicle C and other vehicles in time series.

Further, the analysis unit 102 estimates the absolute position of the vehicle C by analyzing the video data, the absolute position of the accident vehicle estimated by analyzing the video data, and the absolute position of the vehicle C measured by the GPS device. The situation of the accident may be analyzed by regarding the absolute position obtained by averaging the positions based on a predetermined weight as the absolute position of the vehicle C.

Further, the analysis unit 102 compares the image size of the portion of the video data in which the other vehicle is shown with the data indicating the correspondence between the image size and the distance, and the relative position between the vehicle C and the other vehicle. The situation of the accident may be analyzed by estimating the distance between the vehicle C and the other vehicle, which is one of the information indicating the relationship.

Further, the analysis unit 102 compares the difference between the coordinates of the part of the video data in which the other vehicle is shown and the center coordinates of the video data with the data indicating the correspondence between the coordinates and the angle, and compares the vehicle C with the other. The situation of the accident may be analyzed by estimating the angle difference between the traveling direction of the vehicle C and the direction in which the other vehicle exists, which is one of the information indicating the relative positional relationship with the vehicle. ..

The generation unit 103 has a function of generating an image showing the situation of an accident caused by the vehicle C by mapping the absolute position of the vehicle C and the absolute position of another vehicle to the map data. The image may include a bird's-eye view or a moving image. For example, the generation unit 103 may generate a moving image in which images showing the situation of the accident are arranged in chronological order.

The search unit 104 compares the accident situation caused by the vehicle C analyzed by the analysis unit 102 with the accident case DB (accident case data) that associates the accident situation with the past accident cases. It has a function to search for accident cases corresponding to the situation of the accident caused by vehicle C. Further, the search unit 104 may search for the error rate of the vehicle C in the accident caused by the vehicle C as an accident case corresponding to the situation of the accident caused by the vehicle C.

In addition, the accident case may include one or more correction rates (correction information) for correcting the error rate. If the search unit 104 has a correction ratio corresponding to the situation of the accident caused by the accident vehicle among the correction ratios of 1 or more, the search unit 104 may correct the error rate of the accident vehicle according to the correction ratio. The correction rate will be described later.

The output unit 105 displays information indicating the accident situation analyzed by the analysis unit 102, an accident case corresponding to the accident state searched by the search unit 104, and an image showing the accident situation generated by the generation unit 103. Has a function to output to.

<Processing procedure>
Subsequently, the processing procedure when the accident analysis device 10 analyzes the accident situation caused by the vehicle C will be described in detail along with the processing procedure described with reference to FIG. In the following description, it is assumed that the accident analyzer 10 has already acquired vehicle data and video data from the vehicle C that caused the accident. In addition, it is assumed that the vehicle data and the video data include time information or synchronization information, respectively. That is, in the present embodiment, when analyzing the video data at a certain time point, it is possible to perform the analysis using the vehicle data corresponding to the time point, and conversely, when analyzing the vehicle data at a certain time point, the said It is possible to perform analysis using the video data corresponding to the time point.

(Detection of peripheral objects and identification of coordinates)
FIG. 6 is a diagram for explaining a processing procedure when the accident analyzer 10 detects peripheral objects and specifies coordinates. The processing procedure corresponds to the processing procedure of step S12 of FIG.

The analysis unit 102 analyzes each image obtained by decomposing the video data into frames to obtain vehicles, people, bicycles, traffic signs, and traffic lights (signal colors and arrow signals) shown in the images. Includes), structures around roads (utility poles, street lights, guardrails, etc.), falling objects, road signs, pedestrian crossings, lanes, etc., to identify one or more peripheral objects. In addition, the analysis unit 102 specifies the coordinates of the region in which the peripheral object is shown in the image for each of the specified one or more peripheral objects.

The example of FIG. 6 shows an image example of the Xth frame among each image obtained by decomposing the video data into frames. The X-axis shows the number of pixels in the left-right direction (coordinates in the X direction) with the left end as 0, and the Y-axis shows the number of pixels in the vertical direction (coordinates in the Y direction) with the lower end as 0. In the example of FIG. 6, the truck is shown in the back of the left lane of the lane in which the vehicle C is traveling (the second lane from the left), and the passenger car is in the right lane of the lane in which the vehicle C is traveling. Is reflected. The analysis unit 102 can recognize recognition targets (for example, other vehicles, people, bicycles, road signs, traffic lights, structures around the road (telephone poles, street lights, guardrails, etc.), falling objects, lanes, etc.). A trained model having learned abilities may be provided, and by inputting an image into the trained model, the type of one or more peripheral objects and the area of the peripheral objects shown in the image may be specified. .. Such processing can be realized by using existing technologies such as YOLO (Your Only Look Once) and Mask R-CNN (Mask Regional Convolutional Neural Network).

In the example of A in FIG. 6, the analysis unit 102 shows the track in the area C1 (rectangular area with the points of X = 700 and Y = 300 as the upper left and the points of X = 900 and Y = 300 as the lower right). It is specified that the passenger car is reflected in the area C2 (rectangular area with the points of X = 1400 and Y = 250 as the upper left and the points of X = 1750 and Y = 50 as the lower right). .. Similarly, it is specified that utility poles are shown in the areas C3 to C6 (coordinates are not shown).

The example B in FIG. 6 shows an example of the result of the analysis unit 102 identifying peripheral objects for each frame.

In addition, the analysis unit 102 sits by collating the analysis results of the images for each frame to see if the vehicle is blinking the blinkers, whether the door of the vehicle is opened or closed, and whether a person is standing. You may try to identify whether it is or has fallen. For example, the analysis unit 102 may specify whether or not the blinkers are blinking by determining whether or not the color of the blinker portion of the vehicle recognized in each image is periodically changed.

