Virtual accident image generating device, virtual accident image generating method and program

Abstract

To provide a virtual accident image generation device or the like capable of motivating a driver to drive better.SOLUTION: A virtual accident image generation device 3 includes: a dangerous driving determination section 22 capable of determining that driving by a driver is dangerous driving based on information related to a vehicle 10 being driven by the driver; a virtual accident object generation section 23 for generating a virtual accident object having a risk to cause an accident with the vehicle 10 if it actually exists on an image of the driver driving the vehicle 10 based on the information related to the vehicle 10 and a result determined by the dangerous driving determination section 22 to indicate that the driving by the driver is the dangerous driving; and a virtual accident image generation section 25 for generating and outputting a virtual accident image when an accident occurs between the vehicle 10 and the virtual accident object based on the virtual accident object generated by the virtual accident object generation section 23.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a virtual accident image generating device, a virtual accident image generating method, and a program. [Background technology]

[0002] 2. Description of the Related Art Known techniques for encouraging drivers to drive safely include techniques for pointing out dangerous driving behavior to drivers while driving and notifying drivers of dangerous road locations.

[0003] For example, Patent Document 1 discloses a driving assistance device that includes a risk history creation unit that creates risk history information including the position of the vehicle at the time the risk avoidance action is detected each time the detection unit detects a risk avoidance action, and a warning unit that issues a warning to the driver urging safe driving when the vehicle is located within a predetermined range from a position included in the risk history information and is heading toward a position included in the risk history information. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2015-219736 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, even if a driver is driving dangerously, if other vehicles and pedestrians take action to avoid the danger, or if there are no other vehicles or pedestrians, and no accident occurs, the driver himself / herself may not be aware that he / she is driving dangerously. In such a case, even if the driver's dangerous driving is pointed out, the driver may not be convinced, and there is a problem that the driver's awareness of safe driving cannot be raised so that the driver's driving can be improved.

[0006] Therefore, the present disclosure provides a virtual accident image generating device, a virtual accident image generating method, and a program that can encourage drivers to be more aware of safe driving. [Means for solving the problem]

[0007] A virtual accident image generating device according to one embodiment of the present disclosure includes a dangerous driving judgment unit that can make a judgment, a virtual accident object generation unit that generates a virtual accident object that could cause an accident with the vehicle if actually present in an image of the driver driving the vehicle based on information about the vehicle and the result of the dangerous driving judgment unit's judgment that the driver's driving is dangerous, and a virtual accident image generation unit that generates and outputs a virtual accident image of an accident between the vehicle and the virtual accident object based on the virtual accident object generated by the virtual accident object generation unit. Effect of the Invention

[0008] The virtual accident image generating device and the like of the present disclosure can encourage the driver to improve his / her driving. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing a virtual accident image generating system according to an embodiment. [Figure 2A] FIG. 2A is a diagram showing a case where dangerous driving is judged. [Figure 2B] FIG. 2B is a diagram showing a virtual accident object image in which a virtual accident object is added to an image determined to be dangerous driving. [Figure 2C] FIG. 2C is a diagram showing a virtual accident image in the case where a virtual accident object and a vehicle cause an accident. [Diagram 3] FIG. 3 is a flowchart showing a vehicle processing operation in the virtual accident image generating system according to the embodiment. [Figure 4]FIG. 4 is a flowchart showing a processing operation of the virtual accident image generation device in the virtual accident image generation system according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component arrangement and connection forms, steps, and order of steps shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components that are not described in the independent claims are described as optional components.

[0011] In addition, each drawing is a schematic diagram and is not necessarily a precise illustration. In addition, in each drawing, the same components are denoted by the same reference numerals.

[0012] Hereinafter, the embodiment will be specifically described with reference to the drawings.

[0013] (Embodiment) <Configuration> First, an embodiment of a virtual accident image generation system 1 including a virtual accident image generation device 3 according to the present embodiment will be described with reference to FIGS. 1 to 2C.

[0014] Fig. 1 is a block diagram showing a virtual accident image generating system 1 according to an embodiment. Fig. 2A is a diagram showing a case where dangerous driving is judged. Fig. 2B is a diagram showing a virtual accident object image in which a virtual accident object is added to an image judged as dangerous driving. Fig. 2C is a diagram showing a virtual accident image in which a virtual accident object and a vehicle 10 cause an accident.

[0015] As shown in FIG. 1, the virtual accident image generation system 1 includes a vehicle 10, a server 2, and a display device 30.

[0016] Vehicle 10 is a vehicle driven by a driver, and when the driver drives vehicle 10, information about vehicle 10 can be obtained. Vehicle 10 is a moving body equipped with wheels capable of traveling on roads or paths, including bicycles, motorbikes, etc. In this embodiment, vehicle 10 is an automobile. The information about vehicle 10 includes information indicating the surrounding environment of vehicle 10 while it is traveling (hereinafter referred to as surrounding environment information), information indicating the traveling state of vehicle 10 (hereinafter referred to as traveling information), and information about the driver who drives vehicle 10 (hereinafter referred to as driver information).

[0017] Specifically, the vehicle 10 has a surrounding environment information acquisition unit 11, a driving information acquisition unit 12, a driver information acquisition unit 13, and a first communication unit 14.

[0018] The surrounding environment information acquisition unit 11 acquires surrounding environment information. For example, the surrounding environment information acquisition unit 11 acquires the surrounding environment information when the vehicle 10 is traveling by the driver, or acquires the surrounding environment information when the vehicle 10 is stationary.

