JP2017220076A - Vehicle type discrimination device and vehicle type discrimination method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for discriminating among vehicle types more stably.SOLUTION: A vehicle type discrimination device includes: an acquisition part, an image generation part, a panoramic image estimation part, an interpolation part, an axle detection part and a discrimination part. The acquisition part acquires a detection point distance and a reflection intensity from a scanner part and acquires picked-up images from an imaging part. The image generation part generates a reflectance image which is an image obtained by varying the density of the detection point according to the reflection intensity of laser beams. The panoramic image estimation part estimates a panoramic image which is an image indicating the shape of a whole vehicle on the basis of the picked-up images. The interpolation part executes matching processing for comparing feature points of the panoramic image with the detection point of the reflectance image and interpolates the feature points of the panoramic image on the basis of the result of the matching process and the detection point distance. The axle detection part detects axles from the panoramic image whose feature points are interpolated and measures the number of axles and the distance between the axles. The discrimination part discriminates the type of the vehicle on the basis of the number of axles and the distance between the axles.SELECTED DRAWING: Figure 2

Description

  Embodiments described herein relate generally to a vehicle type identification device and a vehicle type identification method.

  For the purpose of charging a toll on the road and grasping the amount of traffic, the type of vehicle to travel is determined. Conventionally, as a method of discriminating the type of a vehicle traveling on a road, based on the technique of measuring or measuring the number of axles, vehicle height, and width of a vehicle by imaging or measuring the vehicle with a camera or a laser scanner. A technique for discriminating a vehicle type is disclosed.

JP 2014-215719 A JP-A-9-89640 Japanese Patent No. 5478419 Japanese Patent Application Laid-Open No. 11-232586

  However, since the vehicle shape including the vehicle height and the vehicle width used in the conventional vehicle type identification technology changes with the passage of time, a method for more stably identifying the vehicle type is required.

  Further, in the conventional vehicle type discrimination technology, it is required to improve the accuracy of discriminating between large vehicles and oversized vehicles.

  The vehicle type discrimination device according to the embodiment includes an acquisition unit, an image generation unit, a panoramic image estimation unit, an interpolation unit, an axle detection unit, and a discrimination unit. The acquisition unit acquires the detection point distance detected by irradiating a laser passing through a predetermined position detected by the scanner unit, and the reflection intensity of the laser reflected at the detection point, and from the imaging unit A captured image obtained by imaging the position is acquired. The image generation unit generates a reflectance image that is an image at a predetermined position including a detection point, and is an image in which the density of the detection point is varied according to the reflection intensity of the laser reflected at the detection point. The panoramic image estimation unit estimates a panoramic image that is an image showing the shape of the entire vehicle based on the captured image. The interpolating unit executes a matching process for matching the feature points of the panoramic image with the detection points of the reflectance image, and interpolates the feature points of the panoramic image based on the result of the matching process and the detection point distance. The axle detection unit detects the axles from the panoramic image obtained by interpolating the feature points, and measures the number of axles and the distance between the axles. The determination unit determines the vehicle type of the vehicle based on the number of axles and the distance between the axles.

The figure which shows an example of a structure of the vehicle type discrimination | determination system of Embodiment 1. FIG. FIG. 2 is a block diagram illustrating an example of a functional configuration of the vehicle type identification device according to the first embodiment. The figure for demonstrating an example of the estimation method of the three-dimensional shape of the vehicle in the vehicle type discrimination | determination apparatus of Embodiment 1. FIG. FIG. 4 is a diagram illustrating an example of a full view image of a vehicle in the first embodiment. FIG. 3 is a diagram illustrating an example of a full view image of a vehicle after a rotation process according to the first embodiment. FIG. 4 is a diagram illustrating an example of point cloud data generated from detected point distances according to the first embodiment. FIG. 3 is a diagram illustrating an example of a reflectance image according to the first embodiment. FIG. 6 is a first diagram for explaining an example of normalization processing of a vehicle image included in a reflectance image in the vehicle type identification device according to the first embodiment. FIG. 5 is a second diagram for explaining an example of normalization processing of a vehicle image included in a reflectance image in the vehicle type identification device according to the first embodiment. The figure for demonstrating concretely an example of the normalization process of the image of the vehicle which the reflectance image in the vehicle type discrimination | determination apparatus of Embodiment 1 contains. The figure for demonstrating an example of the procedure of the image interpolation process in the vehicle type discrimination | determination apparatus of Embodiment 1. FIG. The figure for demonstrating an example of the matching process in the vehicle type discrimination | determination apparatus of Embodiment 1. FIG. The figure for demonstrating an example of the image interpolation process in the vehicle type discrimination | determination apparatus of Embodiment 1. FIG. The figure for demonstrating an example of the axle shaft detection process in the vehicle type discrimination | determination apparatus of Embodiment 1. FIG. The figure for demonstrating an example of the measuring method of the magnitude | size of the axle shaft and the center position of an axle shaft in the vehicle type discrimination | determination apparatus of Embodiment 1. FIG. The figure for demonstrating an example of the lift axle function which lifts 1 axis | shaft in the vehicle type discrimination | determination apparatus of Embodiment 1. FIG. The figure for demonstrating an example of the lift axle function which lifts 2 axis | shafts in the vehicle type identification apparatus of Embodiment 1. FIG. The figure which shows an example of the vehicle type discrimination | determination information in the vehicle type discrimination | determination apparatus of Embodiment 1. FIG. 5 is a flowchart illustrating an example of a procedure of a vehicle type identification process in the vehicle type identification device according to the first embodiment. The figure which shows an example of a structure of the vehicle type discrimination | determination system of Embodiment 2. FIG. The block diagram which shows an example of a function structure of the vehicle type discrimination | determination apparatus of Embodiment 2. FIG. The figure for demonstrating an example of the normalization process in the vehicle type discrimination | determination apparatus of Embodiment 2. FIG. FIG. 9 is a block diagram illustrating an example of a functional configuration of a vehicle type identification device according to a third embodiment. The figure for demonstrating an example of the count process according to vehicle classification in the vehicle type discrimination | determination apparatus of Embodiment 3. FIG. The graph which shows an example of the count result of the number of passing vehicles per time for every vehicle type division in the vehicle type identification device of Embodiment 3. 10 is a flowchart illustrating an example of a procedure of vehicle type identification processing in the vehicle type identification device according to the third embodiment. The figure which shows an example of a structure of the vehicle type discrimination | determination system of Embodiment 4. The figure which shows an example of the some still image which the camera of Embodiment 4 imaged continuously. FIG. 10 is a diagram illustrating an example of a detection result of a laser scanner according to a fourth embodiment. FIG. 9 is a block diagram illustrating an example of a functional configuration of a vehicle type identification device according to a fourth embodiment. The figure which shows an example of the weight analysis for every axle shaft in the vehicle type discrimination | determination apparatus of Embodiment 4. FIG. 10 is a flowchart illustrating an example of a procedure of vehicle type identification processing in the vehicle type identification device according to the fourth embodiment.

(Embodiment 1)
The vehicle type identification system of the present embodiment generates a high-resolution panoramic image of the vehicle using the vehicle image captured by the camera and the detection point distance and reflection intensity obtained by scanning the vehicle with the laser scanner, By measuring the number of axles and the distance between axles from this panoramic image, the vehicle type classification and the vehicle type of the vehicle are discriminated based on the number of axles and the distance between the axles, thereby realizing more stable vehicle type discrimination. It is. Details of this embodiment will be described below.

  FIG. 1 is a diagram illustrating an example of a configuration of a vehicle type identification system 1a according to the present embodiment. As shown in FIG. 1, the vehicle type identification system 1 a according to this embodiment includes a camera 101, a laser scanner 102, and a vehicle type identification device 103. The vehicle type identification system 1a is provided on a road through which a large vehicle passes, a parking lot entrance / exit, a toll gate on an expressway, and the like.

  The camera 101 of this embodiment is a monocular camera or the like, and images a predetermined position T through which the vehicle 10 passes. The camera 101 is an example of an imaging unit in the present embodiment.

  The camera 101 of the present embodiment is provided at a position obliquely above the vehicle 10 that has entered the predetermined position T. Since the legal vehicle height of the vehicle 10 is 3.8 m, specifically, the camera 101 is provided 5 to 7 m above the road surface of the road at the predetermined position T.

  The camera 101 of the present embodiment is provided at a depression angle of 25 to 45 degrees with reference to the road surface of the road at the predetermined position T. In addition, the camera 101 of the present embodiment is provided so as to be able to capture the entire view of the vehicle 10 that passes the predetermined position T. Here, the whole view of the vehicle 10 refers to the overall shape of the vehicle 10.

  Specifically, the camera 101 has a front region of the vehicle 10 (for example, a front end of a tractor portion of a large vehicle) and a rear region of the vehicle 10 (for example, a rear end of a trailer portion of a large vehicle). It is provided to fit within the angle of view. Furthermore, when the vehicle 10 of the imaging target vehicle 10 is long, the camera 101 may adopt a configuration that allows the camera 101 to be tilted dynamically to capture an image in order to widen the angle of view of the camera 101.

  Although the vehicle type identification system 1a of the present embodiment includes at least one camera 101, a configuration including a plurality of cameras 101 that capture the front and rear or side surfaces of the vehicle 10 may be employed. Moreover, the installation position of the camera 101 should just be the camera 101 which can image the vehicle 10, and is not restricted to the installation position mentioned above. The installation angle of the camera 101 is not limited to the aforementioned angle.

  In the present embodiment, the camera 101 captures an image of the vehicle 10 passing through a predetermined position T when an imaging trigger signal is input from the laser scanner 102. Alternatively, the camera 101 may adopt a configuration in which imaging is continuously performed at regular time intervals, or the vehicle type identification system 1a is separately provided with a vehicle detection device (not shown), and the vehicle detection device sends an imaging trigger signal to the camera 101. May be adopted.

  The camera 101 continuously images the vehicle 10 passing through the predetermined position T a plurality of times. The camera 101 transmits a captured image obtained by capturing the vehicle 10 to the vehicle type identification device 103.