Further, the analysis unit 102 may specify whether or not the door of the vehicle is opened or closed by determining the change of the door portion of the vehicle recognized in each image. Further, the analysis unit 102 identifies whether a person is standing, sitting, or lying down based on the ratio of the vertical length and the horizontal length of the area in which the person is captured. You may. For specifying the posture of a person, for example, a conventional technique such as Realtime Multi-Person 2D Pose Optimization using Part Affinity Fields can be used.

(Estimation of relative positional relationship between own vehicle and surrounding objects)
FIG. 7 is a diagram for explaining a processing procedure when the accident analyzer 10 estimates the relative positional relationship between the vehicle C and the surrounding objects. The process corresponds to the process procedure of step S13 of FIG.

The analysis unit 102 determines the relative positional relationship between the vehicle C and the peripheral objects existing around the vehicle C for each peripheral object based on the area in which one or more peripheral objects specified in the image are shown. Estimate to. More specifically, the analysis unit 102 estimates the distance from the vehicle C to the peripheral object (d1 and d2 in B in FIG. 7) based on the size of the region in which the peripheral object is captured. Further, the analysis unit 102 sets the traveling direction of the vehicle C (center direction of the image) as a reference (0 degree) based on the difference in the left-right direction between the center coordinates of the area in which the peripheral object is captured and the center coordinates of the image. In this case, the angles in the left-right direction in which the peripheral object exists (θ1 and θ2 in B in FIG. 7) are estimated.

[Example of calculating the distance to surrounding objects]
A specific example is shown with reference to A in FIG. First, the analysis unit 102 stores data indicating how many pixels occupy in the image when an object having a length of 1 m is placed in the vertical direction (or horizontal direction) and photographed 1 m ahead using the camera D. It is assumed that it has been completed. Since the data differs depending on the angle of view (lens angle) of the camera, it may be stored in association with each model of the camera D. Further, depending on the model of the camera D, a wide-angle lens or a fisheye lens is often used. Therefore, after the analysis unit 102 corrects the distortion of the video data captured by the camera D according to the lens characteristics of the camera D, the analysis unit 102 calculates the distance to the peripheral object and the peripheral object exists as shown below. The angle in the left-right direction may be calculated. Distortion correction can be realized by using conventional techniques such as cupic interpolation.

In the example of A in FIG. 7, when an object having a length of 1 m is placed 1 m ahead in the vertical direction and photographed, it is assumed that the number of pixels is 100 pixels in the vertical direction. Similarly, when an object having a length of 1 m is placed 10 m ahead and photographed in the vertical direction (or horizontal direction), the number of pixels occupied in the image is investigated in advance. In the example of FIG. 7, when an object having a length of 1 m is placed 10 m ahead and photographed, it is assumed that the number of pixels is 10 pixels in the vertical direction.

In addition, the size in the vertical direction (or horizontal direction) is predetermined for each peripheral object. For example, in the case of a truck, the length (height) in the vertical direction may be set to 2 m, and in the case of a passenger car, it may be set to 1.5 m.

As described above, assuming that the number of pixels in the vertical direction of a 1 m long object 1 m ahead is 100 pixels and the number of vertical pixels of a 1 m long object 10 m ahead is 10 pixels, the height is high. It can be calculated that the number of pixels in the vertical direction is 200 pixels when the track of 2 m is 1 m ahead, and the number of pixels in the vertical direction is 20 pixels when the track of height 2 m is 10 m ahead. More specifically, when the number of pixels is X and the distance is Y, the following equation holds.

Y = 200 ÷ X (Equation 1)
Next, the analysis unit 102 calculates the distance between the vehicle C and the truck using Equation 1. In the example of A in FIG. 7, the vertical length of the region C1 is 50 ((400-300) ÷ 2) = 50 pixels. Therefore, according to Equation 1, it can be calculated that Y = 200 ÷ 50 = 4 m.

If the height at which the camera D is attached to the vehicle C (height from the ground) and the direction in which the camera D should be directed are fixed with high accuracy, the distance to the surrounding object can be determined only by the value of the Y axis. It is possible to identify. However, according to the processing procedure described above, the distance between the vehicle C and the surrounding objects can be specified even when the mounting position of the camera D differs depending on the driver, such as when renting a drive recorder. Becomes possible.

[Example of calculating the angle in the left-right direction where peripheral objects exist]
A specific example is shown with reference to A in FIG. First, it is assumed that the analysis unit 102 stores data indicating the angle of view (lens angle) of the camera D. Since the data differs depending on the model of the camera D, the data may be associated and stored for each model of the camera D.

Here, by dividing the angle of view of the camera D by the number of pixels in the left-right direction of the screen, it is possible to find out how many times one pixel corresponds to the angle of view. For example, if the total number of pixels in the left-right direction of the screen is 2000 pixels and the angle of view of the camera D is 80 degrees, 200 pixels corresponds to 8 degrees. That is, when the peripheral object sets the traveling direction of the vehicle C to 0 degrees by calculating the difference in the number of pixels in the horizontal direction between the center position of the area where the peripheral object is shown in the image and the central position of the image. In addition, it is possible to calculate the angle in the left-right direction (angle in the horizontal plane) in which the peripheral object exists.

For example, in A of FIG. 7, the center position in the X direction of the area where the track is shown is X = 800. Further, it is assumed that the angle of view of the camera D is 80 degrees and the total number of pixels in the left-right direction of the screen is 2000 pixels. The center position of the image is X = 1000. Therefore, the center position of the area where the track is shown and the center position of the image are separated by 200 pixels (1000-800). As described above, since 200 pixels corresponds to 8 degrees, it can be calculated that the angle in the left-right direction between the vehicle C and the truck (θ1 in B in FIG. 7) is 8 degrees.

In the calculation method described above, when the peripheral object is extremely close to the vehicle C and a part of the peripheral object goes out of the image, it becomes impossible to calculate the distance and the angle. However, when a part of the peripheral object is out of the image, it is possible to estimate that the distance between the peripheral object and the vehicle C is within a certain distance. It is also possible to estimate the position of the peripheral object that disappeared from the image based on the change in the position of the peripheral object in the images of the previous and next frames.