[0019] The surrounding environment information includes surrounding map information of the vehicle 10 and information on objects existing around the vehicle 10. Specifically, the surrounding map information includes the shape of the roads around the vehicle 10, the size, shape, location and number of intersections existing around the vehicle 10, the location and number of traffic lights existing around the vehicle 10, blind spots from the vehicle 10, etc. Furthermore, the information on objects existing around the vehicle 10 includes the location and number of other vehicles existing around the vehicle 10, the location and number of people existing around the vehicle 10, etc.

[0020] The surrounding environment information acquisition unit 11 is an imaging unit such as a drive recorder capable of recording images and sounds of objects present around the vehicle 10, a sensor such as LiDAR (Light Detection And Ranging) capable of detecting the position, distance and number of objects present around the vehicle 10, etc. The surrounding environment information acquisition unit 11 may also include a map information acquisition unit that acquires surrounding map information of the vehicle 10 driven by the driver.

[0021] The surrounding environment information acquisition unit 11 outputs the acquired surrounding map information to the first communication unit 14. In this way, the first communication unit 14 can transmit the surrounding map information to the server 2.

[0022] The travel information acquisition unit 12 acquires travel information. For example, the travel information acquisition unit 12 acquires information related to the travel of the vehicle 10, and information indicating the position of the vehicle 10. Specifically, the information related to the travel of the vehicle 10 includes the travel speed of the vehicle 10, a history of the steering operation by the driver, and the like. In addition, the information indicating the position of the vehicle 10 includes a history of the travel of the vehicle 10, such as GPS (Global Positioning System) information.

[0023] The driving information acquisition unit 12 includes a vehicle speed sensor capable of detecting the driving speed of the vehicle 10, a steering angle sensor capable of detecting the steering operation, a communication unit capable of acquiring GPS information, and the like.

[0024] The travel information acquisition unit 12 outputs the acquired travel information to the first communication unit 14. In this way, the first communication unit 14 can transmit the travel information to the server 2.

[0025] The driver information acquisition unit 13 acquires driver information. The driver information includes information indicating the line of sight of the driver.

[0026] The driver information acquisition unit 13 is a gaze detection sensor or the like capable of detecting the gaze of the driver who drives the vehicle 10.

[0027] The driver information acquisition unit 13 outputs the acquired driver information to the first communication unit 14. In this way, the first communication unit 14 can transmit the driver information to the server 2.

[0028] The first communication unit 14 is mounted on the vehicle 10 and is a wireless communication module capable of wirelessly communicating with the server 2. As described above, the first communication unit 14 transmits the surrounding environment information, the driving information, and the driver information to the server 2.

[0029] The server 2 is an information processing device provided outside the vehicle 10. The server 2 includes a virtual accident image generation device 3. Note that, although FIG. 1 illustrates an example in which the virtual accident image generation device 3 is mounted on the server 2, the present invention is not limited to this. For example, the virtual accident image generation device 3 may be mounted on the vehicle 10.

[0030] When the driver is driving dangerously, the virtual accident image generating device 3 can present the driver with an image for making the driver aware of the dangerous driving. The image to be presented is a moving image or a still image. The image for making the driver aware of the dangerous driving is an image that actually captures the driver driving dangerously, or a virtual image of the same.

[0031] Specifically, the virtual accident image generation device 3 includes a second communication unit 21, a dangerous driving judgment unit 22, a virtual accident target generation unit 23, a fault proportion estimation unit 24, a virtual accident image generation unit 25, and an exemplary driving operation generation unit 26.

[0032] The second communication unit 21 is a wireless communication module capable of wireless communication with the vehicle 10. The second communication unit 21 receives the surrounding environment information, driving information, and driver information transmitted from the vehicle 10. The second communication unit 21 outputs the received surrounding environment information, driving information, and driver information to the dangerous driving determination unit 22 and the virtual accident target generation unit 23.

[0033] The dangerous driving determination unit 22 can determine that the driver's driving is dangerous based on information about the vehicle 10 when the driver drives the vehicle 10. Dangerous driving is driving that has a high possibility of causing an accident, even though no accident has actually occurred. For example, dangerous driving includes driving in which the driver does not check sufficiently, driving while looking away, driving at high speed in a place where people are around, sudden lane changes, sudden acceleration, sudden steering, lane departure, delayed braking, speeding, driving that violates laws and regulations, and the like.

[0034] Specifically, the dangerous driving determination unit 22 determines whether the driving of the vehicle 10 by the driver corresponds to dangerous driving based on the surrounding environment information, driving information, and driver information acquired from the vehicle 10. For example, when the surrounding environment information indicates the presence of a person nearby, and the driver information indicates inattentive driving such as the driver's line of sight directed into the vehicle cabin, the dangerous driving determination unit 22 determines that the driving of the driver is dangerous driving in a scene where the driver drives the vehicle 10 with insufficient confirmation (not slowing down, inattentive driving, etc.) when the surrounding environment information indicates that the surrounding environment has many blind spots. In this way, the dangerous driving determination unit 22 can determine whether the driving of the driver corresponds to dangerous driving for each scene.

[0035] In addition, when the surrounding environment information indicates that people and other vehicles are present near the vehicle 10, the dangerous driving determination unit 22 may determine that the driver's driving is dangerous when the driver drives the vehicle 10. For example, the dangerous driving determination unit 22 may also determine that the driver's driving is dangerous when the surrounding environment information indicates that a person or other vehicle is present near the vehicle 10 and that the person or other vehicle avoids the vehicle 10 driven by the driver to avoid an accident. In addition, the dangerous driving determination unit 22 may determine that the driver's driving is dangerous when the driving speed indicated in the driving information exceeds a predetermined speed. In addition, the dangerous driving determination unit 22 may determine that the driver's driving is dangerous only when the driver information indicates inattentive driving, such as the driver's line of sight being directed into the vehicle cabin.