  The laser scanner 102 irradiates a target with a laser such as infrared rays and scans the target. The laser scanner 102 irradiates a laser at a predetermined position T through which the vehicle 10 passes to detect the detection point distance and the reflection intensity (reflectance) of the laser reflected at the detection point. Here, the detection point distance indicates the distance from the laser irradiation position to the detection point. The laser scanner 102 is an example of a scanner unit.

  In the present embodiment, the laser scanner 102 is installed at a position where the vehicle 10 can be scanned from the side when the vehicle 10 enters the predetermined position T. Although the vehicle type identification system 1a of the present embodiment includes at least one laser scanner 102, a configuration including a plurality of laser scanners 102 may be employed.

  When the laser scanner 102 detects that the vehicle 10 has entered the predetermined position T, the laser scanner 102 transmits an imaging trigger signal for instructing imaging of the vehicle 10 to the camera 101. Further, the laser scanner 102 irradiates the vehicle 10 with a laser until the vehicle 10 is detected after the vehicle 10 is detected, that is, until the vehicle 10 enters the predetermined position T and then exits. Specifically, the laser scanner 102 detects detection point distances and reflection intensities at a plurality of detection points while scanning a laser in a direction intersecting with the traveling direction of the vehicle 10 (hereinafter referred to as a scanning direction). The laser scanner 102 transmits the detection point distance and reflection intensity detected for each detection point to the vehicle type identification device 103.

  The vehicle type identification device 103 is an example of an electronic computer that executes a vehicle type identification process. The vehicle type identification device 103 is electrically connected to the camera 101 and the laser scanner 102.

  The vehicle type identification device 103 of this embodiment is installed on the side of the road adjacent to the predetermined position T as shown in FIG. 1, but is not limited to this, and may be installed in a management center or the like.

  The vehicle type identification device 103 acquires a captured image from the camera 101. Further, the vehicle type identification device 103 acquires the detection point distance and reflection intensity of each detection point from the laser scanner 102. Then, the vehicle type determination device 103 determines the vehicle type of the vehicle 10 passing through the predetermined position T using the captured image, the detection point distance, and the reflection intensity.

  FIG. 2 is a block diagram illustrating an example of a functional configuration of the vehicle type identification device 103 according to the present embodiment. As shown in FIG. 2, the vehicle type identification device 103 of the present embodiment includes a storage unit 90, an acquisition unit 91, an image generation unit 92, a panoramic image estimation unit 93, an interpolation unit 94, and an axle detection unit 95. And a determination unit 96.

  The storage unit 90 stores vehicle type determination information 20 that is a reference for determining the vehicle type of the vehicle 10. The contents of the vehicle type identification information 20 will be described later. In addition, the storage unit 90 can store information used for processing of each functional unit of the vehicle type identification device 103 in the present embodiment. The storage unit 90 is a storage medium such as a hard disk drive (HDD), a solid state drive (SSD), or a memory.

  The acquisition unit 91 acquires a detection point distance detected by irradiating a laser beam to the vehicle 10 passing through the predetermined position T detected by the laser scanner 102 and a reflection intensity of the laser reflected at the detection point. Specifically, when the laser scanner 102 detects that the vehicle 10 has entered the predetermined position T, the acquisition unit 91 acquires the detection point distances and the reflection intensities of the plurality of detection points from the laser scanner 102. The acquisition unit 91 acquires a captured image obtained by imaging the predetermined position T from the camera 101.

  The acquisition unit 91 sends the acquired detection point distance and reflection intensity to the image generation unit 92. The acquisition unit 91 sends the acquired captured image to the panoramic image estimation unit 93.

  The panoramic image estimation unit 93 estimates (generates) a three-dimensional image (hereinafter referred to as panoramic image D1) indicating the overall shape of the vehicle 10 based on the captured image obtained by the imaging of the camera 101.

  FIG. 3 is a diagram for explaining an example of a method for estimating the three-dimensional shape of the vehicle in the vehicle type identification device 103 of the present embodiment. In the present embodiment, the panoramic image estimation unit 93 acquires a plurality of captured images obtained by capturing the predetermined position T a plurality of times by the camera 101 from the acquisition unit 91. Then, the panoramic image estimation unit 93 estimates the panoramic image D1 of the vehicle 10 according to a plurality of captured images using a technique such as SfM (Structured from Motion).

  A plurality of images 200 to 203 illustrated in FIG. 3 are examples of images obtained by continuously capturing the vehicle 10 in which the camera 101 passes the predetermined position T. For example, the image 200 at the left end is an image in which the camera 101 captures the predetermined position T at time t0 when the vehicle 10 enters the predetermined position T. The rightmost image 203 is an image obtained by the camera 101 capturing the predetermined position T at time t3 when the vehicle 10 leaves the predetermined position T. The two central images 201 to 202 are images in which the camera 101 captures the predetermined position T at predetermined times t1 and t2 after the vehicle 10 enters the predetermined position T and then exits.

  In the lower part of FIG. 3, a method for estimating the movement amount of the feature point P of the vehicle 10 between the times t0 and t1 from the images 200 to 201 is shown. The feature points Pa and Pb are examples of the feature point P of the vehicle 10 included in the images 200 to 201. Locations used as the feature points Pa and Pb include edges (corner portions) of the vehicle 10 included in the images 200 to 201.

  A point a indicates a position where the feature point Pa included in the image 200 is projected on the projection plane 205 with reference to the position 204 of the camera 101. A point b indicates a position where the feature point Pb included in the image 200 is projected on the projection plane 205 with the position 204 of the camera 101 as a reference. Point a ″ and point b ″ indicate the positions at which the characteristic points Pa and Pb included in the image 201 are projected on the projection plane 205 with respect to the position 204 of the camera 101, respectively. The projection plane 205 refers to a virtual plane defined to estimate the positions of the feature points Pa and Pb with the position of the camera 101 as a reference (origin).

  The position of the projection plane 205 is fixed. As the feature points Pa and Pb move between the image 200 and the image 201, the point a is moved to the position of the point a ", and the point b is moved to the position of the point b". Move on the projection plane 205. The panoramic image estimation unit 93 detects the movement of the feature points Pa and Pb and associates the movements of the feature points Pa and the feature points Pb between the images 200 and 201 continuously captured in this manner. Can be estimated.

  The panoramic image estimation unit 93 determines the feature points Pa and Pb based on the positional relationship between the vehicle 10 that is the subject and the position 204 of the camera 101 based on the amount of movement of the points a and b projected on the projection plane 205. Estimate the three-dimensional position.

  The panoramic image estimation unit 93 associates the positions of the feature points Pa and Pb between the images 201 and 202 and between the image 202 and the image 203, and estimates the three-dimensional positions of the feature points Pa and Pb. To do. The panoramic image estimation unit 93 estimates the three-dimensional shape of the panoramic view of the vehicle 10 by sequentially estimating the movement amount of the feature points between the successive images in this way.

  In addition, when the camera 101 is imaged while the camera 101 is not fixed, the panoramic image estimation unit 93 is configured to estimate the three-dimensional positions of the feature points Pa and Pb based on the movement amount of the camera 101. It may be adopted.

  In this way, the panoramic image estimation unit 93 estimates the panoramic image D1 from a plurality of still images continuously captured by the camera 101. The panoramic image estimation unit 93 estimates a panoramic image D1 that is an image showing the three-dimensional shape of the panoramic view of the vehicle 10. In addition, the whole-view image estimation unit 93 may employ a configuration in which the images of the whole-view image D1 are superimposed by combining the captured images in the vicinity of the feature points to further improve the resolution of the whole-view image D1.

  In FIG. 3, four images 200 to 203 are taken as an example, but the camera 101 may adopt a configuration that can capture more images until the vehicle 10 passes the predetermined position T. As the number of captured images is larger, the panoramic image estimation unit 93 can estimate the panoramic image D1 with higher accuracy. The feature points Pa and Pb are examples of the feature point P in the images 200 to 203, and do not limit the number of feature points used for alignment.

  FIG. 4 is a diagram illustrating an example of a full view image D1 of the vehicle in the present embodiment. A point P indicates a feature point that forms the full-view image D1. Here, as described with reference to FIG. 1, since the camera 101 captures the vehicle 10 from an obliquely upper position, the estimated whole-view image D1 has a shape when the vehicle 10 is viewed obliquely as shown in FIG. 4. Represents.

Here, the panoramic image estimation unit 93 rotates the panoramic image D1 and estimates an image viewed from the side of the vehicle 10.
FIG. 5 is a diagram illustrating an example of the full-view image D2 of the vehicle after the rotation process in the present embodiment. By performing the process of rotating the panoramic image D1, the panoramic image estimation unit 93 estimates the panoramic image D2 of the vehicle 10 viewed from the same direction as the radiation position of the laser scanner 102.

  The panoramic image estimation unit 93 measures the vehicle length of the vehicle 10 from the panoramic image D2 and sends it to the image generation unit 92. Further, the panoramic image estimation unit 93 sends the estimated panoramic image D2 to the interpolation unit 94. The panoramic image estimation unit 93 may employ a configuration in which the vehicle width and the vehicle height of the vehicle 10 are further measured from the panoramic image D1 and the panoramic image D2.

Returning to FIG. 2, the image generation unit 92 generates point cloud data g from the detected point distance acquired from the acquisition unit 91. The point cloud data g is an image at a predetermined position T, in which the density of the detection point is varied according to the detection point distance of the detection point.
FIG. 6 is a diagram illustrating an example of point cloud data g generated from the detected point distance. In FIG. 6, the vertical axis indicates the scanning angle when the laser is scanned at each detection point p of the vehicle 10, and the horizontal axis indicates the time when the laser is scanned at each detection point p of the vehicle 10. As shown in FIG. 6, in the point cloud data g, the detection point p with a shorter detection point distance is displayed in a lighter color, and the detection point p with a longer detection point distance is displayed in a darker color.