For example, in the image of a certain frame X, a part of the peripheral object is outside the image, but in the image of the frame X-1 which is one frame before, the entire peripheral object is shown, and the vehicle C and the corresponding object are shown. It is assumed that the distance to the surrounding object is 10 m. Further, in the image of the frame X-2, which is one frame before, it is assumed that the distance between the vehicle C and the peripheral object is 11 m. In this case, the distance between the vehicle C and the peripheral object in the image of the frame X can be estimated to be a distance of 9 m. Further, when the peripheral object is a vehicle, the distance may not be estimated by recognizing the entire vehicle as an image, but the image may be recognized only for a part of the vehicle (for example, a license plate). This makes it possible to estimate the distance and angle from the vehicle C as long as the license plate is shown in the image even when a part of the vehicle goes out of the image.

(Estimate the position of your vehicle and the absolute position of surrounding objects)
FIG. 8 is a diagram for explaining a processing procedure when the accident analyzer 10 estimates the absolute position between the vehicle C and the peripheral object. The process corresponds to the process procedure of step S14 of FIG.

First, the analysis unit 102 estimates the absolute position of the vehicle C. The analysis unit 102 may estimate the absolute position of the vehicle C by using the position information of the vehicle C acquired by the GPS sensor included in the vehicle C included in the vehicle data of the vehicle C. Alternatively, the analysis unit 102 analyzes the video data at the time of the accident by using, for example, a conventional technique called Structure From Motion or Simultaneous Localization and Mapping (hereinafter, referred to as “SFM” for convenience) of the vehicle C. By synthesizing the absolute position and the absolute position of the vehicle C estimated by the GPS sensor, the absolute position of the vehicle C may be estimated more accurately.

[Estimation of absolute position of vehicle C based on SFM]
By using SFM, the feature points included in the image of each frame in the video data are extracted, the corresponding points (corresponding feature points) in the image of each frame are specified from the extracted feature points, and the corresponding corresponding points are further specified. By following the movement, the movement of the camera can be reproduced. The extraction of feature points can be realized, for example, by using a conventional technique called SIFT feature quantity. Further, the identification of the corresponding points can be realized by using, for example, a conventional technique called FLAN (Fast Library for Approximate Nearest Neighbors).

Here, since the camera that captured the video data is the camera D mounted on the vehicle C, the reproduced movement of the camera can be regarded as the movement of the vehicle C. Further, since the vehicle data of the vehicle C includes the vehicle speed data, it is possible to estimate the distance traveled by the vehicle C between each frame by collating the vehicle speed data with the frame rate in the video data. it can. For example, when the vehicle C is traveling at a speed of 60 km / h and the frame rate of the video data is 15 frames per second, the moving distance of the vehicle C per frame is about 1 m.

In addition, in order to obtain the movement of the vehicle C with respect to the surrounding environment instead of other vehicles running in parallel, the analysis unit 102 excludes the feature points of the moving peripherals when extracting the feature points. , Extract feature points for fixedly installed peripheral objects. For example, the analysis unit 102 may extract feature points for traffic signs, traffic lights, and structures around roads (telephone poles, street lights, guardrails, etc.). The type of peripheral object can be grasped by having the trained model described above identify it.

For example, A in FIG. 8 shows image data in frame N, and B in FIG. 8 shows image data in frame N + 1. In A of FIG. 8 and B of FIG. 8, the areas C3 to C6 corresponding to the peripheral objects fixedly installed move backward according to the movement of the vehicle C, but the area C1 of the moving truck The position has hardly changed, and the position of the area C2 of the passenger car traveling in the oncoming lane toward the vehicle C has changed significantly.

By using SFM, for example, the analysis unit 102 moves the position of the vehicle C in the second frame 2 m forward and 1 m left compared to the first frame, and the position of the vehicle C in the third frame is It is possible to calculate information indicating the relative position of the vehicle C with respect to the first frame of the video data, such as moving 2.5 m forward and 0.5 m left compared to the second frame. ..

Subsequently, the analysis unit 102 selects the position information corresponding to the time when the first frame of the video data is taken from the position information indicating the absolute position of the vehicle C acquired by the GPS sensor. The position information acquired by the GPS sensor includes time information, and the video data in the present embodiment also includes time information indicating the recorded time. Therefore, the analysis unit 102 can select the GPS position information corresponding to the first frame of the video data by collating the time included in the video data with the time included in the position information.

Subsequently, the analysis unit 102 identifies the absolute position of the vehicle C in the second and subsequent frames of the video data by using the GPS position information corresponding to the first frame of the video data. As described above, the analysis unit 102 calculates information indicating the relative movement between frames. Further, the vehicle data in the present embodiment includes directional data indicating which direction the front direction of the image is facing, and it is possible to grasp from the directional data which direction the front is pointing. .. Therefore, the analysis unit 102 sets the selected GPS position information as the absolute position of the vehicle C in the first frame, so that the vehicle C corresponds to the relative position of the vehicle C in the second frame (relative position obtained by SFM). The absolute position (latitude and longitude) of can be calculated. For example, when the latitude of the first frame acquired by the GPS sensor is 134.45 degrees and the longitude is 32.85 degrees, the latitude and longitude are used as the starting point to move 2 m forward in the north direction and 1 m in the west direction. The latitude and longitude corresponding to the changed position are the absolute positions of the vehicle C in the second frame. The analysis unit 102 calculates the absolute position (latitude and longitude) of the vehicle C based on the SFM for all the frames by repeating the process for each frame.

[Estimation of absolute position of vehicle C]
As shown in FIG. 9, the analysis unit 102 estimates the absolute position of the vehicle C for each frame by using the absolute position of the vehicle C based on SFM and the absolute position of the vehicle C by GPS. It is assumed that the points f11 to f16 shown on the left side of FIG. 9 are the absolute positions of the vehicle C obtained by analyzing 1 frame to 6 frames of the video data, respectively. Further, the points f21 to f26 shown in the central figure of FIG. 9 are the absolute positions of the vehicle C at the time corresponding to 1 frame to 6 frames of the video data among the absolute positions of the vehicle C obtained by GPS. Assume. The points f31 to f36 shown on the right side of FIG. 9 indicate the estimated absolute positions of the vehicle C. As described above, the point f11 indicating the absolute position of the vehicle C corresponding to one frame of the video data is the same point as the point f21.

