Vehicle inspection system and vehicle inspection method

Abstract

To provide a technique capable of identifying an anomaly factor portion causing an optical axis anomaly when the optical axis anomaly of a camera is detected.SOLUTION: A vehicle inspection system acquires a first camera image captured by a first camera mounted on the front portion of a vehicle while the vehicle is traveling and a second camera image captured by a second camera mounted on the rear portion of the vehicle. The vehicle inspection system also calculates a first optical axis coordinate value from the first camera image and a second optical axis coordinate value from the second camera image. Further, the vehicle inspection system determines whether or not there is an anomaly in each of the first optical axis coordinate value and the second optical axis coordinate value. Furthermore, the vehicle inspection system identifies an anomaly factor portion causing an anomaly in one or both of the first optical axis coordinate value and the second optical axis coordinate value from among the first camera, the second camera, and a vehicle body of the vehicle, on the basis of a combination of the presence or absence of the anomaly in the first optical axis coordinate value and the presence or absence of the anomaly in the second optical axis coordinate value.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to an inspection system for inspecting a vehicle. [Background technology]

[0002] Patent Document 1 discloses an exterior monitoring device that can detect misalignment of the optical axis of a camera (optical axis abnormality) in real time under a driving environment. In this technology, a white line is approximated by a linear or higher approximation equation based on an image captured in front of the vehicle. Then, based on the constants of the approximation equation, a change in the lateral position of the vehicle with respect to the white line when the vehicle has traveled for a set time (first lateral movement amount) is calculated. Also, based on a linear coefficient of the approximation equation, a change in the lateral position of the vehicle with respect to the white line when the vehicle has traveled for a set time (second lateral movement amount) is calculated. Furthermore, if the difference between the first lateral movement amount and the second lateral movement amount is equal to or greater than a preset threshold, it is determined that an abnormality in the optical axis of the camera in the horizontal direction has occurred. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2015-191548 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, when an abnormality in the optical axis of a camera is detected, the cause of the abnormality may be a malfunction of the camera or a misalignment of the camera mounted on the vehicle. In this case, it is difficult for the dealer to identify the abnormality causing the abnormality, and the time required for vehicle diagnosis may be extended. In addition, there is a risk that repairs may be made to parts that do not need repairs.

[0005] One object of the present disclosure is to provide a technology that, when an abnormality in the optical axis of a camera is detected, can identify the location of the abnormality that is causing the abnormality in the optical axis. [Means for solving the problem]

[0006] A first aspect of the present disclosure relates to a vehicle inspection system for inspecting a vehicle. The vehicle inspection system includes a computer connected to a first camera mounted in front of the vehicle and a second camera mounted in the rear of the vehicle. The computer acquires a first camera image taken by the first camera while the vehicle is traveling, and acquires a second camera image taken by the second camera simultaneously with the first camera image being taken by the first camera. The computer also calculates a first optical axis coordinate value from the first camera image and a second optical axis coordinate value from the second camera image. The computer further determines the presence or absence of an abnormality for each of the first optical axis coordinate value and the second optical axis coordinate value. The computer is further configured to identify an abnormality factor location causing an abnormality in one or both of the first optical axis coordinate value and the second optical axis coordinate value from among the first camera, the second camera, and the vehicle body, based on a combination of the presence or absence of an abnormality in the first optical axis coordinate value and the presence or absence of an abnormality in the second optical axis coordinate value.

[0007] A second aspect of the present disclosure relates to a vehicle inspection method for inspecting a vehicle, the vehicle inspection method including: acquiring a first camera image captured by a first camera mounted on the front of the vehicle while the vehicle is traveling, and acquiring a second camera image captured by a second camera mounted on the rear of the vehicle simultaneously with the first camera image captured by the first camera, calculating a first optical axis coordinate value from the first camera image, calculating a second optical axis coordinate value from the second camera image, determining the presence or absence of an abnormality for each of the first optical axis coordinate value and the second optical axis coordinate value, and identifying an abnormality factor location causing an abnormality in one or both of the first optical axis coordinate value and the second optical axis coordinate value from among the first camera, the second camera, and a body of the vehicle based on a combination of the presence or absence of an abnormality in the first optical axis coordinate value and the presence or absence of an abnormality in the second optical axis coordinate value. Effect of the Invention