Further, the image generation unit 92 generates a reflectance image G from the reflection intensity acquired from the acquisition unit 91. The reflectance image G is an image at a predetermined position T, in which the density at the detection point p is varied according to the reflection intensity of the laser reflected at the detection point p.
FIG. 7 is a diagram illustrating an example of the reflectance image G in the present embodiment. In FIG. 7, the vertical axis indicates the scanning angle when the laser is scanned at each detection point p of the vehicle 10, and the horizontal axis indicates the time when the laser is scanned at each detection point p of the vehicle 10. As shown in FIG. 7, in the reflectance image G, the detection point p having a higher laser reflection intensity is displayed in a lighter color and the detection point p having a lower reflection intensity is displayed in a darker color.

  Since the distance and the reflection intensity are acquired in a one-to-one correspondence with each detection point p, each detection point p displayed in the point cloud data g and the reflectance image G has a one-to-one correspondence. It becomes a relationship. That is, the point cloud data g and the reflectance image G are images including the same vehicle 10.

  A characteristic of the image obtained by the laser scanner 102 is that the detection points p are densely detected in the scanning direction of the laser scanner 102, but the detection of the detection points p is sparse in the time axis direction. . That is, in the point cloud data g and the reflectance image G, the detection points p are densely displayed in the vehicle height direction of the vehicle 10, but the detection points p are periodically missing in the vehicle length direction of the vehicle 10. ing.

  Here, in the vehicle type determination process in the vehicle type determination device 103 of the present embodiment, the vehicle type is determined using the number of axles of the vehicle 10 and the distance between the axles measured from the image. For this reason, in the vehicle type identification device 103 according to the present embodiment, the point cloud data g and the reflectance image G are used to perform an image interpolation process on the image captured by the camera 101 to generate a high-resolution image. The accuracy of measuring the number and distance between axles.

  In order to perform the image interpolation process, it is necessary to perform a matching process in which an image captured by the camera 101 and an image obtained by the laser scanner 102 are collated, corresponding points are detected, and alignment is performed. Here, the image captured by the camera 101 is more compatible with the reflectance image G than the point cloud data g. Therefore, it is easier to detect the corresponding points when the reflectance image G is used for matching processing with the image captured by the camera 101 than when the point cloud data g is used. For the above reasons, in this embodiment, the reflectance image G is used for matching processing with an image captured by the camera 101.

  Here, the image obtained by the laser scanner 102 has a property of expanding and contracting in the time axis direction depending on the speed of the object. That is, as the passing speed of the vehicle 10 traveling in front of the laser scanner 102 increases, the number of detection points p in the time axis direction that can be detected by the laser scanner 102 decreases, and the horizontal length of the generated reflectance image G decreases. Becomes shorter.

  If the aspect ratio of the reflectance image G remains changed depending on the speed of the vehicle 10, it is difficult to perform matching processing with the image captured by the camera 101. Therefore, the image generation unit 92 performs a normalization process on the reflectance image G so that the aspect ratio of the reflectance image G matches the actual aspect ratio of the vehicle 10.

  8 and 9 are diagrams for explaining an example of the normalization process of the vehicle image included in the reflectance image G in the vehicle type identification device 103 of the present embodiment. The vehicle length l is the vehicle length of the vehicle 10 in the reflectance image G before normalization processing, and the vehicle length L is the vehicle length of the vehicle 10 in the reflectance image G after normalization processing. 8 shows an example in which the vehicle 10 passes through the predetermined position T at the speed V1, and the vehicle 10 in FIG. 9 passes through the predetermined position T at the speed V2 faster than the speed V1.

  8 and 9 are reflectance images G including images of the vehicle 10 having the same vehicle length. However, the vehicle length l of the image of the vehicle 10 included in the reflectance image G (see FIG. 6) when the vehicle 10 passes at the speed V2 is the reflectance image G when the vehicle 10 passes at the speed V1 (FIG. 5). The vehicle length l of the image of the vehicle 10 included in (see). That is, the vehicle length l of the image of the vehicle 10 included in the reflectance image G becomes shorter as the speed of the vehicle 10 passing through the predetermined position T increases. Therefore, as illustrated in FIGS. 8 and 9, the image generation unit 92 performs a process of normalizing the vehicle length l of the vehicle image included in the reflectance image G to the vehicle length L.

  Next, a specific example of processing for normalizing the reflectance image G will be described with reference to FIG. FIG. 10 is a diagram for specifically explaining an example of normalization processing of the image of the vehicle 10 included in the reflectance image G in the vehicle type identification device 103 of the present embodiment.

  As shown in FIGS. 10A to 10B, first, the image generation unit 92 specifies the vehicle length l of the vehicle image included in the reflectance image G. Further, as shown in FIG. 10C, the image generation unit 92 detects the front axle FS and the rear axle BS included in the reflectance image G. Next, the image generation unit 92 measures the width of the front axle FS and the width of the rear axle BS. The width of the front axle FS can be acquired by measuring the distance from the left end to the right end of the detected front axle FS. The same applies to the method of measuring the width of the rear axle BS. The image generation unit 92 can detect the front axle FS and the rear axle BS in the same manner as the axle detection in the axle detection unit 95. A method of detecting the axle in the axle detection unit 95 will be described later.

  Further, as shown in FIG. 10D, the image generation unit 92 calculates the ratio of the width of the front axle FS and the width of the rear axle BS as a rate of change in speed when the vehicle 10 passes the predetermined position T. . When the speed change rate is “1” and there is a high possibility that the vehicle 10 has passed at a constant speed when passing the predetermined position T, as shown in FIG. The reflectance image G is uniformly expanded and contracted in the vehicle length direction of the vehicle image, and the vehicle length of the vehicle image included in the reflectance image G is normalized to a predetermined length L.

  Here, in the present embodiment, the image generation unit 92 uses the vehicle length L of the panoramic image D2 estimated (generated) by the panoramic image estimation unit 93 from the image captured by the camera 101 as the predetermined length L.

  On the other hand, when the speed change rate is not “1”, the image generation unit 92 is likely to have accelerated or decelerated when the vehicle 10 passes the predetermined position T. Change the expansion ratio in the long direction. For example, when the rate of change in speed is smaller than “1” and there is a high possibility that the vehicle 10 has decelerated when passing the predetermined position T, the image generation unit 92 moves toward the vehicle length direction of the vehicle image. As the distance from the rear end increases, the interval between the detection points p of the reflectance image G is reduced. On the other hand, when the rate of change in speed is greater than “1” and there is a high possibility that the vehicle 10 has accelerated when passing through the predetermined position T, the image generation unit 92 moves toward the vehicle length direction of the vehicle image. As the distance from the rear end increases, the interval between the detection points p of the reflectance image G is increased.

  The image generation unit 92 sends the point cloud data g and the normalized reflectance image G to the interpolation unit 94.

  Returning to FIG. 2, the interpolation unit 94 executes a matching process for matching the feature point P of the panoramic image D <b> 2 with the detected point p of the normalized reflectance image G. Then, the interpolation unit 94 interpolates the feature point P of the panoramic image D2 based on the result of the matching process and the detection point p included in the point cloud data g generated from the detection point distance.

  FIG. 11 is a diagram for explaining an example of image interpolation processing in the vehicle type identification device 103 of the present embodiment. As shown in FIG. 11A, the interpolation unit 94 executes a matching process for matching the feature point P included in the panoramic image D2 with the detection point p of the reflectance image G. Next, as illustrated in FIG. 11B, the interpolation unit 94 interpolates between the feature points P collated with the detection points p with the point cloud data g existing between the feature points P in the panoramic image D2. An interpolation process is executed to obtain a panoramic image A having dense feature points P.

The matching process performed by the interpolation unit 94 will be specifically described.
FIG. 12 is a diagram for explaining an example of matching processing in the vehicle type identification device 103 of the present embodiment. As illustrated in FIG. 12, the interpolation unit 94 specifies the detection point p of the normalized reflectance image G that matches the feature point P of the full-view image D2.

Next, the image interpolation process performed by the interpolation unit 94 will be specifically described.
FIG. 13 is a diagram for explaining an example of image interpolation processing in the vehicle type identification device 103 of the present embodiment. As described in FIGS. 6 and 7, the point cloud data g has the detection points p densely in the scanning direction of the vehicle 10. However, in the point cloud data g, the detection points p are sparse in the time axis direction of the vehicle 10.

  On the other hand, as described with reference to FIGS. 3 to 5, the panoramic image D <b> 2 of the vehicle 10 is estimated based on the captured image obtained by the imaging of the camera 101. Therefore, the panoramic image D2 is easily affected by the subject (vehicle 10) and the imaging conditions when imaging the subject. In general, the feature points P included in the panoramic image D2 have a property of becoming sparser than the detection points p included in the point cloud data g.

  As described with reference to FIG. 7, since the detection point p of the reflectance image G and the detection point p of the point cloud data g have a one-to-one correspondence, the point cloud data g matching the feature point P of the full-view image D2. The detection point p is identified by the result of the matching process described in FIG.

  As illustrated in FIG. 13, the interpolation unit 94 interpolates (enlarges) between the feature points P that match (match) the detection points p with the detection points p existing between the feature points P in the full-view image D2. Accordingly, as illustrated in FIG. 13, the interpolation unit 94 can obtain a panoramic image A having dense feature points P (in other words, a panoramic image A in which the feature points P are expanded).

  The interpolation unit 94 sends the panoramic image A to the axle detection unit 95.

  Returning to FIG. 2, the axle detection unit 95 detects the axles from the panoramic image A obtained by interpolating the feature points P with the point cloud data g, and measures the number of axles and the distance between the axles.

  FIG. 14 is a diagram for explaining an example of an axle detection process in the vehicle type identification device 103 of the present embodiment. The semi-trailer full-view image A1 that has passed the predetermined position T shown in FIG. 14 is an example of the interpolated full-view image A.

  In FIG. 14, the lower limit in the height direction of the range in which the axle detection unit 95 performs the axle detection is the road surface 300. Further, the upper limit position 301 in the height direction of the range in which the axle detection unit 95 detects the axle is the height of the bottom surface of the vehicle body of the vehicle 10 included in the full-view image A1.