For example, the analysis unit 102 may estimate the absolute position obtained by simply averaging the absolute position of the vehicle C based on SFM and the absolute position of the vehicle C based on GPS as the absolute position of the vehicle C. For example, for a speed of 30 km / h or more, the value obtained by dividing the total value by 2 may be set as the absolute position of the vehicle C. If the vehicle speed of the vehicle C at the points f13 and f23 is 30 km or more, the latitude of the absolute position of the vehicle C (point f33) is calculated by (latitude of point f13 + latitude of point f23) / 2. be able to. Similarly, the longitude of the absolute position of the vehicle C (point f33) can be calculated by (longitude of point f13 + longitude of point f23) / 2.

Further, the analysis unit 102 sets the absolute position of the vehicle C based on the SFM and the absolute position of the vehicle C based on the GPS as the absolute position of the vehicle C by averaging them with a predetermined weight according to the vehicle speed of the vehicle C. It may be something to do. For example, for speeds less than 30 km / h (for example, 5 km), the absolute position of vehicle C based on SFM is considered to be more accurate, and the absolute position of vehicle C based on SFM and the absolute position of vehicle C based on GPS are determined. For example, the absolute position averaged at a ratio of 4: 1 may be set as the absolute position of the vehicle C. If the vehicle speed of vehicle C at points f16 and f26 is 5 km or more, the latitude of the absolute position of vehicle C (point f36) is (latitude of point f16 x 4 + latitude of point f26 x 1) / 5 Can be calculated with. Similarly, the longitude of the absolute position of the vehicle C (point f36) can be calculated by (longitude of point f16 × 4 + longitude of point f26 × 1) ÷ 5.

The analysis unit 102 estimates the absolute position of the vehicle C for each frame by using the absolute position of the vehicle C based on the SFM that has been subjected to the noise removal processing and the absolute position of the vehicle C that is based on the GPS that has been subjected to the noise removal processing. You may try to do it. For noise removal, for example, a Kalman filter may be used. The Kalman filter used to remove the noise at the absolute position of the vehicle C based on SFM and the Kalman filter used to remove the noise at the absolute position of the vehicle C based on GPS may be different Kalman filters. It is possible to further improve the accuracy of the estimated absolute position of the vehicle C.

[Estimate the absolute position of surrounding objects]
The analysis unit 102 calculates the absolute position of the peripheral object for each frame based on the estimated absolute position of the vehicle C for each frame and the relative positional relationship between the vehicle C for each frame and the peripheral object. As described above, the relative positional relationship between the vehicle C and the peripheral object is the distance between the vehicle C and the peripheral object and the traveling direction of the vehicle C (center direction of the image) as shown in B of FIG. Is indicated by the angle in the left-right direction in which the peripheral object exists when the reference point (0 degree) is used. The analysis unit 102 sets the moving direction of the absolute position of the vehicle C obtained by taking the difference between the frames with respect to the absolute position of the vehicle C for each frame as the traveling direction of the vehicle C, and uses the estimated traveling direction as a reference to the periphery. By calculating the latitude and longitude corresponding to the relative position of the object, the absolute position (latitude, longitude) of the surrounding object can be obtained.

(Generation of an image showing the accident situation)
The generation unit 103 generates an image showing the accident situation by mapping the absolute position of the vehicle C and the absolute position of the surrounding objects for each frame estimated by the processing procedure described above on the road map. The process corresponds to the process procedure of step S15 of FIG.

FIG. 10 is a diagram showing an example of an image generated by the accident analyzer 10. The Y-axis and the X-axis in FIG. 10 correspond to latitude and longitude, respectively. In the roads shown in FIGS. 10A to E, the central line indicates a median strip. The two lanes on the right side are lanes traveling from bottom to top, and the two lanes on the left side are lanes traveling from top to bottom. It is assumed that A to E in FIG. 10 correspond to frame X, frame X + 1, frame X + 2, frame X + 3, and frame X + 4, respectively, but this is merely an example and is not limited thereto. By mapping the absolute position of the vehicle C and the absolute position of the surrounding objects for each frame on the road map, the situation of the accident can be reproduced. For example, at the time point A in FIG. 10, the passenger car V2 is running from the oncoming lane of the vehicle C, and at the time point B in FIG. 10, the vehicle C and the passenger car V2 are close to each other and collide with each other. Regarding the presence / absence of a collision and the display of the collision site, the result estimated by the processing procedure related to "estimation of the collision position" described later may be used.

In addition, in C to E of FIG. 10, the passenger car V2 does not exist. This is because the passenger car V2 has moved to the rear of the vehicle C, so that the passenger car V2 cannot be seen in the camera D. If the camera D can also photograph the rear of the vehicle, the movement of the passenger car V2 after the collision is estimated and reflected in the image showing the accident situation by analyzing the video data obtained by photographing the rear of the vehicle C. It is possible.

(Estimation of collision direction)
The analysis unit 102 estimates the direction in which the vehicle C collides with another vehicle, an obstacle, or the like, based on the acceleration generated in the vehicle C at the moment of the collision. The process corresponds to the process procedure of step S16 of FIG.

More specifically, the analysis unit 102 determines the direction of the collision based on the acceleration pattern generated in the vehicle C at the moment of the collision. When a collision occurs, the vehicle C generates a negative acceleration in the direction in which the collision target exists, and also generates an acceleration in the direction opposite to the collision direction due to the reaction of the collision. That is, it is common that the vehicle C is accelerated so as to reciprocate on an axis connecting a certain direction of the colliding object and a direction 180 degrees opposite to the direction.