[0008] According to the present disclosure, the vehicle inspection system acquires the first optical axis coordinate value and the second optical axis coordinate value based on the first camera image and the second camera image acquired while the vehicle is traveling. The vehicle inspection system also determines the presence or absence of an abnormality for each of the first optical axis coordinate value and the second optical axis coordinate value. Furthermore, the vehicle inspection system identifies an abnormality factor part that is causing an abnormality in one or both of the first optical axis coordinate value and the second optical axis coordinate value from among the first camera, the second camera, and the vehicle body based on a combination of the presence or absence of an abnormality in the first optical axis coordinate value and the presence or absence of an abnormality in the second optical axis coordinate value. As a result, even if an optical axis abnormality occurs, the abnormality factor part is identified in a short time, and further, repairs are limited to the parts that need repair. [Brief description of the drawings]

[0009] [Figure 1] 1 is a block diagram showing an overview and a specific example of a vehicle inspection system according to a first embodiment. [Diagram 2] 4 is a flowchart showing an example of processing of the vehicle inspection system according to the first embodiment. [Diagram 3] 11 is a block diagram showing an overview and a specific example of a vehicle inspection system according to a second embodiment. FIG. [Figure 4] 10 is a flowchart showing an example of processing of the vehicle inspection system according to the second embodiment. [Diagram 5] 11 is a block diagram showing an overview and a specific example of a vehicle inspection system according to a third embodiment. FIG. [Figure 6] 11 is a flowchart showing an example of processing of a vehicle inspection system according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] A vehicle inspection system and a vehicle inspection method according to an embodiment of the present disclosure will be described with reference to the accompanying drawings.

[0011] 1. First embodiment 1-1. Overview 1 is a block diagram showing an overview and a specific example of a vehicle inspection system 10 according to a first embodiment. As shown in FIG. 1(A), the vehicle inspection system 10 is mounted on a vehicle 1 and inspects the vehicle 1. The vehicle 1 on which the vehicle inspection system 10 is mounted may be an autonomous vehicle. The vehicle inspection system 10 includes a first camera 20, a second camera 30, and a computer 100.

[0012] The first camera 20 is mounted on the front of the vehicle 1 and detects the situation around the vehicle 1. The second camera 30 is mounted on the rear of the vehicle 1 and detects the situation around the vehicle 1.

[0013] The computer 100 is connected to at least the first camera 20 and the second camera 30. The computer 100 includes one or more processors 110 (hereinafter simply referred to as the processor 110) and one or more storage devices 120 (hereinafter simply referred to as the storage device 120). The processor 110 executes various processes. For example, the processor 110 includes a CPU (Central Processing Unit). The storage device 120 stores various information. Examples of the storage device 120 include a volatile memory, a non-volatile memory, a HDD (Hard Disk Drive), and an SSD (Solid State Drive). Typically, the computer 100 is mounted on the vehicle 1.

[0014] The storage device 120 stores a vehicle inspection program 121, first camera acquired information 122, second camera acquired information 123, and the like.

[0015] The vehicle inspection program 121 is a computer program for inspecting the vehicle 1. The processor 110 executes the vehicle inspection program 121 to realize various processes by the computer 100. The vehicle inspection program 121 may be recorded in a recording medium readable by the computer 100.

[0016] The first camera acquired information 122 includes a camera image (hereinafter referred to as a first camera image) captured by the first camera 20 while the vehicle 1 is traveling. The second camera acquired information 123 includes a camera image (hereinafter referred to as a second camera image) captured by the second camera 30 while the vehicle 1 is traveling. The second camera image is captured by the second camera 30 at the same time as the first camera image is captured by the first camera 20.

[0017] Furthermore, the first camera acquired information 122 and the second camera acquired information 123 include object information related to objects around the vehicle 1. Examples of objects around the vehicle 1 include pedestrians, other vehicles, white lines, road structures, obstacles, etc. As another example, objects can be recognized and identified by analyzing images obtained by the cameras.