[0036] The dangerous driving determination unit 22 outputs the result of determining that the driver's driving is dangerous to the virtual accident target generation unit 23.

[0037] The virtual accident target generation unit 23 acquires the result that the driver's driving is judged to be dangerous from the dangerous driving judgment unit 22, and acquires the surrounding environment information, driving information, and driver information from the second communication unit 21. Based on the surrounding environment information, driving information, and driver information, and the result that the dangerous driving judgment unit 22 judges that the driver's driving is dangerous, the virtual accident target generation unit 23 generates and outputs a virtual accident target in Fig. 2B that may cause an accident with the vehicle 10 if it actually exists, according to the image (scene image) in Fig. 2A when the driver is driving the vehicle 10.

[0038] Specifically, the virtual accident object generation unit 23 generates a virtual accident object according to a scene in which the driver is determined to be driving recklessly. For example, the virtual accident object generation unit 23 generates a virtual accident object according to a scene in which a person or another vehicle exists near the vehicle 10, and the behavior of the person or the other vehicle has changed. When the environmental information indicates that a person or another vehicle in the vicinity of the vehicle 10 is moving in a manner that does not avoid the vehicle 10 driven by the driver, the virtual accident object generation unit 23 generates a virtual accident object. In yet another example, a virtual accident object is generated that shows a state in which the vehicle 10 is approaching according to a scene in which the driver is looking away while driving. In another example, a virtual accident object is generated that shows a state in which a virtual accident object jumps out of a blind spot and approaches the vehicle 10 in a scene in which the driver is driving the vehicle 10 without sufficient confirmation.

[0039] In this way, the virtual accident object generation unit 23 generates a virtual accident object based on factors that determine that the driver's driving is dangerous, or generates a virtual accident object in a place where the driver has not confirmed safety. In this embodiment, the virtual accident object generation unit 23 generates a virtual accident object that has a high possibility of causing an accident if an accident object exists, even though no accident has actually occurred.

[0040] Here, the virtual accident object is an object that may cause an accident with the vehicle 10 if it actually exists, and more specifically, an object that virtually reproduces the high possibility of causing an accident if an accident object exists in a scene where a dangerous driving is determined, even though no accident actually occurred. For example, the virtual accident object includes an accident object that virtually reproduces the state where another vehicle and person that the driver can recognize approach the vehicle 10 so as to come into contact with it, and an accident object that virtually reproduces the state where the vehicle 10 and a person are assumed to be hiding in an obstacle as shown in FIG. 2B and approach the vehicle 10 from the blind spot of the obstacle so as to come into contact with it.

[0041] Then, the virtual accident object generating unit 23 outputs to the fault proportion estimating unit 24 a virtual accident object corresponding to the scene determined to be dangerous driving.

[0042] The fault ratio estimation unit 24 estimates the fault ratio that occurs when a virtual accident occurs with the virtual accident object generated by the virtual accident object generation unit 23.

[0043] For example, the fault ratio estimation unit 24 may compare the hypothetical accident with a data table showing the relationship between the preset accident details and the fault ratio to estimate the fault ratio. Specifically, the fault ratio estimation unit 24 estimates the fault ratio for the hypothetical accident using a data table showing the relationship between the preset accident details and the fault ratio. For example, the data table may be stored in a storage unit mounted on the server 2.

[0044] The data table may be set, for example, as follows:

[0045] (1) In the case of an accident at an intersection with traffic lights, where the first vehicle is traveling on one road and the second vehicle is traveling on the other road, and the first vehicle enters the intersection because the light on one road is green, but the second vehicle enters the intersection despite the light on the other road being red and collides with the first vehicle, the second vehicle's share of the fault may be determined to be 100%.

[0046] (2) In the case of an accident at an intersection without traffic lights, where a first vehicle is traveling on one road and a second vehicle is traveling on the other road at the same speed as the first vehicle, and both vehicles enter the intersection and collide with each other, the first vehicle has the right of way to the left when it enters the intersection from the left side relative to the second vehicle, so the fault ratio of the first vehicle may be 40% and the fault ratio of the second vehicle may be 60%.

[0047] (3) At an intersection without traffic lights, when a first vehicle is traveling on one road and a second vehicle is traveling on the other one-way road, when the first vehicle enters the intersection, If an accident involves two vehicles violating a one-way street and entering an intersection, resulting in a collision between the two vehicles, the first vehicle's share of the fault may be determined to be 20% and the second vehicle's share of the fault may be 80%.

[0048] (4) In the case of an accident at an intersection without traffic lights, where a first vehicle is traveling on one wide road and a second vehicle is traveling on the other narrow road, and both vehicles enter the intersection and collide with each other, the fault of the first vehicle may be determined to be 30% and the fault of the second vehicle may be determined to be 70%.

[0049] (5) In the case of an accident at an intersection without traffic lights, where there is a stop sign on one road and a first vehicle is traveling on the other road without a stop sign, and a second vehicle traveling on the road with a stop sign enters the intersection and collides with the first vehicle, the second vehicle's share of the fault may be determined to be 80% and the first vehicle's share of the fault may be determined to be 20%.

[0050] (6) In the case of an accident at an intersection without traffic lights, where a first vehicle is traveling on one road, which is a priority road, and a second vehicle is traveling on the other road, which is a non-priority road, and both vehicles enter the intersection and collide with each other, the fault ratio of the first vehicle may be determined to be 10% and the fault ratio of the second vehicle may be determined to be 90%.

[0051] In this way, the data table may set the fault ratio appropriately depending on the accident details.