Therefore, the analysis unit 102 sums the output values from the acceleration sensor for each direction within a predetermined period from the time at the time of the collision, and estimates the direction in which the total value is large as the collision direction. More specifically, as shown in A of FIG. 11, when the direction of acceleration is 0 degrees in the front direction of the vehicle C, the front (D1: 337.5 degrees to 22.5 degrees) and the right front (D1: 337.5 degrees to 22.5 degrees) D2: 22.5 degrees to 67.5 degrees), right (D3 direction: 67.5 degrees to 112.5 degrees), right rear (D4: 112.5 degrees to 157.5 degrees), rear (D5: 157 degrees) .5 degrees to 202.5 degrees), left rear (D6: 202.5 degrees to 247.5 degrees), left (D7: 247.5 degrees to 292.5 degrees), and left front (D8: 292.5 degrees) It is totaled for each of the eight directions (degree to 337.5 degrees), and the direction with the larger total value is regarded as the collision direction. The acceleration measured by the acceleration sensor is an acceleration according to the law of inertia. Therefore, at the time of a collision, a positive acceleration is measured in the collision direction.

The analysis unit 102 may consider the time when the acceleration sensor detects the largest acceleration as the time at the time of the collision. When the camera D is a drive recorder, the analysis unit 102 may consider the time at the time of the collision recorded in the drive recorder as the time at the time of the collision.

As shown in B of FIG. 11, when the vehicle C comes into contact with the vehicle V in the right front direction, for example, from the acceleration sensor, time 1 (45 degree direction, 3.0 G), time 2 (47 degree direction, 1. It is assumed that the acceleration data of 0G), time 3 (225 degree direction, 2.5G), time 4 (227 degree direction, 0.5G), and time 5 (40 degree direction, 2.0G) are acquired in chronological order. .. That is, the vehicle C suddenly decelerates due to the collision with the vehicle V at the time of time 1, and then the acceleration is generated so as to reciprocate between the front right and the rear left.

In this case, the total acceleration corresponding to the right front (D2) is 3.0G + 1.0G + 2.0G = 6.0G, and the total acceleration corresponding to the left rear (D6) is 2.5G + 0.5G = 3. It is 0.0G. Therefore, the analysis unit 102 estimates that the right front (D2) direction is the collision direction.

Further, the analysis unit 102 may estimate the direction in which the vehicle C collides with the pedestrian by analyzing the video data. For example, in the above-mentioned process of detecting peripheral objects, it is estimated that the vehicle C and the pedestrian collide in the front (D1) direction when the pedestrian is shown in the image in a predetermined size or larger. It may be. When vehicle C and a pedestrian collide, the acceleration detected by the acceleration sensor is smaller than when it collides with another vehicle, and when vehicle C and a pedestrian collide, a frontal collision may occur. Mostly. Therefore, when detecting a collision between the vehicle C and a pedestrian, it is preferable to analyze the video data.

(Output of information indicating the situation of the accident)
The analysis unit 102 outputs information indicating the situation of the accident caused by the vehicle C. The process corresponds to the process procedure of step S17 of FIG. As described above, the analysis unit 102 outputs each item shown in FIG. 3 as information indicating the situation of the accident. Hereinafter, the processing procedure for specifying each item shown in FIG. 3 will be described in detail. In the following description, the "other vehicle" means an opponent with whom the vehicle C collides.

[A: Target of contact (cars, obstacles, etc.)]
At the time when the vehicle C collides, the analysis unit 102 sets the peripheral object closest to the vehicle C among the peripheral objects existing in a predetermined range (for example, a range of 45 degrees) centered on the collision direction of the vehicle C to the vehicle C. Identify the object in contact. More specifically, the analysis unit 102 searches for the absolute position of the peripheral object at the time when the vehicle C collides (or the image of the frame closest to the time when the vehicle C collides), and the absolute position of the peripheral object is obtained. ), The peripheral objects existing in a predetermined range centered on the collision direction of the vehicle C and existing at the position closest to the vehicle C are extracted, and the extracted peripheral objects are the objects in contact with the vehicle C. Identify as being.

A specific example will be described with reference to FIG. In FIG. 12, it is assumed that the vehicle C collides with a peripheral object on the right front side. In the case of A in FIG. 12, the object closest to the vehicle C in the right front direction is the passenger car V1. Therefore, the analysis unit 102 identifies that the vehicle C has collided with the passenger car V1. Similarly, in the case of B in FIG. 12, the object closest to the vehicle C in the right front direction is the passenger car V5. Therefore, the analysis unit 102 identifies that the vehicle C has collided with the passenger car V5.

If the collision direction of the vehicle C is a direction not photographed by the camera D (that is, only the front of the vehicle C is photographed), the absolute value of the surrounding object cannot be estimated for that direction. Therefore, it is not possible to identify the object in contact. In this case, the analysis unit 102 may consider that some vehicle has collided.

[B: Contact site]
The analysis unit 102 identifies the contact portion according to the collision position of the vehicle C. Specifically, when the collision direction is the front surface (D1) of FIG. 11, the contact portion is assumed to be the front bumper shown in FIG. When the collision direction is the front right (D2), the contact portion is assumed to be the front right bumper shown in FIG. When the collision direction is right (D3), the contact portion is assumed to be the right side surface shown in FIG. When the collision direction is right rear (D4), the contact portion is assumed to be the right rear bumper shown in FIG. Further, when the collision direction is rearward (D5), the contact portion is assumed to be the rear bumper shown in FIG. Further, when the collision direction is the left rear (D6), the contact portion is assumed to be the left rear bumper shown in FIG. When the collision direction is left (D7), the contact portion is assumed to be the left side surface shown in FIG. When the collision direction is the left front (D8), the contact portion is assumed to be the left front bumper shown in FIG.

[C: Road type]
The analysis unit 102 identifies the road type of the road on which the vehicle C was traveling at the time of the collision by collating the absolute position of the vehicle C at the time when the vehicle C collided with the map data. The road type specified by the analysis unit 102 may include information such as in a straight road, a curve, an intersection, and a T-junction in addition to information such as a general road and an expressway.