[0018] 1-2.Specific examples 1-2-1. Example of determining whether or not there is an abnormality in the optical axis Processor 110 determines whether or not there is an abnormality in the optical axes of first camera 20 and second camera 30. An example of determining whether or not there is an abnormality in the optical axes will be described in detail below.

[0019] The processor 110 acquires the first camera acquired information 122 and the second camera acquired information 123, and calculates the optical axis coordinate values ​​of the first camera 20 and the second camera 30. Specifically, as shown in Fig. 1(B), the processor 110 calculates a first optical axis coordinate value FOE1 which is the optical axis coordinate value of the first camera 20 based on the first camera image 21. In addition, as shown in Fig. 1(C), the processor 110 calculates a second optical axis coordinate value FOE2 which is the optical axis coordinate value of the second camera 30 based on the second camera image 31.

[0020] The first optical axis coordinate value FOE1 and the second optical axis coordinate value FOE2 refer to, for example, a point at infinity (also called a vanishing point (FOE: Focus Optical Expansion)). An example of calculating the first optical axis coordinate value FOE1 and the second optical axis coordinate value FOE2 is the technology of the reference document (JP Patent Publication 2011-081613 A). According to this document, the intersection of optical flows obtained from characteristic points such as road structures existing around the vehicle 1 is set as the optical axis coordinate value (FOE).

[0021] Furthermore, the processor 110 determines whether or not there is an optical axis abnormality for each of the calculated first optical axis coordinate value FOE1 and second optical axis coordinate value FOE2.

[0022] First, the determination of the presence or absence of an optical axis abnormality in the first optical axis coordinate value FOE1 will be described. As shown in FIG. 1B, the first camera acquired information 122 further includes information of a first predetermined area IMG_A1. The first predetermined area IMG_A1 is an area of ​​a predetermined size set around the position where the optical axis coordinate value should be located in the first camera image 21, and means an image area indicating that the optical axis is not abnormal. Therefore, when the first optical axis coordinate value FOE1 is included in the first predetermined area IMG_A1, the processor 110 determines the presence or absence of an optical axis abnormality as "absent". On the other hand, when the first optical axis coordinate value FOE1 is not included in the first predetermined area IMG_A1, the processor 110 determines the presence or absence of an optical axis abnormality as "present".

[0023] Next, the determination of the presence or absence of an optical axis abnormality in the second optical axis coordinate value FOE2 will be described. As shown in FIG. 1(C), the second camera acquired information 123 further includes information of the second predetermined area IMG_A2. The second predetermined area IMG_A2 is an area of ​​a predetermined size set around the position where the optical axis coordinate value should be located in the second camera image 31, and means an image area indicating that the optical axis is not abnormal. Therefore, when the second optical axis coordinate value FOE2 is included in the second predetermined area IMG_A2, the processor 110 determines the presence or absence of an optical axis abnormality as "absent". On the other hand, when the second optical axis coordinate value FOE2 is not included in the second predetermined area IMG_A2, the processor 110 determines the presence or absence of an optical axis abnormality as "present". The size of the second predetermined area IMG_A2 may be different from or the same as the size of the first predetermined area IMG_A1.

[0024] The presence or absence of an optical axis abnormality may be determined based on the number of times that the optical axis coordinate value (FOE) becomes abnormal. For example, if the number of times that the first optical axis coordinate value FOE1 goes out of the first predetermined area IMG_A1 is equal to or greater than a set reference number, the presence or absence of an optical axis abnormality is determined to be "present." The reference number may be different or the same for the first optical axis coordinate value FOE1 and the second optical axis coordinate value FOE2.

[0025] 1-2-2. Example of determining the location of the cause of the abnormality After the processor 110 has determined whether or not there is an optical axis abnormality, it identifies the abnormality location causing the optical axis abnormality. Specifically, the abnormality location is identified based on the combination of whether or not there is an abnormality in the first optical axis coordinate value FOE1 and whether or not there is an abnormality in the second optical axis coordinate value FOE2. For example, as shown in FIG. 1(D), consider a case where an abnormality occurs in either the first optical axis coordinate value FOE1 or the second optical axis coordinate value FOE2 (patterns 2 and 3). In this case, it is highly likely that the camera corresponding to the optical axis coordinate value where the abnormality occurs is the abnormality location. Therefore, in the case of pattern 2, the abnormality location is determined to be the second camera 30, and in the case of pattern 3, the abnormality location is determined to be the first camera 20.