[0052] For example, the fault ratio estimation unit 24 may compare a hypothetical accident with an actual accident that has occurred in the past. Therefore In this case, the fault ratio estimation unit 24 uses a plurality of historical data showing the relationship between the contents of an accident that actually occurred in the past and the fault ratio thereof. Specifically, the fault ratio estimation unit 24 uses a plurality of historical data showing the relationship between the contents of an accident that actually occurred in the past and the fault ratio thereof. Therefore The fault ratio estimation unit 24 may estimate a fault ratio that is a value obtained by adjusting the fault ratio indicated in the extracted history data based on the similarity between the virtual accident and the actual accident. The fault ratio estimation unit 24 may also extract history data that is most similar to the virtual accident, and estimate the fault ratio indicated in the extracted history data as the fault ratio caused by the virtual accident.

[0053] The fault proportion estimation unit 24 outputs the estimated fault proportion to the virtual accident image generation unit 25. The fault proportion estimation unit 24 may also output the estimated fault proportion to the display device 30. This allows the display device 30 to display the estimated fault proportion. For example, the display device 30 may display "vehicle fault proportion 〇%" or "virtual accident object fault proportion ▽%."

[0054] The virtual accident image generation unit 25 acquires a virtual accident object from the virtual accident object generation unit 23 via the fault proportion estimation unit 24. The virtual accident image generation unit 25 generates and outputs a virtual accident image in the case where an accident occurs between the vehicle 10 and the virtual accident object, based on at least the virtual accident object generated by the virtual accident object generation unit 23. The virtual accident image generation unit 25 can also generate and output a virtual accident image based on information related to the vehicle and the virtual accident object.

[0055] Specifically, as shown in FIG. 2C, the virtual accident image generating unit 25 generates a virtual accident image showing a state in which a virtual accident occurs between a virtual accident object and the vehicle 10.

[0056] For example, the virtual accident image generating unit 25 may use a scene image determined to be dangerous driving based on the surrounding environment information, the driving information, and the driver information. The scene image may be a virtually reproduced image imitating the entire surrounding environment, or may be a moving image of actual driving shown in the surrounding environment information acquired by the surrounding environment information acquiring unit 11. Specifically, When the driver is engaged in inattentive driving, which is dangerous driving, the virtual accident image generating unit 25 may generate a scene image showing the driver inattentive driving. In yet another example, when the vehicle 10 is traveling in an area with many blind spots, the virtual accident image generating unit 25 may generate a scene image showing the driver driving the vehicle 10 without sufficient confirmation, as shown in Fig. 2B. The virtual accident image generating unit 25 may add a virtual accident object to such a scene image, and generate a virtual accident image showing a virtual accident between the virtual accident object and the vehicle 10, as shown in Fig. 2C.

[0057] The field of view shown by the virtual accident image may be, for example, a bird's-eye view, an image seen by people around the vehicle 10, an image seen by a driver in another vehicle, an image seen by a driver inside the vehicle, or the like.

[0058] The virtual accident image generating unit 25 may further use information about the vehicle 10 to generate and output an image showing the behavior of the driver when the driver performs dangerous driving. In this case, when generating a virtual accident image, the virtual accident image generating unit 25 may add a virtual image obtained by cutting out the field of view of the driver when the dangerous driving is determined. The virtual accident image generating unit 25 may also display on the display device 30 a virtual image obtained by cutting out the field of view of the driver together with a bird's-eye view. This is expected to make the driver aware that he or she was driving dangerously.

[0059] Furthermore, when the fault ratio estimated by the fault ratio estimation unit 24 is higher than a predetermined value, the virtual accident image generation unit 25 generates and outputs a virtual accident image. Furthermore, when the fault ratio estimated by the fault ratio estimation unit 24 is equal to or lower than a predetermined value, the virtual accident image generation unit 25 does not need to generate a virtual accident image. Note that whether the fault ratio estimated by the fault ratio estimation unit 24 is higher than the predetermined value may be determined by the virtual accident image generation unit 25 and the model driving performance generation unit 26, or may be determined by the fault ratio estimation unit 24. The predetermined value is a value set in advance and can be set arbitrarily.

[0060] The virtual accident image generating unit 25 outputs the generated virtual accident image to the display device 30.

[0061] The model driving performance generating unit 26 acquires the virtual accident object from the virtual accident object generating unit 23 via the fault proportion estimating unit 24. When the fault proportion estimated by the fault proportion estimating unit 24 is higher than a predetermined value, the model driving performance generating unit 26 generates and outputs an model driving image that shows model driving to the driver, based on at least the virtual accident object. The model driving performance generating unit 26 may also generate and output an model driving image based on information related to the vehicle and the virtual accident object.

[0062] Specifically, the model driving performance generating unit 26 generates a model driving image that shows model driving to the driver, and outputs it to the display device 30, in order to show the driver how to drive the driver who has been determined to be driving dangerously. For example, the model driving performance generating unit 26 generates a model driving image that corrects the driver's driving operations such as steering, accelerator, and brake operations that are model driving, and outputs it to the display device 30. In yet another example, the model driving performance generating unit 26 generates a model driving image to point out to the driver problems such as insufficient checking, inattentive driving, high-speed driving in a place where people are around, sudden lane changes, lane departure, and speeding, and outputs it to the display device 30. In yet another example, when the driver is performing an incorrect steering operation, the model driving performance generating unit 26 generates a model driving image that corrects the steering angle so that the steering operation is correct, and outputs it to the display device 30. In yet another example, when the driver's braking timing is delayed, the model driving performance generating unit 26 generates a model driving image that shows appropriate braking timing and outputs it to the display device 30. For example, when the driver is actually driving the vehicle 10, the model driving performance generating unit 26 generates a model driving image that shows appropriate braking timing and outputs it to the display device 30. In this case, a message or icon urging the driver to brake may be displayed on the screen of the display device 30. Note that the message or icon may be displayed on the screen of the display device 30 only when the driver drives dangerously, and is not limited to when the driver brakes late.