[D: Signal color of own vehicle and other vehicles]
The analysis unit 102 identifies the signal color by using the color of the traffic light or the arrow signal specified by performing image analysis of the video data frame by frame. For example, when the road type of the road on which the vehicle C was traveling at the time of the collision is in an intersection, the analysis unit 102 has the most color of the traffic light identified by analyzing the video data at the time of the collision. The color of the traffic light, which is an image of a frame at a near time and was specified before the vehicle C entered the intersection, is specified as the signal color (blue, yellow, or red) on the vehicle C side. .. Whether or not the vehicle C has entered the intersection can be determined by collating the absolute position of the vehicle C with the map data.

It is assumed that the signal color on the intersecting side at the intersection is not reflected in the video data, or is reflected only for a very short time, and it is difficult to judge. Therefore, when the signal color on the vehicle C side is blue, the analysis unit 102 may specify that the signal color on the intersecting side is red. If another vehicle that collided with vehicle C is driving on the road on the side crossing the intersection, the signal on the side of the other vehicle was red (that is, the other vehicle entered the intersection at the red light). Will be specified. Similarly, when the signal color on the vehicle C side is red, the analysis unit 102 may specify that the signal color on the intersecting side is blue. If another vehicle that collided with vehicle C is driving on the road on the side crossing the intersection, the signal on the side of the other vehicle is blue (that is, the other vehicle entered the intersection at the green light). Will be identified.

[E: Positional relationship between pedestrians and pedestrian crossings / safety zones]
When the target in contact with the vehicle C is a pedestrian, the analysis unit 102 uses the absolute position of the pedestrian and the absolute position of the pedestrian crossing (or the safety zone) specified by image analysis of the video data frame by frame. , Identify the positional relationship between pedestrians and pedestrian crossings / safety zones. The analysis unit 102 may acquire the absolute position of the pedestrian crossing from the map data.

The analysis unit 102 analyzes the image of the frame before the time when the vehicle C collides with the pedestrian and finally shows the pedestrian crossing or the safety zone, and the absolute position of the vehicle C is obtained. And the point where the line connecting the absolute position of the pedestrian in contact with the pedestrian crossing or the area of the safe zone collides with the side farthest from the vehicle C is calculated. Subsequently, the analysis unit 102 identifies that the pedestrian in the accident was on a pedestrian crossing or a safe zone when the distance between the absolute position of the person and the point is within a predetermined distance (for example, 7 m). In addition, the analysis unit 102 identifies that the pedestrian in the accident was not on the pedestrian crossing or the safe zone when the distance between the absolute position of the person and the point exceeds a predetermined distance (for example, 7 m).

FIG. 14 is a diagram for explaining a process of identifying a positional relationship between a pedestrian and a pedestrian crossing / safety zone. For example, in FIG. 14, in FIG. 14, in the analysis unit 102, the line L connecting the absolute position (absolute position of the center of the vehicle C) P1 of the vehicle C and the absolute position P3 of the pedestrian collides with the side of the pedestrian crossing farthest from the vehicle C. When the point P2 is calculated and the distance between the absolute position P3 of the pedestrian and the point P2 is a predetermined distance, the pedestrian R is considered to be on the pedestrian crossing.

In the example A of FIG. 14, the pedestrian is on the pedestrian crossing, but in the example B of FIG. 14, the pedestrian is not on the pedestrian crossing. However, in both examples, the analysis unit 102 considers the pedestrian to be on the pedestrian crossing if the distance between the absolute value P3 of the pedestrian and the point P2 is within a predetermined distance. In the accident case, it is not so important whether the pedestrian in the accident was walking on the pedestrian crossing surely, and it is judged that he was walking on the pedestrian crossing even if it slightly protruded from the pedestrian crossing. This is because it is often done.

[F: Travel trajectory of own vehicle / other vehicle at an intersection]
When an accident occurs near an intersection (when the absolute position of the vehicle C at the time when the vehicle C collides is within a predetermined range from the center position of the intersection), the analysis unit 102 determines the absolute position of the vehicle C for each frame. Are arranged on the map data of the intersection to specify the traveling locus of the vehicle C. In addition, the analysis unit 102 identifies the traveling locus of the other vehicle by arranging the absolute positions of the other vehicle for each frame on the map data of the intersection. More specifically, the analysis unit 102 determines whether the vehicle C and another vehicle went straight at the intersection, turned right, turned left, or turned right quickly based on the magnitude and direction of the curvature of the traveling locus. To identify.

FIG. 15 is a diagram for explaining the traveling directions of the vehicle C and the other vehicle. For example, when the curvature of the traveling locus in the predetermined region including the intersection is less than the predetermined value, the analysis unit 102 determines that the vehicle C or another vehicle has traveled straight through the intersection, and determines that the curvature of the traveling locus in the predetermined region. If is greater than or equal to a predetermined value, it may be determined that the vehicle C or another vehicle has turned left or right at the intersection. Further, when the traveling locus of the own vehicle and another vehicle making a right turn passes inside the center of the intersection, the analysis unit 102 may determine that the vehicle makes a fast turn to the right. Further, the analysis unit 102 may determine that the vehicle C and the other vehicle have changed lanes when the curvature is reversed within a predetermined time (for example, 2 seconds or the like).

"G: Lane position on the highway"
When the accident occurs on the highway (when the absolute position of the vehicle C at the time when the vehicle C collides is on the highway), the analysis unit 102 analyzes the video data frame by frame, or By collating the absolute position of the vehicle C with the map data, it is possible to identify whether the vehicle C is traveling in the main lane or the overtaking lane. For example, if the vehicle is in the rightmost lane, it is identified as being in the overtaking lane, and if it is in a lane other than the rightmost, it is identified as being in the main lane.