[0026] On the other hand, as shown in pattern 4 of FIG. 1(D), consider a case where an abnormality occurs in both the first optical axis coordinate value FOE1 and the second optical axis coordinate value FOE2. In this case, even if both the first optical axis coordinate value FOE1 and the second optical axis coordinate value FOE2 are abnormal, it is difficult to consider that both the first camera 20 and the second camera 30 are abnormality-causing locations. Rather, it is highly likely that both the first optical axis coordinate value FOE1 and the second optical axis coordinate value FOE2 are abnormal due to the body of the vehicle on which the first camera 20 and the second camera 30 are mounted. Therefore, in the case of pattern 4, the location where the abnormality occurs is determined to be the body of the vehicle 1.

[0027] In this way, in the vehicle inspection system 10 according to the first embodiment, the presence or absence of an abnormality is determined for each of the first optical axis coordinate value FOE1 and the second optical axis coordinate value FOE2. Furthermore, according to the vehicle inspection system 10, based on the combination of the presence or absence of an abnormality in the first optical axis coordinate value FOE1 and the presence or absence of an abnormality in the second optical axis coordinate value FOE2, the abnormality factor part causing the abnormality in one or both of the first optical axis coordinate value FOE1 and the second optical axis coordinate value FOE2 is specified from among the first camera 20, the second camera 30, and the vehicle body. As a result, even if an optical axis abnormality occurs, the abnormality factor part can be specified in a short time. Furthermore, it is possible to perform repairs limited to the parts that need repairs.

[0028] 1-3. Processing example FIG. 2 is a flowchart showing an example of processing performed by the vehicle inspection system 10 according to the first embodiment.

[0029] In step S100, processor 110 acquires various types of information such as first camera acquired information 122, second camera acquired information 123, etc. Then, the process proceeds to step S110.

[0030] In step S110, the processor 110 calculates a first optical axis coordinate value FOE1 based on the first camera acquired information 122, and calculates a second optical axis coordinate value FOE2 based on the second camera acquired information 123. After that, the process proceeds to step S120.

[0031] In step S120, the processor 110 determines whether or not there is an abnormality in the first optical axis coordinate value FOE1, and determines whether or not there is an abnormality in the second optical axis coordinate value FOE2. After that, the process proceeds to step S130.

[0032] In step S130, the processor 110 determines whether or not the condition that the first optical axis coordinate value FOE1 has an abnormality and the second optical axis coordinate value FOE2 has an abnormality is satisfied. If it is determined that the condition is satisfied (step S130; Yes), the process proceeds to step S140. Otherwise (step S130; No), the process proceeds to step S150.

[0033] In step S140, the processor 110 determines that the abnormality causing the abnormality in both the first optical axis coordinate value FOE1 and the second optical axis coordinate value FOE2 is the body of the vehicle.

[0034] In step S150, the processor 110 determines whether or not there is an abnormality in the first optical axis coordinate value FOE1. If it is determined that there is an abnormality in the first optical axis coordinate value FOE1 (step S150; Yes), the process proceeds to step S160. Otherwise (step S150; No), the process proceeds to step S170.

[0035] In step S160, the processor 110 determines that the first camera 20 is the abnormality source location causing the abnormality only in the first optical axis coordinate value FOE1.

[0036] In step S170, the processor 110 determines that the second camera 30 is the abnormality source location causing the abnormality only in the second optical axis coordinate value FOE2.

[0037] 2. Second embodiment 2-1. Overview In the vehicle inspection system 10 according to the first embodiment, when there is an abnormality in both the first optical axis coordinate value FOE1 and the second optical axis coordinate value FOE2, it is determined that the abnormality causing location is the body of the vehicle 1. However, there are various abnormality causes for a vehicle body determined to be an abnormality causing location. Therefore, according to the vehicle inspection system 10 according to the second embodiment, when it is determined that the abnormality causing location is the vehicle body, the abnormality cause of the vehicle body is further estimated. This makes it possible to clarify the location requiring repair in addition to the effect of the first embodiment. The following mainly describes the differences from the first embodiment.