[0063] Furthermore, the virtual accident image generating device 3 may record an image of the driver when he or she engaged in dangerous driving and an image of the driver after the driver has improved his or her driving after viewing the model driving image. In this case, the virtual accident image generating device 3 may display the image of the driver when he or she engaged in dangerous driving and the image of the driver after the driver's driving has improved side by side on the display device 30. This allows the driver to know that his or her driving has improved.

[0064] Furthermore, in the virtual accident image generating device 3, the driving behavior of a skilled driver may be recorded in advance. In this case, images of the driving behavior of a plurality of skilled drivers may be stored in the storage unit so as to correspond to various scenes. The model driving performance generating unit 26 may display, on the display device 30, a model driving image using an image of a scene similar to the scene in which the driver performed dangerous driving (an image of the driving behavior of a skilled driver). Furthermore, the model driving performance generating unit 26 may virtually reproduce the scene in which the driver performed dangerous driving as the model driving image, thereby immersing the driver in the virtual scene and virtually teaching driving operations.

[0065] In some cases, the model driving performance generator 26 may forcibly control the vehicle 10 by outputting a control signal for controlling the vehicle 10 to an ECU (Electronic Control Unit) or the like of the vehicle 10. The model driving performance generating unit 26 may apply the brakes of the vehicle 10 or may vibrate the driver's seat to notify the driver of the dangerous driving. Furthermore, if the vehicle 10 is speeding, the model driving performance generating unit 26 may forcibly release the accelerator pedal by outputting a control signal for controlling the vehicle 10 to the ECU or the like of the vehicle 10.

[0066] The display device 30 can display virtual accident objects, fault ratios, virtual accident images, and model driving images. These images displayed by the display device 30 include a bird's-eye view, an image seen from the perspective of people around the vehicle 10, an image seen from the perspective of a driver in another vehicle, an image seen from the driver inside the vehicle, and the like. These images displayed by the display device 30 may be, for example, all images generated by computer graphics, or may be images in which virtual accident objects are added to an image (scene image) of the driver driving the vehicle 10.

[0067] The display device 30 is, for example, an electronic mirror, a HUD (Head-Up Display), a car navigation system, The display device 30 may be an application, a smartphone, a driving simulator, etc. The display device 30 may be mounted on the vehicle 10 or may be installed in a facility.

[0068] <Processing Operation> Next, the processing operation of the virtual accident image generating device 3 in this embodiment will be described.

[0069] First, the processing operation of the vehicle 10 will be described with reference to FIG.

[0070] FIG. 3 is a flowchart showing the processing operation of the vehicle 10 in the virtual accident image generation system 1 according to the embodiment.

[0071] 3, the surrounding environment information acquisition unit 11 acquires surrounding environment information (S11). The surrounding environment information acquisition unit 11 outputs the acquired surrounding map information to the first communication unit .

[0072] Next, the travel information acquisition unit 12 acquires travel information (S12). The acquired driving information is output to the first communication unit 14.

[0073] Next, the driver information acquisition unit 13 acquires the driver information (S13). The driver information acquisition unit 13 outputs the acquired driver information to the first communication unit .

[0074] The processing order of steps S11 to S13 may be rearranged or may be performed in parallel.

[0075] The first communication unit 14 transmits the surrounding environment information, the traveling information, and the driver information to the server 2 (S14). Note that the first communication unit 14 may transmit the surrounding environment information, the traveling information, and the driver information to the server 2 each time the surrounding environment information, the traveling information, and the driver information are acquired, or may transmit the surrounding environment information, the traveling information, and the driver information to the server 2 all at once.

[0076] Then, the processing operation of the vehicle 10 in FIG. 3 ends.

[0077] Next, the processing operation of the virtual accident image generating device 3 in the server 2 will be described with reference to FIG.

[0078] FIG. 4 is a flowchart showing a processing operation of the virtual accident image generation device 3 in the virtual accident image generation system 1 according to the embodiment.

[0079] 4, the second communication unit 21 receives the surrounding environment information, the driving information, and the driver information from the vehicle 10, and the server 2 acquires the surrounding environment information, the driving information, and the driver information (S21). The second communication unit 21 outputs the surrounding environment information, the driving information, and the driver information to the dangerous driving determination unit 22 and the virtual accident target generation unit 23.

[0080] Next, the dangerous driving determination unit 22 determines whether or not the driver's driving of the vehicle 10 corresponds to dangerous driving based on the surrounding environment information, the driving information, and the driver information acquired from the second communication unit 21 (S22). When the dangerous driving determination unit 22 determines that the driver's driving of the vehicle 10 does not correspond to dangerous driving (NO in S22), it ends the flowchart of FIG.

[0081] On the other hand, if the dangerous driving judgment unit 22 judges that the driver's driving of the vehicle 10 corresponds to dangerous driving (YES in S22), it outputs the result that the driver's driving is dangerous driving to the virtual accident target generation unit 23.

[0082] Next, the virtual accident object generation unit 23 acquires the result that the driver's driving is judged to be dangerous from the dangerous driving judgment unit 22, and acquires the surrounding environment information, the driving information, and the driver information from the second communication unit 21. Based on the information about the vehicle 10 and the result that the dangerous driving judgment unit 22 judges that the driver's driving is dangerous, the virtual accident object generation unit 23 generates a virtual accident object corresponding to an image when the driver is driving the vehicle 10 dangerously, and outputs the generated virtual accident object to the fault proportion estimation unit 24 (S23).