"H: Priority relationship at intersections"
When the accident occurred near the intersection, the analysis unit 102 extracted the presence / absence of the stop sign at the intersection and the road width from the map data, so that the vehicle C could preferentially enter the intersection. Judge whether or not. More specifically, among the roads crossing the intersection, the vehicle traveling on the side without the stop sign is specified as having priority. In addition, when there is a difference of more than twice in the width of the road crossing the intersection, the vehicle traveling on the wider road is specified as having priority. If there is no stop sign and there is no difference in road width, it is specified that there is no priority relationship.

"I: Pause, presence or absence of signal ignore violation"
The analysis unit 102 collates the traveling locus of the vehicle C obtained by arranging the absolute positions of the vehicle C and the other vehicle for each frame with the map data, so that the vehicle C and the other vehicle do not stop the stop sign. Identify whether or not it passed. Further, in the analysis unit 102, when the vehicle C or another vehicle passes without stopping the stop sign, the vehicle speed of the vehicle C or another vehicle at the time of passing is a predetermined section (for example, 3 m before and after) before and after the stop sign. Etc.), if the speed is higher than the predetermined speed (for example, 5 km, etc.), it is specified as a stop violation. On the other hand, when the vehicle speed of the vehicle C or another vehicle at the time of passing is less than the predetermined speed, the analysis unit 102 identifies that the suspension violation has not occurred.

Further, the analysis unit 102 detects whether or not the vehicle C has passed the signal by collating the travel locus of the vehicle C obtained by arranging the absolute positions of the vehicle C for each frame with the map data. When the signal is passed, the analysis unit 102 acquires the color of the traffic light obtained by analyzing the image of the frame before the time when the signal is passed and the image of the frame in which the traffic light is last shown. To do. When the color of the signal is red, the analysis unit 102 identifies that the vehicle C is ignoring the signal when passing through the signal.

"J: Whether or not the speed limit is being observed"
The analysis unit 102 identifies the speed limit set on the road on which the vehicle C travels by collating the travel locus of the vehicle C obtained by arranging the absolute positions of the vehicle C for each frame with the map data. .. Further, the analysis unit 102 collates the vehicle speed and the speed limit at the timing of a predetermined time (for example, 5 seconds before) the time when the vehicle C collides with the vehicle speed data included in the vehicle data of the vehicle C. When the vehicle speed exceeds the speed limit, the analysis unit 102 identifies that the vehicle C has violated the speed. When the vehicle speed is equal to or less than the speed limit, the analysis unit 102 identifies that the vehicle C has not violated the speed.

"K: Speed before collision of own vehicle / other vehicle"
The analysis unit 102 identifies the speed of the vehicle C before the collision from the vehicle speed data included in the vehicle data of the vehicle C. Further, the analysis unit 102 calculates the vehicle speed of the other vehicle from the change in the absolute position of the other vehicle estimated for each frame of the video data. For example, when the moving distance of another vehicle per frame is 1 m and the frame rate of video data is 15 frames per second, it can be specified that the other vehicle is traveling at a speed of 60 km / h.

"L: Presence or absence of obstacles on the road"
The analysis unit 102 identifies the presence or absence of obstacles on the road by performing image analysis of the video data frame by frame. The analysis unit 102 has an object that cannot be determined to be a road on the lane road on which the vehicle C is traveling, the object is neither a person, a car, a bicycle, or a motorcycle, and the absolute position between the frames is fixed. If it is not moving, it may be considered an obstacle.

"M: Opening and closing the door of another vehicle that collided"
The analysis unit 102 identifies whether the door of another vehicle is open or closed by performing image analysis of the video data frame by frame.

"N: Presence or absence of blinker operation before collision"
The analysis unit 102 identifies whether or not the vehicle C has operated the blinker before the collision from the blinker operation information included in the vehicle data of the vehicle C. In addition, the analysis unit 102 identifies whether or not the blinker was operated before the collision by performing image analysis of the video data for each frame.

FIG. 16 shows the contents specified by the processing procedure described above for each item included in the information indicating the accident situation.

(Search for corresponding accident cases)
FIG. 17 is a diagram showing an example of an accident case DB. As shown in FIG. 17, the accident cases include "search conditions" for searching the accident case, "accident content" indicating the content of the accident, and "negligence rate (basic)" indicating the basic negligence rate. Includes "correction factors" that are conditions that require correction of the negligence rate.

In the example of FIG. 17, the target of the collision of the vehicle C is a vehicle (corresponding to "passenger car or truck" in the item A of FIG. 16), and the vehicle C and the vehicle collide with each other at the intersection (item C). (Corresponds to "inside the intersection"), and it is an accident between a straight-ahead vehicle and a right-turning vehicle (in item F, vehicle C is going straight and another vehicle is turning right, or another vehicle is going straight and vehicle C is turning right) , A straight-ahead vehicle is a red light and a right-turning vehicle is yellow (in item D, vehicle C is red and another vehicle is yellow, or vehicle C is yellow and another vehicle is red). It is shown that the ratio of straight-ahead vehicles and right-turn vehicles is 70:30.

In addition, as a correction factor, it is shown that when a straight-ahead vehicle violates a speed of 15 km or more (a speed violation of 15 km or more in items J and K), the negligence ratio between the straight-ahead vehicle and the right-turn vehicle is 75:25. Has been done. Similarly, when a straight-ahead vehicle violates a speed of 30 km or more (a speed violation of 30 km or more in items J and K), the negligence ratio between the straight-ahead vehicle and the right-turn vehicle is shown to be 80:20. .. Further, it is shown that when a right-turning vehicle makes a right turn without a blinker (without a blinker in item N), the error ratio between the straight-ahead vehicle and the right-turning vehicle is 60:40.

The correction element may also include a correction element that is difficult for the accident analyzer 10 to determine, such as "other significant errors" in the error rate.

The search unit 104 searches for an accident case that matches the specified content by collating the specified content for each item of information indicating the accident situation with the accident case DB. In addition, the search unit 104 acquires the error rate corresponding to the searched accident case from the accident case DB. In addition, the search unit 104 searches for the presence or absence of the corresponding correction element by collating the searched correction element of the accident case with each item indicating the accident situation. When the corresponding correction element exists, the search unit 104 corrects the error rate according to the correction rate of the corresponding correction element.