[0038] Here, let us consider the abnormality factor that is estimated when the abnormality factor location is the vehicle body. When the total load capacity of the rear seats and trunk room located in the rear part of the vehicle body exceeds the maximum load capacity (i.e., when the vehicle 1 is overloaded), it is assumed that the vehicle body cannot be kept horizontal and the rear part of the vehicle body will tilt so as to sink toward the ground from the horizontal. Therefore, as an example of an abnormality factor of the vehicle body, it is assumed that the vehicle height of the rear part of the vehicle body will be significantly lowered. Note that a height sensor that can measure the vehicle height is typically provided in the rear part of the vehicle body. Therefore, according to the vehicle inspection system 10 of the second embodiment, it is estimated whether the abnormality factor when the abnormality factor location is the vehicle body is overloaded or not based on the information on the vehicle height obtained from the height sensor.

[0039] Fig. 3 is a block diagram showing an overview and a specific example of the vehicle inspection system 10 according to the second embodiment. Specifically, as shown in Fig. 3(A), the vehicle inspection system 10 further includes a height sensor 40. As described above, the height sensor 40 is a sensor that is provided on the vehicle body and measures the vehicle height of the vehicle body. The storage device 120 further stores vehicle height information 124 acquired from the height sensor 40.

[0040] 2-2.Specific examples In the above-described example of determining the abnormality cause location, when it is determined that the abnormality cause location is the vehicle body, the processor 110 estimates the abnormality cause based on the vehicle height information 124. Specifically, as shown in FIG. 3(B), when the vehicle body is identified as the abnormality cause location and the vehicle height acquired from the height sensor 40 is less than a threshold value (pattern 4(a)), the processor 110 estimates that the abnormality cause is overloading of the vehicle 1. On the other hand, when the vehicle height acquired from the height sensor 40 is equal to or greater than the threshold value (pattern 4(b)), that is, when the abnormality cause is not overloading of the vehicle 1, it is assumed that the structure of the vehicle body is deformed. Therefore, in this case, the processor 110 estimates that the abnormality cause is structural deformation of the vehicle body.

[0041] 2-3. Processing example FIG. 4 is a flowchart showing an example of processing of the vehicle inspection system 10 according to the second embodiment.

[0042] In step S200, if the abnormality cause location is determined to be the vehicle body in the above-mentioned step S140, processor 110 acquires vehicle height information 124. After that, the process proceeds to step S210.

[0043] In step S210, processor 110 determines whether or not the vehicle height is less than the threshold. If it is determined that the vehicle height is less than the threshold (step S210; Yes), the process proceeds to step S220. Otherwise (step S210; No), the process proceeds to step S230.

[0044] In step S220, the processor 110 estimates that the cause of the abnormality is that the vehicle 1 is overloaded.

[0045] In step S230, the processor 110 estimates that the abnormality cause is structural deformation of the vehicle body.

[0046] 3. Third embodiment 3-1. Overview In the vehicle inspection system 10 according to the first embodiment, when there is an abnormality in either the first optical axis coordinate value FOE1 or the second optical axis coordinate value FOE2, it is determined that the abnormality location is the camera corresponding to the optical axis coordinate value having the abnormality (i.e., the first camera 20 or the second camera 30). However, there are various abnormality factors of the cameras. Therefore, according to the vehicle inspection system 10 according to the third embodiment, when it is determined that the abnormality location is the first camera 20 or the second camera 30, the abnormality factor of the camera is further estimated. This makes it possible to clarify the location requiring repair in addition to the effect of the first embodiment. The following mainly describes the differences from the first embodiment.

[0047] Here, let us consider the cause of the abnormality when the location causing the abnormality is the first camera 20 or the second camera 30. The camera mounted on the vehicle 1 is fixedly attached to the vehicle body. For this reason, one example of a cause of the abnormality in the camera is assumed to be a displacement of the position of the camera attached to the vehicle body due to vibration of the vehicle 1 while the vehicle 1 is traveling. Therefore, according to the vehicle inspection system 10 of the second embodiment, when the location causing the abnormality is the first camera 20 or the second camera 30, it is estimated whether or not the cause of the abnormality is a displacement of the camera position based on information detecting the displacement of the camera position.