  It is desirable that the range in which the axle detection unit 95 performs the axle detection is set so that the structure other than the axles 11a to 11d is contained as much as possible. For example, the upper limit position 301 in the height direction of the range in which the axle detection is performed may be a value of the minimum ground altitude of the vehicle type assumed to travel the predetermined position T. Alternatively, the height from the road surface to the upper limit position 301 in the height direction of the range in which the axle detection is performed may be a value that is half the diameter of the assumed axles 11a to 11d. The structure which the memory | storage part 90 memorize | stores may be employ | adopted for the minimum ground altitude of the assumed vehicle type, and the diameter of the assumed axles 11a-11d.

  The axle detection unit 95 detects an area where the occurrence frequency of the feature point P is higher than a predetermined threshold within a range in which the axle detection is performed in the panoramic image A1. In the region where the axles 11a to 11d are present, the occurrence frequency of the feature points P is higher than that of the space portion where the axles 11a to 11d are not present. For this reason, the axle detection unit 95 can detect the positions of the axles 11a to 11d by detecting an area where the occurrence frequency of the feature point P is higher than a predetermined threshold. As described above, the axle detection unit 95 detects the positions of the axles 11a to 11d included in the panoramic image A1 shown in FIG. The storage unit 90 stores a predetermined threshold value for the occurrence frequency of the feature point P.

  The method of detecting the axles 11a to 11d in the axle detection unit 95 is not limited to this. For example, since it is assumed that the axles 11a to 11d are generally circular and black, the axle detection unit 95 matches the circular and black image pattern (template) by pattern matching (template matching) from the whole scene image A1. You may employ | adopt the structure which detects an area | region as axle 11a-11d. In this case, a configuration in which the storage unit 90 stores the circular black image pattern (template) may be employed.

  Further, as described with reference to FIGS. 8 and 9, the reflectance image G after the normalization process has a vehicle length direction (time axis direction) of the vehicle 10 as the speed of the vehicle 10 passing through the predetermined position T increases. The number of detection points p is reduced. That is, for the vehicle 10 having a high traveling speed, the lateral resolution of the reflectance image G is insufficient, and the error in the position of the detection point p may be increased by extending the image in the lateral direction by normalization processing. . For this reason, for example, the axle detection unit 95 sets a lower limit for the horizontal resolution of the reflectance image G before normalization processing, and detects the axle from the uninterpolated panoramic image D2 if the resolution is below the lower limit. A configuration may be adopted in which the number of axles and the distance between the axles are measured. The lower limit of the horizontal resolution of the reflectance image G may be based on, for example, a case where the axle width in the image of the vehicle 10 included in the reflectance image G before normalization processing is equal to or less than a certain value.

  The axle detection method shown in FIG. 14 is also applicable to axle detection in the normalization process of the reflectance image G. That is, the image generation unit 92 may employ a configuration in which the axle detection unit 95 detects the axle from the reflectance image G using a method in which the axle processing is performed from the full-view image A.

  FIG. 15 is a diagram for explaining an example of a method of measuring the size of the axle and the center position of the axle in the vehicle type identification device 103 of the present embodiment. As shown in FIG. 15A, the axle detection unit 95 detects the axle 310 from the full-view image A. FIG. 15B is an enlarged view of a region including the axle 310. The axle detection unit 95 can acquire the diameter (size) of the axle 310 by measuring the distance from the road surface 312 to the highest position 311 from the road surface of the axle 310. The axle detection unit 95 measures an intermediate position 313 between the road surface 312 and the road surface of the axle 310 from the highest position 311, and measures the distance from the road surface 312 to the intermediate position 313, thereby The radius can be obtained.

  The distance from the left end 314 to the right end 315 of the axle 310 indicates the width of the axle 310. An intermediate position 316 between the left end 314 and the right end 315 of the axle 310 is the center of the axle 310.

  As shown in FIG. 14, the axle detector 95 measures the distance between the axles 11a to 11d after detecting the positions of the axles 11a to 11d. 14 is a distance from the center of the first axle 11a to the center of the second axle 11b, a symbol L2 is a distance from the center of the second axle 11b to the center of the third axle 11c, and a symbol L3 is The distances from the center of the third axle 11c to the center of the fourth axle 11d are respectively shown. The axle detection unit 95 measures the distance between the axles in the actual vehicle 10 by converting the distances L1 to L3 between the axles 11a to 11d on the panoramic image A1 into the actual size of the vehicle 10.

  Here, as a matter that needs to be considered when detecting the axle, a case where the vehicle 10 having a lift axle function passes will be described with reference to FIGS. 16 and 17. The lift axle function refers to a function that lifts a part of the axle from the road surface so that it does not come into contact with the ground when the load is light, for example, in a trailer. An axle that floats off the road surface is not an axle in terms of vehicle type classification and vehicle type criteria. Therefore, the lift axle function is often used to reduce the number of axles in order to lower the vehicle type classification from an oversized vehicle to a large vehicle to save on highway tolls.

  Specifically, there is a case where one axle is lifted from the road surface as shown in FIG. 16, a case where two axles are lifted from the road surface as shown in FIG. There is a space from the road surface 400 shown in FIGS. 16 and 17 to the lower limit 401 of the height of the lifted axle. Since there is a difference in the distribution of the feature points P in the panoramic image A1 between the axle portion and the space portion, when the axle detection unit 95 recognizes the space below the axle from the distribution of the feature points P in the panoramic image A1, the axle is Judge that it is not an axle. The method for determining the lift of the axle is not limited to this, and the axle detector 95 may identify the space under the axle from the detection point distance of the detection point p measured by the laser scanner 102. Further, the axle detection unit 95 may determine the lift of the axle by recognizing that the position of the circular image estimated as the axle is away from the road surface 400 as a result of pattern matching.

  When it is determined that the axle is floating from the road surface, the axle detection unit 95 measures the number of axles excluding the axle floating from the road surface and the distance between the axles.

  The axle detection unit 95 sends the measured number of axles of the vehicle 10 and the distance between the axles to the determination unit 96.

  Returning to FIG. 2, the determination unit 96 determines the vehicle type classification and vehicle type of the vehicle 10 based on the number of axles acquired from the axle detection unit 95 and the distance between the axles. Here, the process in which the determination unit 96 determines the vehicle type classification and the vehicle type is referred to as a vehicle type determination process.

  Here, the vehicle type classification means a classification for classifying the vehicle type. In the present embodiment, the vehicle type classification employs a configuration in which the vehicle types are classified into light cars, ordinary cars, medium-sized cars, large-sized cars, and extra-large cars according to the car type classifications used in the toll standards for highways.

  The determination unit 96 executes a vehicle type determination process according to the standard defined in the vehicle type determination information 20. The vehicle type discrimination information 20 is information that defines a vehicle type classification and a standard for specifying the vehicle type based on the number of axles and the distance between the axles.

  For example, the discriminator 96 determines whether the vehicle 10 has three or more axles according to the criteria defined in the vehicle type discriminating information 20 and the distance between the axles in the vehicle 10 is more than a predetermined distance. If it is determined that there are more or less places, it is determined that the vehicle 10 is an oversized vehicle. Further, when determining that the number of axles of the vehicle 10 is five or more, the determination unit 96 determines that the vehicle 10 is an extra large vehicle.

  Details of the standard defined in the vehicle type discrimination information 20 will be described with reference to FIG. FIG. 18 is a diagram illustrating an example of the vehicle type identification information 20 in the vehicle type identification device 103 according to the present embodiment. In the present embodiment, the vehicle type identification information 20 is stored in the storage unit 90. Alternatively, the vehicle type determination information 20 may be configured to be stored in an external storage device (not shown) connected to the vehicle type determination device 103 via a network.

  As shown in FIG. 18, the vehicle type determination information 20 includes at least information on “number of axes”, “distance condition between axles (m)”, “vehicle type classification”, and “vehicle type”. Note that the item “image diagram” displays the modeled position of the axle based on the inter-axle distance condition, and thus is not essential as the configuration of the vehicle type discrimination information 20.

  The item “number of axes” of the vehicle type identification information 20 indicates the number of axles. According to the vehicle type discriminating information 20 in FIG. 18, the number of axles is classified into four types of 2 axes, 3 axes, 4 axes, 5 axes and more. In this embodiment, if the number of axles of the vehicle 10 is two, the determination unit 96 determines that the vehicle type classification of the vehicle 10 is “light car, ordinary car, medium-sized car” or “medium-sized car” depending on the distance between the axles. And “large car”. If the number of axles of the vehicle 10 is 3, the discriminator 96 determines that the vehicle type classification of the vehicle 10 is either “extra large vehicle” or “medium-sized vehicle, large vehicle” depending on the combination of distances between the axles. Determine. If the number of axles of the vehicle 10 is 4, the determination unit 96 determines that the vehicle type classification of the vehicle 10 is “extra-large vehicle” or “large vehicle” according to the combination of distances between the axles. When the number of axles of the vehicle 10 is five or more, the determination unit 96 determines that the vehicle type classification of the vehicle 10 is “extra large vehicle” regardless of the distance between the axles.

  The “number of axes” of the vehicle type identification information 20 shown in FIG. 18 is an example, and the present invention is not limited to this.

  The item “distance between axles (m)” of the vehicle type discrimination information 20 indicates a threshold value of distance between axles in units of meters. When the number of axles is three or more, the combination of distances between the axles is a condition for discriminating the vehicle type classification and the vehicle type. The items L1 to L3 of the “distance condition between axles (m)” of the vehicle type identification information 20 shown in FIG. 18 are obtained by measuring the distance between two adjacent axles of the vehicle 10 from the center of each axle and converting to the actual size. Indicates the length. Specifically, as in FIG. 14, the item L1 in FIG. 18 is the distance between the foremost axle (first axle) of the vehicle 10 and the second axle (second axle) from the front side. Indicates. The item L2 indicates the distance between the second axle of the vehicle 10 and the third axle (third axle) from the front side. The item L3 indicates the distance between the third axle of the vehicle 10 and the fourth axle (fourth axle) from the front side.

  Items L1 to L3 of “distance condition between axles (m)” of the vehicle type identification information 20 are AND conditions, and only when all combinations of distances are satisfied, the conditions are met. The values n1 to n3 set in the items L1 to L3 are an example of a predetermined threshold for the distance between the axles. The magnitude relationship between the values n1 to n3 is n1 <n2 <n3.