[0083] Next, the fault proportion estimation unit 24 acquires the virtual accident object from the virtual accident object generation unit 23. The fault proportion estimation unit 24 estimates the fault proportion that occurs when a virtual accident occurs with the virtual accident object generated by the virtual accident object generation unit 23 (S24).

[0084] Next, the fault ratio estimation unit 24 judges whether the estimated fault ratio is higher than a predetermined value (S25). When the fault ratio estimation unit 24 judges that the estimated fault ratio is equal to or lower than the predetermined value (NO in S25), the flow chart of FIG. 4 is terminated.

[0085] On the other hand, if the fault proportion estimation unit 24 determines that the estimated fault proportion is higher than a predetermined value (YES in S25), the fault proportion estimation unit 24 outputs a virtual accident object to the virtual accident image generation unit 25 and the exemplary driving operation generation unit 26.

[0086] Next, when the fault ratio estimated by the fault ratio estimation unit 24 is higher than a predetermined value, the virtual accident image generation unit 25 generates a virtual accident image based on at least the virtual accident object, and outputs it to the display device 30 (S26). The display device 30 displays the virtual accident image shown in Fig. 2C. At this time, the virtual accident image generation unit 25 may cause the display device 30 to display the fault ratio estimated by the fault ratio estimation unit 24.

[0087] Next, when the fault ratio estimated by the fault ratio estimation unit 24 is higher than a predetermined value, the model driving performance generation unit 26 generates a model driving image that shows model driving to the driver based on at least the virtual accident object, and outputs the model driving image to the display device 30 (S27). The display device 30 displays the model driving image.

[0088] Then, the processing operation of the server 2 in Fig. 4 is completed. Note that the processing in Fig. 3 and Fig. 4 is repeated.

[0089] <Action and effect> The effects of the virtual accident image generating device 3, the virtual accident image generating method, and the program according to this embodiment will be described below.

[0090] As described above, the virtual accident image generating device 3 of this embodiment includes a dangerous driving judgment unit 22 that is capable of determining that the driver's driving is dangerous based on information about the vehicle 10 driven by the driver, a virtual accident object generation unit 23 that generates a virtual accident object in an image when the driver is driving the vehicle 10, which may cause an accident with the vehicle 10 if it actually exists, based on information about the vehicle 10 and the result of the dangerous driving judgment unit 22 determining that the driver's driving is dangerous, and a virtual accident image generation unit 25 that generates and outputs a virtual accident image in the event that an accident occurs between the vehicle 10 and the virtual accident object, based on the virtual accident object generated by the virtual accident object generation unit 23.

[0091] According to this, it is possible to determine whether the driver is driving recklessly, and therefore it is possible to inform the driver that the driver's driving has been reckless by using the virtual accident object.

[0092] In addition, since a virtual accident image can be generated when a virtual accident object and the vehicle 10 cause an accident, the driver can look back on what went wrong with the dangerous driving he or she performed and recognize what kind of accidents may occur due to dangerous driving. For example, by presenting the driver with a virtual accident object that shows a case where another vehicle does not avoid the dangerous driving, or a case where a person jumps out from a blind spot, the driver can be made to recognize what kind of accident may develop. Therefore, the driver can be made to understand the situation of the dangerous driving that the driver actually performed, so that the driver can easily recognize that his or her driving was dangerous even if an accident does not actually occur.

[0093] Therefore, the virtual accident image generating device 3 can encourage drivers to increase their awareness of safe driving. This is expected to increase the awareness of safe driving, making drivers reflect on their dangerous driving and strive to drive safely. As a result, the increase in awareness of safe driving in society can suppress human losses, property losses, rising insurance premiums, and increased expenses due to property losses.

[0094] In addition, the virtual accident image generating method of this embodiment includes determining that the driver's driving is dangerous based on information about the vehicle 10 driven by the driver, generating a virtual accident object in an image of the driver driving the vehicle 10 when it is actually present, which may cause an accident with the vehicle 10, based on the information about the vehicle 10 and the result of the determination that the driver's driving is dangerous, and generating and outputting a virtual accident image in which an accident occurs between the vehicle 10 and the virtual accident object, based on the generated virtual accident object.

[0095] This method also provides the same effects as those described above.

[0096] Moreover, the program of the present embodiment is a program for causing a computer to execute the virtual accident image generating method.

[0097] This program also provides the same effects as those described above.

[0098] In addition, in the virtual accident image generating device 3 of this embodiment, the information regarding the vehicle 10 includes information indicating the surrounding environment of the vehicle 10, information indicating the driving state of the vehicle 10, and information regarding the driver driving the vehicle 10.

[0099] This makes it possible to generate a realistic virtual accident image, which can encourage drivers to be more conscious of safe driving.

[0100] In addition, the virtual accident image generating device 3 of this embodiment further includes a fault ratio estimation unit 24 that estimates the fault ratio that occurs when a virtual accident occurs with the virtual accident object generated by the virtual accident object generation unit 23.

[0101] This allows the driver to estimate the degree of fault in a hypothetical accident, and thus allows the driver to recognize that his driving was dangerous and the degree of fault associated with it. Therefore, if the driver were to cause an accident, he or she would be able to grasp the degree of fault.

[0102] In addition, since the system estimates the degree of fault when a virtual accident occurs, it is less likely that the system will estimate the degree of fault when the driver is not at fault, resulting in the display of a virtual accident image. This makes it possible to prevent the driver's sense of satisfaction from decreasing.

[0103] In the virtual accident image generating device 3 of the present embodiment, the fault ratio estimating unit 24 compares a virtual accident with an actually occurring accident. Similar to The degree of fault is estimated based on the degree of similarity.