[0048] FIG. 5 is a block diagram showing an overview and a specific example of the vehicle inspection system 10 according to the third embodiment. Specifically, as shown in FIG. 5(A), the vehicle inspection system 10 further includes a first sensor 50 and a second sensor 60. The first sensor 50 is attached to the first camera 20 and is a sensor that measures the attitude angle of the first camera 20. The second sensor 60 is attached to the second camera 30 and is a sensor that measures the attitude angle of the second camera 30. The storage device 120 further stores first attitude angle information 125 acquired from the first sensor 50 and second attitude angle information 126 acquired from the second sensor 60. Examples of sensors that measure attitude angles include gyro sensors. In addition, the attitude angle is indicated by, for example, rotation angles in the Yaw direction, Pitch direction, and Roll direction.

[0049] 3-2.Specific examples In the above-described example of determining the abnormality cause location, if it is determined that the abnormality cause location is the first camera 20, the processor 110 estimates the abnormality cause based on the first attitude angle information 125. Specifically, as shown in FIG. 5(B), if the second camera 30 is identified as the abnormality cause location and the attitude angle (second attitude angle) of the second camera 30 acquired from the second sensor 60 is outside the allowable range (pattern 2(a)), the processor 110 estimates that the abnormality cause is a positional deviation of the second camera 30 attached to the vehicle body. On the other hand, if the second attitude angle is within the allowable range (pattern 2(b)), it is assumed that the inside of the second camera 30 is broken. Therefore, in this case, the processor 110 estimates that the abnormality cause is an internal failure of the second camera 30. In the above-described example of determining the abnormality cause location, if it is determined that the abnormality cause location is the first camera 20, the same process as that of the second camera 30 is performed, and therefore a description thereof will be omitted here.

[0050] 3-3. Processing example Fig. 6 is a flowchart showing an example of processing of the vehicle inspection system 10 according to the embodiment 3. Specifically, Fig. 6(A) is a flowchart showing an example of processing for estimating an abnormality factor of the first camera 20, and Fig. 6(B) is a flowchart showing an example of processing for estimating an abnormality factor of the second camera 30.

[0051] 6(A), in step S300, if the abnormality cause location is determined to be the first camera 20 in the above-mentioned step S160, the processor 110 acquires the first attitude angle information 125. After that, the process proceeds to step S310.

[0052] In step S310, the processor 110 determines whether the first attitude angle is outside the allowable range. If it is determined that the first attitude angle is outside the allowable range (step S310; Yes), the process proceeds to step S320. Otherwise (step S310; No), the process proceeds to step S330.

[0053] In step S320, processor 110 estimates that the cause of the abnormality is a deviation in the position of first camera 20.

[0054] In step S330, processor 110 estimates that the cause of the abnormality is an internal malfunction of first camera 20.

[0055] In steps S400 to S430 shown in the flow chart of FIG. 6B, the same processes as those in steps S300 to S330 described above are performed, and therefore a description thereof will be omitted here.

[0056] 4. Other embodiments 4-1. First Example According to the vehicle inspection system 10 of the other embodiment, after the above-mentioned abnormality factor is estimated, an alarm is issued to the occupant to prompt the repair of the part requiring repair. This allows the dealer or the like to perform diagnosis and repair early. The part requiring repair means that the abnormality factor is any of the following: an internal failure or positional deviation of the first camera 20, an internal failure or positional deviation of the second camera 30, or a structural deformation of the vehicle body.