Further, the output unit 105 outputs the accident content and the error rate of the accident case searched by the search unit 104 to the terminal 20. Further, when the correction element includes a correction element that is difficult to determine by the accident analyzer 10, the output unit 105 determines the number of the accident case, the determined negligence rate, and the correction element that is difficult to determine. The wording may be output to the terminal 20.

When searching the accident case DB, the search unit 104 may search for accident cases that correspond to a part of the search conditions in addition to the accident cases that correspond to all the search conditions. Further, when searching for an accident case corresponding to a part of the search condition, the search unit 104 ranks the accident cases according to the number of unmatched items, and the output unit 105 outputs the accident contents in the order of the ranking. You may do so.

Further, the accident cases are further ranked in descending order of the number of occurrences, and the search unit 104 may search the accident case DB in descending order of the rank.

<Summary>
According to the embodiment described above, the accident analyzer 10 analyzes the information indicating the accident situation based on the vehicle data and the video data acquired from the vehicle C. This made it possible to quickly collate the accident situation with the accident case.

The embodiments described above are for facilitating the understanding of the present invention, and are not for limiting and interpreting the present invention. The flowchart, sequence, each element included in the embodiment, its arrangement, material, condition, shape, size, and the like described in the embodiment are not limited to those exemplified, and can be changed as appropriate. In addition, the configurations shown in different embodiments can be partially replaced or combined.

10 ... Accident analyzer, 11 ... Processor, 12 ... Storage device, 13 ... Communication IF, 14 ... Input device, 15 ... Output device, 20 ... Terminal, 100 ... Storage unit, 101 ... Acquisition unit, 102 ... Analysis unit, 103 … Generation unit, 104… Search unit, 105… Output unit

Claims (11)

An acquisition unit that acquires vehicle data measured by a sensor provided in the accident vehicle and video data taken by a camera provided in the accident vehicle.
An analysis unit that analyzes the situation of an accident caused by the accident vehicle based on the acquired vehicle data and the video data.
By comparing the accident situation caused by the accident vehicle analyzed by the analysis unit with the accident case data in which the accident situation and the information on the past accident case are associated with each other, the accident caused by the accident vehicle is compared. A search unit that searches for information on past accident cases that correspond to the situation in
Accident analyzer with. The information on past accident cases includes the error rate,
The search unit searches for the accident rate of the accident vehicle in the accident caused by the accident vehicle as information on the accident case corresponding to the situation of the accident caused by the accident vehicle.
The accident analyzer according to claim 1. The information regarding the past accident case includes one or more correction information for correcting the error rate.
When the search unit includes the correction information corresponding to the situation of the accident caused by the accident vehicle in the one or more correction information, the search unit corrects the error rate of the accident vehicle according to the correction information.
The accident analyzer according to claim 2. The vehicle data of the accident vehicle includes at least information indicating the absolute position of the accident vehicle measured by the GPS device included in the accident vehicle.
The analysis unit analyzes the information indicating the absolute position of the accident vehicle and the image of the other vehicle in the video data to obtain the relative positional relationship between the accident vehicle and the other vehicle. The absolute position of the other vehicle is estimated based on the information indicating the change on the time axis.
The accident analyzer according to any one of claims 1 to 3. It has a generation unit that generates an image showing the situation of an accident caused by the accident vehicle on a time axis by mapping the absolute position of the accident vehicle and the absolute position of the other vehicle to map data.
The accident analyzer according to claim 4. The analysis unit averages the absolute position of the accident vehicle estimated by analyzing the video data and the absolute position of the accident vehicle measured by the GPS device based on a predetermined weight. The situation of the accident is analyzed by regarding it as the absolute position of the accident vehicle.
The accident analyzer according to claim 4 or 5. The analysis unit compares the image size of the portion of the video data in which the other vehicle is shown with the data indicating the correspondence between the image size and the distance, and the relative of the accident vehicle and the other vehicle. The situation of the accident is analyzed by estimating the distance between the accident vehicle and the other vehicle showing a proper positional relationship.
The accident analyzer according to any one of claims 4 to 6. The analysis unit compares the difference between the coordinates of the portion of the video data in which the other vehicle is shown and the center coordinates of the video data with the data indicating the correspondence between the coordinates and the angle. The situation of the accident is analyzed by estimating the angle difference between the traveling direction of the accident vehicle and the direction in which the other vehicle exists, which indicates the relative positional relationship between the accident vehicle and the other vehicle.
The accident analyzer according to any one of claims 4 to 7. The situation of the accident includes at least the color of the signal when the accident vehicle passes through the intersection, the priority relationship when passing through the intersection, and whether or not the vehicle speed of the accident vehicle exceeds the speed limit. ,
The accident analyzer according to any one of claims 1 to 8. This is an accident analysis method performed by an accident analyzer.
The step of acquiring the vehicle data measured by the sensor of the accident vehicle and the video data taken by the camera of the accident vehicle, and
A step of analyzing the situation of an accident caused by the accident vehicle based on the acquired vehicle data and the video data, and
By comparing the situation of the accident caused by the accident vehicle analyzed in the step of the analysis with the accident case data in which the situation of the accident and the information on the past accident case are associated with each other, the accident vehicle caused the accident. Steps to search for information on past accident cases that correspond to the accident situation, and
Accident analysis methods including. On the computer
The step of acquiring the vehicle data measured by the sensor of the accident vehicle and the video data taken by the camera of the accident vehicle, and
A step of analyzing the situation of an accident caused by the accident vehicle based on the acquired vehicle data and the video data, and
By comparing the situation of the accident caused by the accident vehicle analyzed in the step of the analysis with the accident case data in which the situation of the accident and the information on the past accident case are associated with each other, the accident vehicle caused the accident. Steps to search for information on past accident cases that correspond to the accident situation, and
A program to execute.