[0104] This allows for more accurate estimation of the percentage of fault in the event of a hypothetical accident.

[0105] Moreover, in the virtual accident image generating device 3 of this embodiment, the fault proportion estimating section 24 estimates the fault proportion based on the virtual accident and a preset fault proportion of the accident.

[0106] This makes it easy to estimate the percentage of fault in the event of a hypothetical accident.

[0107] In the virtual accident image generating device 3 of the present embodiment, the virtual accident image generating unit 25 Furthermore, using information about the vehicle 10, an image showing the behavior of the driver when the driver engages in dangerous driving is generated and output.

[0108] According to this, an image showing the driver's behavior when driving recklessly can be presented to the driver, so that the driver can recognize what was wrong with the reckless driving he or she engaged in. This can encourage the driver to be more conscious of safe driving.

[0109] Furthermore, in the virtual accident image generating device 3 of this embodiment, the virtual accident image generating unit 25 generates and outputs a virtual accident image when the fault ratio estimated by the fault ratio estimating unit 24 is higher than a predetermined value.

[0110] For example, if a virtual accident image in which the driver's share of the fault is small is generated and presented to the driver, the driver will find it difficult to recognize that there is something wrong with him or her, and will be less convinced by the virtual accident image.

[0111] However, according to the present embodiment, a virtual accident image is generated when the driver is highly at fault, so the driver can recognize what kind of accident may occur due to his / her own reckless driving. This can encourage the driver to be more conscious of safe driving.

[0112] In addition, the virtual accident image generation device 3 of this embodiment further includes an exemplary driving operation generation unit 26 that generates and outputs an exemplary driving image that shows exemplary driving to the driver when the dangerous driving judgment unit 22 judges that the driver's driving is dangerous.

[0113] According to this, by presenting the model driving image to the driver, the driver's dangerous driving can be improved, and it is expected that the driver's awareness of safe driving will be enhanced so that the driver will strive to drive safely.

[0114] (Other variations, etc.) Although the present disclosure has been described above based on the embodiments, the present disclosure is not limited to these embodiments.

[0115] For example, in the virtual accident image generating device etc. of the present embodiment, the dangerous driving judgment unit is mounted on the server, but may be mounted on the vehicle. Furthermore, the virtual accident target generating unit may be mounted on the vehicle. Furthermore, the fault ratio estimating unit may be mounted on the vehicle. Furthermore, the virtual accident image generating unit may be mounted on the vehicle. Furthermore, the model driving operation generating unit may be mounted on the vehicle. Therefore, the virtual accident image generating device may be mounted on the vehicle. In this case, the fault ratio, the virtual accident image, the model driving image, etc. can be displayed on the display device mounted on the vehicle, so that it is not necessary to transmit each piece of information to the server. As a result, it is possible to suppress an increase in the communication load between the vehicle and the server.

[0116] In addition, the virtual accident image generating device or the like in this embodiment may measure the number of dangerous driving incidents. In this case, if the number of dangerous driving incidents in a predetermined period is equal to or greater than a threshold, this may be reflected in the driver's insurance premium. In other words, if the number of dangerous driving incidents is equal to or greater than a threshold, the driver's insurance premium may be increased.

[0117] Furthermore, in the virtual accident image generating device etc. according to the present embodiment, the virtual accident image may be provided to the National Police Agency, driving schools etc. In this case, the National Police Agency and driving schools can use the virtual accident image to promote safe driving.

[0118] In addition, the virtual accident image generating device or the like in this embodiment may include a storage unit that stores the virtual accident object, the fault ratio, the virtual accident image, and the model driving image. In this case, the storage unit may store the virtual accident object, the fault ratio, the virtual accident image, and the model driving image in association with information related to the vehicle. The storage unit may aggregate the virtual accident object, the fault ratio, the virtual accident image, and the model driving image that are similar scenes into one.

[0119] In addition, each of the components included in the virtual accident image generating device and the like in this embodiment is typically realized as an LSI, which is an integrated circuit. These may be individually implemented as single chips, or may be integrated into a single chip so as to include some or all of them.

[0120] The integrated circuit is not limited to an LSI, but may be realized by a dedicated circuit or a general-purpose processor. A field programmable gate array (FPGA) that can be programmed after the LSI is manufactured, or a reconfigurable processor that can reconfigure the connections and settings of the circuit cells inside the LSI may also be used.

[0121] In the above embodiment, each component may be implemented by dedicated hardware or by executing a software program suitable for each component. Each component may be implemented by a program execution unit such as a CPU or processor reading and executing a software program recorded on a storage medium such as a hard disk or semiconductor memory.

[0122] Furthermore, all the numbers used above are merely examples for the purpose of specifically explaining the present disclosure, and the embodiments of the present disclosure are not limited to the numbers exemplified.

[0123] In addition, the division of functional blocks in the block diagram is an example, and multiple functional blocks may be realized as one functional block, one functional block may be divided into multiple blocks, or some functions may be transferred to another functional block. Furthermore, the functions of multiple functional blocks having similar functions may be processed in parallel or in a time-sharing manner by a single piece of hardware or software.

[0124] In addition, the order in which the steps in the flowchart are executed is merely for illustrative purposes and may be other than the above. In addition, some of the steps may be executed simultaneously (in parallel) with other steps.

[0125] In addition, the present disclosure also includes forms obtained by applying various modifications to the embodiments that a person skilled in the art may think of, and forms realized by arbitrarily combining the components and functions of the embodiments within the scope that does not deviate from the spirit of the present disclosure.

[0126] The following describes features of the virtual accident image generating device, the virtual accident image generating method, and the program described based on the above embodiment.