[0057] 4-2. Second Example According to the vehicle inspection system 10 according to the other embodiment, at least one of the results of the determination of the abnormality cause location and the estimation of the abnormality cause is retained even when the engine is restarted. As a result, even if the abnormality cause location returns to normal after the engine is restarted, the information on the result that was once determined to be abnormal continues to be notified to the occupants. This allows the dealer or the like to perform diagnosis and repairs early. [Explanation of symbols]

[0058] 1...vehicle, 10...vehicle inspection system, 20...first camera, 21...first camera image, 30...second camera, 31...second camera image, 40...height sensor, 50...first sensor, 60...second sensor, 100...computer, 110...processor, 120...storage device, 121...vehicle inspection program, 122...information acquired by first camera, 123...information acquired by second camera, 124...vehicle height information, 125...first attitude angle information, 126...second attitude angle information, FOE1...first optical axis coordinate value, FOE2...second optical axis coordinate value, IMG_A1...first specified area, IMG_A2...second specified area

Claims

1. A vehicle inspection system for inspecting a vehicle, comprising: a computer connected to a first camera mounted at the front of the vehicle and a second camera mounted at the rear of the vehicle; The computer includes: acquiring a first camera image taken by the first camera while the vehicle is traveling, and acquiring a second camera image taken by the second camera simultaneously with the first camera taking the first camera image; Calculating a first optical axis coordinate value from the first camera image; Calculating a second optical axis coordinate value from the second camera image; determining whether or not there is an abnormality with respect to each of the first optical axis coordinate value and the second optical axis coordinate value; and identifying an abnormality factor location causing an abnormality in one or both of the first optical axis coordinate value and the second optical axis coordinate value from among the first camera, the second camera, and the vehicle body based on a combination of the presence or absence of an abnormality in the first optical axis coordinate value and the presence or absence of an abnormality in the second optical axis coordinate value. A vehicle inspection system comprising:

2. 2. A vehicle inspection system according to claim 1, The computer includes: If there is an abnormality in the first optical axis coordinate value and there is no abnormality in the second optical axis coordinate value, the abnormality location is identified as the first camera; When an abnormality is present in the second optical axis coordinate value and an abnormality is not present in the first optical axis coordinate value, the abnormality location is determined to be the second camera; When an abnormality is present in both the first optical axis coordinate value and the second optical axis coordinate value, the abnormality causing location is identified as the vehicle body. A vehicle inspection system comprising:

3. 3. A vehicle inspection system according to claim 2, Further, a height sensor provided on the vehicle body, The computer includes: When the vehicle body is identified as the abnormality causing portion and the vehicle height acquired from the height sensor is equal to or greater than a threshold value, the abnormality causing portion is estimated to be a structural deformation of the vehicle body; When the vehicle body is identified as the abnormality causing portion and the vehicle height is less than the threshold value, the abnormality causing portion is estimated to be an overload of the vehicle. A vehicle inspection system comprising:

4. 3. A vehicle inspection system according to claim 2, a first sensor that measures an attitude angle of the first camera and a second sensor that measures an attitude angle of the second camera, The computer includes: when the first camera is identified as the location causing the abnormality and the first attitude angle acquired from the first sensor is within a tolerable range, the cause of the abnormality is estimated to be an internal failure of the first camera; When the first camera is identified as the location causing the abnormality and the first attitude angle is outside the allowable range, the cause of the abnormality is estimated to be a deviation in position of the first camera attached to the vehicle body; when the second camera is identified as the location causing the abnormality and the second attitude angle acquired from the second sensor is within the allowable range, the cause of the abnormality is estimated to be an internal failure of the second camera; When the second camera is identified as the location causing the abnormality and the second attitude angle is outside the allowable range, the abnormality is estimated to be caused by a positional deviation of the second camera attached to the vehicle body. A vehicle inspection system comprising:

5. A vehicle inspection method for inspecting a vehicle, comprising: acquiring a first camera image taken by a first camera mounted on a front side of the vehicle while the vehicle is traveling, and acquiring a second camera image taken by a second camera mounted on a rear side of the vehicle simultaneously with the first camera image being taken by the first camera; Calculating a first optical axis coordinate value from the first camera image; Calculating a second optical axis coordinate value from the second camera image; determining whether or not there is an abnormality with respect to each of the first optical axis coordinate value and the second optical axis coordinate value; Identifying an abnormality factor location causing an abnormality in one or both of the first optical axis coordinate value and the second optical axis coordinate value from among the first camera, the second camera, and the vehicle body based on a combination of the presence or absence of an abnormality in the first optical axis coordinate value and the presence or absence of an abnormality in the second optical axis coordinate value; Includes A vehicle inspection method comprising:

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