  For example, the No. of the vehicle type identification information 20 in FIG. 6, the “number of axes” is 4 axes, L1 of “distance between axles (m)” is “n2 ≦ L1 <n3”, L2 is “n2 ≦ L2 <n3”, and L3 is “0 <L3 < n2 ". That is, the distance between the first axle and the second axle of the vehicle 10 is not less than n2 meters and less than n3 meters, and the distance between the second axle and the third axle is not less than n2 meters and less than n3 meters. When the distance between the third axle and the fourth axle is greater than 0 m and less than n2 meters, the determination unit 96 determines that the vehicle 10 is No. 6 “axle distance condition (m)” is determined to be satisfied.

  Further, the number of axles of the vehicle 10 is 4, and the No. of the vehicle type identification information 20 in FIG. When none of the “axle distance conditions (m)” of 6 to 9 is satisfied, As shown in FIG. 10, it is determined that the “vehicle type” of the vehicle 10 is a large vehicle, and the “vehicle type” is “other 4-axle vehicle” other than a normal truck, a single vehicle, and a semi-trailer.

  The item “vehicle type classification” of the vehicle type identification information 20 indicates the classification of the vehicle type of the vehicle 10 specified from the “number of axes” and the “distance condition between axles (m)”.

  The item “vehicle type” of the vehicle type determination information 20 indicates the vehicle type of the vehicle 10 specified from the “number of axes” and the “distance condition between axles (m)”. For example, the No. of the vehicle type identification information 20 shown in FIG. As defined in 1, if the number of axles is two and the distance L1 between the axles is n1 meters or less, the vehicle type classification is either a light vehicle, a normal vehicle, or a medium-sized vehicle, There are passenger cars or rough terrain cranes. Further, the vehicle type identification information 20 of No. As defined in 2, if the number of axles is two and the distance L1 between the axles is longer than n1 meters, the vehicle type classification is a medium-sized vehicle or a large vehicle, and the assumed vehicle type is a single vehicle Ordinary trucks, buses, tractors. Here, the single vehicle is a general term for a lorry having a structure that cannot be separated into a towed portion and a towed portion.

  The vehicle type shown in FIG. 18 is an example, and the vehicle type identification information 20 may adopt a configuration that stores more vehicle types.

  Here, the contents of the vehicle type discrimination process will be specifically described with reference to FIGS. 14 and 18. As described in FIG. 14, the axle detection unit 95 detects the axles 11 a to 11 d from the whole view image A <b> 1 and measures the distances L <b> 1 to L <b> 3 between the axles. The determination unit 96 acquires the detection results of the axles 11a to 11d from the axle detection unit 95, searches the vehicle type determination information 20 shown in FIG. 18, and determines the “number of axes” and the “distance between axles condition (m ) ”Combination. The number of axles 11a to 11d of the vehicle 10 shown in FIG. 14 is four, and the distances between the axles are n2 ≦ L1 <n3, n2 ≦ L2 <n3, and 0 <L3 <n2. In this case, the panoramic image A1 shown in FIG. 6 satisfies the condition of the combination of “number of axes” and “distance condition between axles (m)”. Therefore, the determination unit 96 determines that the “vehicle type classification” of the panoramic image A1 is an oversized vehicle and the “vehicle type” is a single vehicle or a semi-trailer.

  On the other hand, when the vehicle 10 shown in the panoramic image A1 separates the towed vehicle that is the rear loading portion and travels independently only with the front towing vehicle, the number of axles of the vehicle 10 is as follows. 2 axles 11a to 11b. In this case, the determination unit 96 searches the vehicle type determination information 20 and determines that “the number of axes” is applicable to the vehicle 10 in which the “number of axes” is two and the distance L1 between the axes is “n2 ≦ L1 <n3”. The combination of “distance condition between axles (m)” is acquired. In this case, the vehicle 10 has the vehicle type identification information 20 of No. 2 satisfies the condition of the combination of “number of axes” and “distance condition between axles (m)”. Therefore, the determination unit 96 determines that the “vehicle type classification” is a medium-sized vehicle or a large vehicle, and the “vehicle type” is a single vehicle, a normal truck, a bus, or a tractor.

  Further, according to the vehicle type discrimination information 20 shown in FIG. 3, no. 6, no. 7, no. 11 is satisfied. That is, as described above, the determination unit 96 includes two or more locations where the vehicle 10 of the vehicle type determination information 20 includes three or more axles and the distance between the axles in the vehicle 10 is n2 meters or more. Or when it is determined that the number of axles of the vehicle 10 is five or more, the vehicle 10 is determined to be an oversized vehicle.

  The determination unit 96 outputs the result of the vehicle type process to an external device (not shown). Alternatively, the determination unit 96 may adopt a configuration in which the result of the vehicle type determination process is displayed on a display unit (not shown) of the vehicle type determination device 103. In addition, when the determination unit 96 determines a specific vehicle type classification or vehicle 10 of a vehicle type, a configuration in which a notification unit (not shown) of the vehicle type determination device 103 notifies may be adopted.

  As described above, in the vehicle type discriminating apparatus 103 in the present embodiment, the discriminating unit 96 discriminates the vehicle type based on the number of axles and the distance between the axles, so the shape of other parts of the vehicle 10 affects the vehicle type discrimination. do not do. For this reason, the interpolation unit 94 may adopt a configuration in which a specific range of the panoramic image D2 is interpolated so as to improve the resolution around the axle, instead of interpolating the entire panoramic image D2 uniformly. For example, the interpolation unit 94 may employ a configuration for interpolating a range from the road surface on the image of the full-view image D2 to the maximum value of the assumed axle height. In this case, a configuration in which the storage unit 90 stores the assumed maximum value of the axle height may be employed. Or the interpolation part 94 may employ | adopt the structure which detects an axle shaft by pattern matching from the foreground image D2 before interpolation, and interpolates the range close | similar to an axle shaft. By limiting the range to be interpolated, it is possible to reduce the load of matching processing and image interpolation processing performed by the interpolation unit 94 as compared to the case of interpolating the entire panoramic image D2.

  Next, the flow of the vehicle type discrimination process of the present embodiment configured as described above will be described. FIG. 19 is a flowchart illustrating an example of the procedure of the vehicle type identification process in the vehicle type identification device 103 of the present embodiment. The process of this flowchart starts when the laser scanner 102 detects that the vehicle 10 has entered the predetermined position T, for example.

  In S <b> 10, the acquisition unit 91 acquires a captured image from the camera 101. The acquisition unit 91 sends the acquired captured image to the panoramic image estimation unit 93.

  In S <b> 11, the panoramic image estimation unit 93 extracts the feature point P of the vehicle image included in the captured image acquired by the acquisition unit 91. Further, the panoramic image estimation unit 93 estimates the panoramic image D1 of the vehicle 10 by specifying the position of each extracted feature point P based on the positional relationship between the vehicle 10 and the camera 101.

  In S12, the panoramic image estimation unit 93 estimates the panoramic image D2 that is an image in the side surface direction of the vehicle 10 by rotating the panoramic image D1. The panoramic image estimation unit 93 sends the panoramic image D2 to the interpolation unit 94. The panoramic image estimation unit 93 sends the vehicle length L of the vehicle 10 measured from the panoramic image D2 to the image generation unit 92.

  In S <b> 13, the acquisition unit 91 acquires the detection point distance and reflection intensity of each detection point p from the laser scanner 102. The acquisition unit 91 sends the acquired detection point distance and reflection intensity of each detection point p to the image generation unit 92.

  In S <b> 14, the image generation unit 92 changes the density of the detection point p of the vehicle 10 based on the detection point distance of the detection point p based on the detection point distance of each detection point p acquired by the acquisition unit 91. Point cloud data g, which is a displayed image, is generated. Further, the image generation unit 92 is an image in which the density of the detection point p of the vehicle 10 is varied according to the reflection intensity of the detection point p based on the reflection intensity of each detection point p acquired by the acquisition unit 91. The reflectance image G is generated.

  In S <b> 15, the image generation unit 92 performs a process of normalizing the reflectance image G using the vehicle length L acquired from the panoramic image estimation unit 93. The image generation unit 92 sends the point cloud data g and the normalized reflectance image G to the interpolation unit 94.

  In S <b> 16, the interpolation unit 94 executes a matching process that collates the feature point P of the panoramic image D <b> 2 with the detection point p of the reflectance image G after the normalization process. Based on the result of the matching process, the interpolation unit 94 performs an image interpolation process for interpolating the feature point P of the panoramic image D2 using the detection point p included in the point cloud data g. The interpolation unit 94 generates a panoramic image A with improved resolution by image interpolation processing. The interpolation unit 94 sends the panoramic image A to the axle detection unit 95.

  In S17, the axle detection unit 95 detects the number and position of axles from the full-view image A. In addition, the axle detection unit 95 measures the distance between the axles converted to the actual size of the vehicle 10. The axle detection unit 95 sends the measurement results of the number of axles and the distance between the axles to the determination unit 96.

  In S <b> 18, the determination unit 96 refers to the vehicle type determination information 20 stored in the storage unit 90, and specifies the vehicle type classification and the vehicle type that satisfy the conditions for the number of axles acquired from the axle detection unit 95 and the distance between the axles. Car type discrimination processing is performed.

  In S19, the determination unit 96 outputs the result of the vehicle type determination process to an external device (not shown).

  As described above, according to the vehicle type determination device 103 according to the present embodiment, the determination unit 96 performs the vehicle type determination process based on the number of axles and the distance between the axles. Compared with the case where it performs, it is hard to receive the influence of the change of a vehicle body shape by a secular change, and can obtain the result of the more stable vehicle type discrimination | determination process. Further, since it is not affected by the difference in the shape of the vehicle 10 due to the difference in cargo or the like, the vehicle type determination device 103 according to the present embodiment can improve the accuracy of the vehicle type determination process.