[0127] <Technology 1> A dangerous driving determination unit capable of determining that a driver's driving is dangerous based on information about a vehicle driven by the driver; a virtual accident object generating unit that generates a virtual accident object that may cause an accident with the vehicle if it actually exists in an image of the driver driving the vehicle, based on information about the vehicle and a result of the dangerous driving determination unit determining that the driver is driving the vehicle at risk; Based on the virtual accident object generated by the virtual accident object generation unit, a virtual accident image generating unit that generates and outputs a virtual accident image in the case where an accident occurs between the virtual accident object and the vehicle. Virtual accident image generator.

[0128] <Technology 2> The information about the vehicle includes information about the surrounding environment of the vehicle, information about the driving state of the vehicle, and information about the driver who drives the vehicle. The virtual accident image generating device according to technology 1.

[0129] <Technology 3> The system further includes a fault ratio estimation unit that estimates a fault ratio that occurs when a virtual accident occurs with the virtual accident object generated by the virtual accident object generation unit. 3. The virtual accident image generating device according to claim 1 or 2.

[0130] <Technology 4> The fault ratio estimation unit compares the hypothetical accident with an actual accident. Therefore Estimate the degree of fault based on the similarity of The virtual accident image generating device according to technology 3.

[0131] <Technology 5> The fault ratio estimation unit estimates a fault ratio based on the hypothetical accident and a preset fault ratio of the accident. The virtual accident image generating device according to technology 3.

[0132] <Technology 6> The virtual accident image generating unit further uses information about the vehicle to generate and output an image showing the behavior of the driver when the driver performs the dangerous driving. The virtual accident image generating device according to any one of the first to fifth aspects.

[0133] <Technology 7> The virtual accident image generating unit generates and outputs the virtual accident image when the fault ratio estimated by the fault ratio estimating unit is higher than a predetermined value. The virtual accident image generating device according to any one of the third to fifth aspects.

[0134] <Technology 8> The vehicle further includes an exemplary driving operation generating unit that generates and outputs an exemplary driving image that shows exemplary driving to the driver when the dangerous driving determination unit determines that the driving of the driver is the dangerous driving. The virtual accident image generating device according to any one of the first to seventh aspects.

[0135] <Technology 9> Determining that a driver's driving is dangerous based on information about a vehicle driven by the driver; generating a virtual accident object that may cause an accident with the vehicle if it actually exists in an image of the driver driving the vehicle based on information about the vehicle and a result of determining that the driver's driving is dangerous; and generating and outputting a virtual accident image in the case where an accident occurs between the vehicle and the virtual accident object based on the generated virtual accident object. A method for generating virtual accident images.

[0136] <Technology 10> A method for causing a computer to execute the virtual accident image generation method described in Technology 9. program. [Industrial Applicability]

[0137] The present disclosure is applicable to, for example, vehicles, driving simulators, and the like. [Explanation of symbols]

[0138] 3. Virtual Accident Image Generation Device 10 Vehicles 22 Dangerous Driving Judgment Unit 23 Virtual Accident Target Generation Unit 24 Fault ratio estimation section 25 Virtual Accident Image Generation Unit 26 Model driving operation generation unit

Claims

1. A dangerous driving determination unit capable of determining that the driving of the driver is dangerous driving based on information about the vehicle driven by the driver, a virtual accident object generation unit that generates a virtual accident object that may actually cause an accident with the vehicle in an image when the driver is driving the vehicle, based on the information about the vehicle and the result determined by the dangerous driving determination unit that the driving of the driver is the dangerous driving, and a virtual accident image generation unit that generates and outputs a virtual accident image when an accident occurs between the vehicle and the virtual accident object based on the virtual accident object generated by the virtual accident object generation unit. A virtual accident image generation device.

2. The information about the vehicle includes information indicating the surrounding environment of the vehicle, information indicating the driving state of the vehicle, and information about the driver driving the vehicle. The virtual accident image generation device according to Claim 1.

3. Furthermore, it includes a loss ratio estimation unit that estimates the loss ratio that occurs when a virtual accident occurs between the virtual accident object generated by the virtual accident object generation unit. The virtual accident image generation device according to Claim 1.

4. The loss ratio estimation unit estimates the loss ratio based on the similarity between the virtual accident and an actually occurred accident. The virtual accident image generation device according to Claim 3.

5. The loss ratio estimation unit estimates the loss ratio based on the virtual accident and a preset loss ratio of an accident. The virtual accident image generation device according to Claim 3.

6. The virtual accident image generation unit further generates and outputs an image showing the behavior of the driver when the driver performs the dangerous driving, using the information about the vehicle. The virtual accident image generation device according to any one of Claims 1 to 5.

7. When the loss ratio estimated by the loss ratio estimation unit is higher than a predetermined value, the virtual accident image generation unit generates and outputs the virtual accident image. The virtual accident image generation device according to any one of Claims 3 to 5.

8. Furthermore, when the dangerous driving determination unit determines that the driving of the driver is the dangerous driving, it includes a model driving operation generation unit that generates and outputs a model driving image that becomes a model driving for the driver. The virtual accident image generation device according to any one of Claims 1 to 5.

9. A virtual accident image generation method executed by a computer, Based on information about the vehicle driven by the driver, determining that the driving of the driver is dangerous driving; Based on the information about the vehicle and the result of determining that the driving of the driver is the dangerous driving, generating a virtual accident object that may cause an accident with the vehicle if it actually exists in the image when the driver is driving the vehicle; Generating and outputting a virtual accident image in the case where an accident occurs between the vehicle and the virtual accident object based on the generated virtual accident object. Virtual accident image generation method.

10. For causing a computer to execute the virtual accident image generation method according to Claim 9 Program.

Images