  Further, scanning by the laser scanner 102 may be difficult depending on the color of the vehicle body of the vehicle 10, and particularly for a black vehicle body, the laser light output from the laser scanner 102 is specularly reflected, so that the light returns to the light receiving side. Sometimes distance measurement values cannot be acquired correctly. On the other hand, for the tire (axle) portion, the laser scanner 102 can measure the distance relatively stably due to irregular reflection. According to the vehicle type identification device 103 according to the present embodiment, since the vehicle type identification process is performed based on the number of axles and the distance between the axles, the influence of the specular reflection of the vehicle body of the vehicle 10 is small and more stable. Car type discrimination processing can be performed.

  Further, according to the vehicle type identification device 103 according to the present embodiment, the acquisition unit 91 acquires the detection point distance from the laser scanner 102 to the detection point p and the reflection intensity of the laser at the detection point p. A captured image at a predetermined position T is acquired. The image generation unit 92 generates a reflectance image G, and the panoramic image estimation unit 93 estimates the panoramic image D1 and panoramic image D2 from the captured image. Further, the interpolation unit 94 executes a matching process between the reflectance image G and the whole scene image D2, and interpolates the feature point P of the whole scene image D2 based on the result of the matching process and the detection point distance. The axle detection unit 95 detects the axle from the panoramic image A obtained by interpolating the feature point P, measures the number of axles and the distance between the axles, and the discrimination unit 96 determines the number of axles and the distance between the axles. The vehicle type of the vehicle 10 is determined.

  For this reason, according to the vehicle type identification device 103 according to the present embodiment, it is generated based only on the detection point distance and the reflection intensity estimated based only on the image captured by the camera 101 or acquired by the laser scanner 102. The axle can be detected using an image having a higher resolution than that of the image. That is, it is possible to improve the accuracy of the detection of the axle by the axle detection unit 95 and the measurement of the distance between the axles, and the discrimination unit 96 can perform the vehicle type discrimination process with high accuracy.

  Furthermore, according to the vehicle type identification device 103 according to the present embodiment, the image generation unit 92 performs a normalization process for matching the aspect ratio of the reflectance image G with the actual aspect ratio of the vehicle 10 with respect to the reflectance image G. Apply. The panoramic image estimation unit 93 estimates the panoramic image D1 and the panoramic image D2 from a plurality of still images continuously captured by the camera 101. Then, the interpolation unit 94 performs a matching process for collating the feature point P of the panoramic image D2 with the detection point p of the reflectance image G that has been subjected to normalization. For this reason, according to the vehicle type discriminating apparatus 103 according to the present embodiment, the position of the feature point P of the panoramic image D2 and the position of the detection point p of the point cloud data g can be linked with high accuracy, and image interpolation can be performed with higher accuracy. Processing can be performed.

  Further, according to the vehicle type identification device 103 according to the present embodiment, the determination unit 96 includes two or more locations where the vehicle 10 includes three or more axles and the distance between the axles is a predetermined distance or more. If it is determined that the vehicle 10 is an oversized vehicle, the oversized vehicle can be determined efficiently.

  Furthermore, according to the vehicle type determination device 103 according to the present embodiment, the determination unit 96 performs the vehicle type determination process according to the threshold conditions for the number of axles and the distance between the axles defined in the vehicle type determination information 20. Compared to the case where the vehicle type is determined based on the shape, the vehicle type is less likely to be determined in the vehicle type determination process. In addition, since the vehicle type identification information 20 is configured by numerical data of the number of axles and the distance between the axles, the vehicle type classification, and the vehicle type, the storage unit 90 stores it as compared with the case where the vehicle body shape of each vehicle type is stored. The amount of data can be reduced.

  The axle detection unit 95 of the vehicle type identification device 103 according to the present embodiment may further adopt a configuration in which the turning radius of the vehicle 10 is determined from the number of axles and the distance between the axles. In this case, the vehicle type identification system 1a of the present embodiment is installed near the entrance of a mountain road or the like where a sharp curve exists, and issues a warning from a not-illustrated notification unit when a vehicle 10 exceeding a predetermined turning radius enters. A configuration may be adopted. Alternatively, the vehicle type identification system 1a of the present embodiment may adopt a configuration to which a function of closing a gate (not shown) and stopping the passage of the vehicle 10 is added. The threshold value of the predetermined turning radius may be stored in the storage unit 90. With this configuration, according to the vehicle type identification system 1a of the present embodiment, it is possible to prevent the vehicle 10 that cannot turn a sharp curve with a large turning radius from entering the mountain road and the like, thereby preventing danger. can do.

(Embodiment 2)
In the vehicle type discriminating apparatus 103 of the first embodiment, the reflectance image G is normalized based on the vehicle length of the vehicle 10 estimated from the image captured by the camera 101. However, in the vehicle type discriminating apparatus 1103 of the present embodiment, the speedometer Based on the speed of the vehicle 10 measured by 104, the reflectance image G is normalized.

  FIG. 20 is a diagram illustrating an example of the configuration of the vehicle type identification system 1b according to the present embodiment. As shown in FIG. 20, the vehicle type identification system 1 b of this embodiment includes a camera 101, a laser scanner 102, a vehicle type identification device 1103, and at least one speedometer 104.

  The camera 101 and the laser scanner 102 of the present embodiment have the same configuration as that of the first embodiment described with reference to FIG.

  The speedometer 104 measures the speed of the vehicle 10. For example, as shown in FIG. 20, the speedometer 104 measures the speed when the vehicle 10 enters the predetermined position T (reference numeral 10b) from the position immediately before the predetermined position T (reference numeral 10a). The speedometer 104 transmits the measured speed of the vehicle 10 to the vehicle type identification device 1103. The speedometer 104 is an example of a speed measurement unit.

  The vehicle type identification device 1103 is an example of an electronic computer that executes vehicle type identification processing. The vehicle type identification device 1103 is electrically connected to the camera 101, the laser scanner 102, and the speedometer 104. The installation location of the vehicle type identification device 1103 is the same as the installation location of the vehicle type identification device 103 described in FIG.

  FIG. 21 is a block diagram illustrating an example of a functional configuration of the vehicle type identification device 1103 of the present embodiment. As shown in FIG. 21, the vehicle type identification device 1103 of the present embodiment includes a storage unit 90, an acquisition unit 1091, an image generation unit 1092, a panoramic image estimation unit 93, an interpolation unit 94, an axle detection unit 95, and the like. And a determination unit 96.

  The storage unit 90, the panoramic image estimation unit 93, the interpolation unit 94, the axle detection unit 95, and the determination unit 96 of the present embodiment are the same as the configuration of the first embodiment described in FIG.

  The acquisition unit 1091 of the present embodiment further acquires the speed of the vehicle 10 from the speedometer 104 in addition to the function of the acquisition unit 91 of the first embodiment. The acquisition unit 1091 sends the acquired speed to the image generation unit 1092.

  The image generation unit 1092 acquires the speed of the vehicle 10 from the acquisition unit 1091. The image generation unit 1092 performs normalization processing of the reflectance image G based on the speed of the vehicle 10.

  As described with reference to FIGS. 8 and 9, as the speed of the vehicle 10 increases, the vehicle length l of the image of the vehicle 10 included in the reflectance image G becomes shorter. That is, by measuring the speed of the vehicle 10, the expansion / contraction ratio of the vehicle length l of the vehicle 10 included in the reflectance image G compared to the actual vehicle length of the vehicle 10 can be calculated.

  The image generation unit 1092 calculates the expansion / contraction ratio of the reflectance image G in the time axis direction from the speed of the vehicle 10 and corrects the length of the reflectance image G in the time axis direction based on the calculation result. Thus, the aspect ratio of the reflectance image G becomes the actual aspect ratio of the vehicle 10.

  FIG. 22 is a diagram for explaining an example of normalization processing in the vehicle type identification device 1103 of the present embodiment. FIG. 22 shows a model 500 of the vehicle length l and axle position of the vehicle 10 in the reflectance image G before normalization, and a model 501 of the vehicle length L and axle position of the vehicle 10 after normalization. The image generation unit 1092 calculates a magnification for setting the vehicle length l to the vehicle length L in accordance with the actual aspect ratio of the vehicle 10 from the expansion / contraction rate calculated from the speed of the vehicle 10. Then, as shown in the models 500 to 501, the image generation unit 1092 performs normalization of the reflectance image G by extending the width of the reflectance image G before normalization according to the magnification of the calculation result.

  Further, the vehicle type identification system 1b may employ a configuration including a plurality of speedometers 104 arranged at a constant distance interval. In this case, since it is possible to measure that the vehicle 10 has accelerated and decelerated while passing the predetermined position T, the entire reflectance image G is not expanded at the same ratio as shown in FIG. The degree of extension for each predetermined area can be changed according to the above. Further, in the normalization processing of the first embodiment, the speed change rate of the vehicle 10 is calculated from the change in the width of the axles of the front wheels and the rear wheels. When a plurality of speedometers 104 are provided, the speed change rate can be calculated based on the measurement result of the speedometer 104.

  The flow of the vehicle type determination process in the vehicle type determination device 1103 of the present embodiment configured as described above is the same as the flow of the vehicle type determination process of the first embodiment described with reference to FIG. Further, the speed of the vehicle 10 is acquired from the speedometer 104. In the process of S15, the image generation unit 1092 performs the normalization process of the reflectance image G based on the speed of the vehicle 10 acquired by the acquisition unit 1091.

  Thus, according to the vehicle type identification device 1103 of the present embodiment, the acquisition unit 1091 acquires the speed of the vehicle 10 from the speedometer 104, and the image generation unit 1092 is based on the speed of the vehicle 10 acquired by the acquisition unit 1091. Since the normalization processing of the reflectance image G is performed, the reflectance image G can be expanded and contracted in accordance with the actual speed of the vehicle 10. For this reason, according to the vehicle type discrimination device 1103 of the present embodiment, more accurate normalization processing can be performed, and the accuracy of the subsequent matching processing with the whole-view image D2 can be improved. That is, according to the vehicle type discriminating apparatus 1103 of this embodiment, the accuracy of measurement of the number of axles and the distance between the axles is improved by generating a higher-resolution panoramic image A, and more accurate vehicle type discrimination is performed. It can be carried out.

(Embodiment 3)
Although the vehicle type discrimination device 103 of the first embodiment discriminates the vehicle type of the vehicle 10, the vehicle type discrimination device 2103 of this embodiment further counts the vehicle type classification of the vehicle 10 or the number of passing vehicles for each vehicle type.

  The vehicle type identification system 1c of this embodiment includes a camera 101, a laser scanner 102, and a vehicle type identification device 2103.

  The camera 101 and the laser scanner 102 of the present embodiment have the same configuration as that of the first embodiment described with reference to FIG.

  The vehicle type identification device 2103 is an example of an electronic computer that executes vehicle type identification processing. The vehicle type identification device 2103 is electrically connected to the camera 101 and the laser scanner 102. The installation location of the vehicle type identification device 2103 is the same as the installation location of the vehicle type identification device 103 described in FIG.

  FIG. 23 is a block diagram illustrating an example of a functional configuration of the vehicle type identification device 2103 of the present embodiment. As shown in FIG. 23, the vehicle type identification device 2103 of this embodiment includes a storage unit 90, an acquisition unit 91, an image generation unit 92, a panoramic image estimation unit 93, an interpolation unit 94, and an axle detection unit 95. A discriminator 96 and a counter 97.

  The storage unit 90, the acquisition unit 91, the image generation unit 92, the panoramic image estimation unit 93, the interpolation unit 94, the axle detection unit 95, and the determination unit 96 of the present embodiment are the same as those described in FIG. The configuration is the same as that of FIG.

  The counter 97 obtains the vehicle type discrimination result of the discrimination unit 96 and counts (counts) the number of passing vehicles for each vehicle type division or each vehicle type. The counter 97 may count the number of passing vehicles based on either one of the vehicle type classification or the vehicle type, or may count based on both categories. Alternatively, the counter 97 may employ a configuration that counts only the vehicles 10 of a specific vehicle type classification or vehicle type.

  The counter 97 associates the count result of each vehicle type or vehicle type of the vehicle 10 with the time when each vehicle 10 is counted. The counter 97 may employ a configuration that calculates the total value of the number of passing vehicles for each vehicle type classification or for each vehicle type at predetermined time intervals. Alternatively, the counter 97 may adopt a configuration in which the vehicle type classification or the vehicle type and the time are linked each time one vehicle 10 is counted.

  The counter 97 outputs the count result to an external device (not shown). Alternatively, the counter 97 may employ a configuration that displays the count result on a display unit (not shown) of the vehicle type identification device 2103.

  FIG. 24 is a diagram for explaining an example of the counting process for each vehicle type in the vehicle type identification device 2103 of this embodiment. The axle detection unit 95 measures the number of axles and the distance of the axles from the panoramic image A, and the discrimination unit 96 identifies the vehicle type classification to which the vehicle 10 corresponding to the panoramic image A corresponds by the vehicle type discrimination process. The counter 97 acquires the determination result of the determination unit 96, and counts the number of passing vehicles for each vehicle type division.

  FIG. 25 is a graph showing an example of a count result of the number of vehicles passing by time for each vehicle type classification in the vehicle type identification device 2103 of this embodiment. In FIG. 25, the horizontal axis represents time, and the vertical axis represents the number of passing vehicles. The solid line 600 in FIG. 25 shows the transition of the value of the number of passing vehicles that is the sum of all the vehicle types, and the broken line 601 shows the transition of the value of the passing vehicle of the vehicle 10 whose vehicle type classification is the oversized vehicle. In general, oversized vehicles such as lorries often travel in the middle of the night, and as shown in FIG. 25, the peak of the total number of passing vehicles and the number of passing vehicles of oversized vehicles may be in different times as shown in FIG. is there. Even if the total number of passing vehicles is the same for all vehicle types, the road may be more congested if extra large vehicles pass more. In the vehicle type discriminating apparatus 2103 of this embodiment, the counter 97 counts the number of passing vehicles for each vehicle type division, so that it can be more accurately compared with the case where the road congestion situation is grasped only from the total number of passing vehicles of all vehicle types. The situation of road congestion can be grasped.

Next, the flow of the vehicle type discrimination process of the present embodiment configured as described above will be described.
FIG. 26 is a flowchart illustrating an example of the procedure of the vehicle type identification process in the vehicle type identification device 2103 of the present embodiment. The process from acquisition of the captured image of the camera in S20 to the vehicle type determination process in S28 is performed in the same manner as S10 to S18 in the vehicle type determination process of the first embodiment described in FIG.

  If the discrimination | determination part 96 performs the vehicle type discrimination | determination process by S28, the counter 97 will acquire the vehicle classification or vehicle type which the discrimination | determination part 96 discriminate | determined by S29, and will count the number of passing vehicles for every vehicle classification or each vehicle type. The counter 97 associates the count result with the time at the time of counting.

  In S30, the determination unit 96 outputs the result of the vehicle type determination process to an external device (not shown). The counter 97 outputs the count result and the time at the time of counting to an external device (not shown).

  Thus, according to the vehicle type discriminating apparatus 2103 according to the present embodiment, the counter 97 counts the number of passing vehicles for each vehicle type division or for each vehicle type, and thus the number of passing vehicles for each vehicle type division can be measured. For this reason, according to the vehicle type identification device 2103 according to the present embodiment, it is possible to grasp the road congestion state more accurately as compared with the case where the road congestion state is grasped only from the total number of passing vehicles of all vehicle types.

  Furthermore, according to the vehicle type identification device 2103 according to the present embodiment, the counter 97 associates the vehicle type division of each vehicle 10 or the count result for each vehicle type with the time at which each vehicle 10 is counted. The number of passing vehicles can be measured for each vehicle type or vehicle type.

  The vehicle type identification device 2103 of this embodiment can provide the count result of the counter 97 and the time at the time of counting as, for example, road traffic information. As described in FIG. 25, by using the number of passing vehicles for each vehicle type classification or for each vehicle type, a more accurate road compared to the case of grasping the road congestion status from the total number of passing vehicles for all vehicle types. The congestion situation can be provided as road traffic information. By installing the vehicle type identification system 1c of the present embodiment on a plurality of roads and counting the number of passing vehicles for each vehicle type division or for each vehicle type at each installation location, the accuracy of road traffic information is further improved. Accurate traffic jam prediction can be performed.

  Moreover, you may integrate the number of passing vehicles for every vehicle type classification per time or for every vehicle type in the installation place of the vehicle type discrimination system 1c with map information. In this case, it is possible to grasp the number of passing vehicles for each vehicle type classification or for each vehicle type at each point corresponding to the installation location of the vehicle type identification system 1c on the map. For example, by providing the information to the driver of an oversized vehicle, the driver can know a more accurate road congestion situation and can travel while avoiding a crowded road.

  On the specific road, if the vehicle 10 having an oversized vehicle type is not traveling at all, there is a possibility that the oversized vehicle cannot travel because the road is narrow. In this case, the driver of the oversized vehicle can travel while avoiding the road by knowing the number of passing vehicles for each type of vehicle. That is, according to the vehicle type identification system 1c of the present embodiment, it is possible to provide assistance for the driver to select an appropriate route. The integration of the count result and the map information may employ a configuration performed by an external device (not shown), or may adopt a configuration where the vehicle type identification device 2103 further acquires map information and performs an integration process.

  Further, in general, the traffic on an oversized vehicle has a higher load on the road than the traffic of the vehicle 10 in another vehicle type. According to the vehicle type discriminating apparatus 2103 according to the present embodiment, the number of passing vehicles in a specific vehicle type classification can be grasped, so that it can also be used for analyzing the load situation on the road. For example, the output result of the vehicle type discriminating apparatus 2103 can be used for the calculation of the maintenance time so that the maintenance of the road on which the oversized vehicle passes is performed more frequently than the road on which the oversized vehicle passes less. .

(Embodiment 4)
Although the vehicle type discrimination device 103 of the first embodiment discriminates the vehicle type of the vehicle 10, the vehicle type discrimination device 3103 of the present embodiment further measures the weight of the vehicle 10 by the weight measuring devices 105a to 105c.

  FIG. 27 is a diagram illustrating an example of a configuration of the vehicle type identification system 1d according to the present embodiment. As shown in FIG. 27, the vehicle type identification system 1d of this embodiment includes a camera 101, a laser scanner 102, a vehicle type identification device 3103, and weight measuring devices 105a to 105c.

  The camera 101 and the laser scanner 102 of the present embodiment have the same configuration as that of the first embodiment described with reference to FIG.

  In FIG. 27, the vehicle type identification system 1d includes three weight measuring units 105a to 105c, but this is an example, and the vehicle type identification system 1d includes at least one weight measuring unit 105. It ’s fine. Here, the weight measuring device 105 is a generic name of the weight measuring devices 105a to 105c. The weight measuring device 105 is installed on a road (vehicle passage) at a predetermined position T, and measures the weight of the vehicle 10 when the axle of the vehicle 10 passes over the weight measuring device 105. The weight measuring device 105 may be a plate-like device installed on the road, or may be configured to be embedded in the road. The weight measuring device 105 transmits the measured weight of the vehicle 10 to the vehicle type identification device 3103. The weight measuring device 105 is an example of a weight measuring unit.

  As in the weight measuring device 105b of FIG. 27, it is desirable that at least one weight measuring device 105 is installed at a position in front of the laser scanner 102. Further, since there is a change in the value of the weight measured due to the shaking of the vehicle 10, more accurate weight can be measured by measuring with a plurality of units such as the weight measuring devices 105a to 105c in FIG. When any of the weight measuring devices 105a to 105c fails, the measurement value of the weight measuring device 105a to 105c that is operating normally is adopted, and the configuration that can continue the operation of the vehicle type identification system 1d is adopted. May be.

  The reason why it is desirable to install the weight measuring device 105 at a position in front of the laser scanner 102 will be described with reference to FIGS. 28 and 29. FIG.

  FIG. 28 is a diagram illustrating an example of a plurality of still images continuously captured by the camera 101 of the present embodiment. Assume that the camera 101 captures continuous images 1200 to 1203 at times t0 to t3, respectively. At this time, the time when the vehicle 10 entered the field of view of the camera 101 and the time when it exited can be acquired, but it is difficult to detect the exact position of the vehicle 10 at any time between time t0 and time t3.

On the other hand, since the laser scanner 102 acquires the position information of the vehicle 10 at a cycle shorter than that of the camera 101, it is possible to acquire the time at which each axle passes in front of the laser scanner 102.
FIG. 29 is a diagram illustrating an example of a detection result of the laser scanner 102 according to the present embodiment. As shown in FIG. 29, the point cloud data g generated from the distance information acquired by the laser scanner 102 scanning the vehicle 10 includes an axle 701. Further, as indicated by reference numeral 702, the weight measuring device 105b measures the weight of the vehicle 10 at time t1.

  As shown in FIG. 27, when the weight measuring device 105b is installed at the front position of the laser scanner 102, the time t1 when the axle of the vehicle 10 passes in front of the laser scanner 102, and the weight measuring device 105b measures the weight of the axle. The time t1 is almost the same. Therefore, the weight measuring device 105b measures the weight by synchronizing the time t1 when the laser scanner 102 scans the axle 701 shown in FIG. 29 with the time t1 when the weight measuring device 105b measures the weight of the axle 701. It can be specified that the axle 701 is the axle 701 scanned by the laser scanner 102. For the above reasons, it is desirable that at least one weight measuring device 105 is installed at a position in front of the laser scanner 102.

  Returning to FIG. 27, the vehicle type identification device 3103 is an example of an electronic computer that executes a vehicle type identification process. The vehicle type identification device 3103 is electrically connected to the camera 101, the laser scanner 102, and the weight measuring devices 105a to 105c. The installation location of the vehicle type identification device 3103 is the same as the installation location of the vehicle type identification device 103 described in FIG.

  FIG. 30 is a block diagram illustrating an example of a functional configuration of the vehicle type identification device 3103 of the present embodiment. As shown in FIG. 30, the vehicle type identification device 3103 of this embodiment includes a storage unit 1090, an acquisition unit 2091, an image generation unit 92, a panoramic image estimation unit 93, an interpolation unit 94, and an axle detection unit 1095. A discriminating unit 1096, an axle-specific weight measuring unit 98, and an overload determining unit 99.

  The image generation unit 92, the panoramic image estimation unit 93, and the interpolation unit 94 of the present embodiment are the same as the configuration of the first embodiment described with reference to FIG.

  In addition to the storage contents of the storage unit 90 of the first embodiment, the storage unit 1090 of the present embodiment further assumes the vehicle type classification or the vehicle body of the vehicle type as the upper limit value of the weight of the load amount for each vehicle type or for each vehicle type. The overload reference value which is a weight obtained by adding the upper limit value of the weight to be stored is stored. Further, the storage unit 1090 further stores a limit weight that is an upper limit value of a weight that can safely be supported per axle for each vehicle type division or each vehicle type. The limit weight may be defined as a different value depending on the size of the axle. The calculation method of the above overload reference value and the limit weight is an example, and is not limited to this.

  In addition to the function of the acquisition unit 91 of the first embodiment, the acquisition unit 2091 of the present embodiment further acquires the weight of the vehicle 10 and the time when the weight of the vehicle 10 is measured from the weight measuring devices 105a to 105c. The acquisition unit 2091 sends the acquired weight to the axle-specific weight measurement unit 98.

  When the axle detection unit 1095 detects the axle from the whole scene image A interpolated by the point cloud data g, the axle detection unit 1095 associates each axle with the time when the detection point p corresponding to each axle is detected by the laser scanner 102. The axle detection unit 1095 sends the time when each axle was scanned by the laser scanner 102 to the axle-by-axle weight measurement unit 98 in addition to the number of axles and the distance between the axles. The method of associating the time at which each axle is scanned with the detection result of each axle is not limited to this, and a configuration using point cloud data g or reflectance image G may be adopted.

  The axle-specific weight measuring unit 98 acquires the number of axles, the distance between the axles, and the time when each axle was scanned by the laser scanner 102 from the axle detecting unit 1095. The axle-specific weight measuring unit 98 acquires the weight of the vehicle 10 acquired by the acquiring unit 2091 from the weight measuring devices 105a to 105c and the time when the weight of the vehicle 10 is measured from the acquiring unit 2091. The axle-by-axle weight measurement unit 98 includes the number of axles acquired from the axle detection unit 1095, the distance between the axles, the time at which each axle was scanned by the laser scanner 102, the weight of the vehicle 10 acquired by the acquisition unit 2091, and the vehicle 10 The weight of each axle is measured by comparing the time when the weight of the vehicle is measured.

  Here, as described in FIGS. 16 and 17, the vehicle 10 may lift a part of the axle from the road surface by the lift axle function. There is a space from the road surface 400 shown in FIGS. 16 and 17 to the lower limit 401 of the height of the lifted axle. However, a specific standard for the distance from the road surface 400 to the lower limit 401 of the height of the lifted axle has not been determined. For this reason, the distance from the road surface 400 to the lower limit 401 of the height of the lifted axle shaft becomes very short, and it is determined from the shape information of the vehicle 10 such as the panoramic image A that the axle is floating from the road surface 400. May be difficult. In this case, although a part of the axle of the vehicle 10 actually floats from the road surface 400, the axle detection unit 1095 may determine that the floating axle is an axle that is in contact with the road surface 400.

  Therefore, the axle-based weight measurement unit 98 of the present embodiment determines whether or not each axle is floating from the road surface, based on the measurement result of the weight of each axle of the vehicle 10. That is, even when the axle-based weight measuring unit 98 determines that the axle detection unit 1095 is an axle that is grounded on the road surface, if the weight measuring devices 105a to 105c are not measuring the weight of the axle, It is determined that the axle is floating from the road surface.

  When the axle-specific weight measuring unit 98 determines that the axle is floating from the road surface, it again measures the number of axles excluding the axle floating from the road surface and the distance between the axles.

  The axle-specific weight measuring unit 98 sends the measured number of axles of the vehicle 10 and the distance between the axles to the determining unit 1096. When there is no axle with the axle floating from the road surface, the axle-specific weight measurement unit 98 sends the number of axles measured by the axle detection unit 1095 and the distance between the axles to the determination unit 1096. The axle-specific weight measuring unit 98 sends the weight of each axle of the vehicle 10 to the overload determining unit 99.

  Returning to FIG. 30, the determination unit 1096 determines the vehicle type classification and the vehicle type of the vehicle 10 based on the number of axles measured by the axle-specific weight measurement unit 98 and the distance between the axles. The discriminating unit 1096 performs the vehicle type discriminating process based on the criteria defined in the vehicle type discriminating information 20 similarly to the discriminating unit 96 of the first embodiment described with reference to FIG. The determination unit 1096 sends the result of the vehicle type determination process to the overload determination unit 99.

  Returning to FIG. 30, the overload determination unit 99 acquires the result of the vehicle type determination process from the determination unit 1096. Further, the overload determination unit 99 acquires the weight of each axle of the vehicle 10 from the axle-specific weight measurement unit 98. The overload determination unit 99 determines whether the vehicle 10 is overloaded based on the vehicle type determination result of the determination unit 1096 and the weight of the vehicle 10.

  Specifically, the overload determination unit 99 acquires an overload reference value that matches the vehicle type classification or vehicle type of the vehicle type determination result of the determination unit 1096 from the storage unit 1090 and compares it with the weight of the vehicle 10. That is, when the overload determination unit 99 determines that the weight of the vehicle 10 acquired by the acquisition unit 2091 exceeds the overload reference value set for the vehicle type classification or vehicle type determined by the determination unit 1096. Then, it is determined that the vehicle 10 is overloaded. Alternatively, the overload determination unit 99 may adopt a configuration in which the vehicle 10 having a weight close to the overload reference value by a certain amount or more is determined even if the overload reference value is not reached.

  The overload determination unit 99 also measures the vehicle measured by the axle-specific weight measurement unit 98 based on the vehicle type determination result acquired from the determination unit 1096 and the weight for each axle of the vehicle 10 acquired from the axle-specific weight measurement unit 98. It is determined whether the weight for each of the ten axles has reached the limit weight that is the upper limit value of the weight for each axle.

  Specifically, the overload determination unit 99 obtains a limit weight that matches the vehicle type classification or vehicle type of the vehicle type determination result of the determination unit 1096 from the storage unit 1090 and compares it with the weight of each axle of the vehicle 10. Then, it is determined whether or not the weight of each axle of the vehicle 10 has reached the limit weight. Further, even when the weight of each axle of the vehicle 10 does not reach the limit weight, the overload determination unit 99 may adopt a configuration that is a determination target if the weight difference between the axles is significant. A configuration may be employed in which the storage unit 1090 stores the threshold value for the weight difference between the axles as a predetermined weight difference.

  Further, the overload determination unit 99 may acquire the size (diameter) of the axle measured by the axle detection unit 95 as described in FIG. In that case, the limit weight defined according to the size of the axle is further obtained from the storage unit 1090 for each vehicle type or vehicle type, and it is determined whether the weight for each axle has reached the limit weight.

  The overload determination unit 99 outputs an overload determination result, a determination result as to whether or not each axle has reached a limit weight, and a weight measurement result for each axle to an external device (not shown). Alternatively, the overload determination unit 99 employs a configuration that issues a warning by sending a signal to a notification unit (not shown) when the vehicle 10 is overloaded or when any axle has reached the limit weight. You may do it. Further, a configuration may be adopted in which the weight measurement result for each axle is directly output to the external device (not shown) by the axle-specific weight measurement unit 98.

  FIG. 31 is a diagram illustrating an example of weight analysis for each axle in the vehicle type identification device 3103 of the present embodiment. As shown in FIG. 31, the panoramic image A of the vehicle 10 includes an axle 801 corresponding to the front wheel of the vehicle 10 and an axle 802 corresponding to the rear wheel. The lower part of FIG. 31 shows the result of weight measurement for each axle by the axle-specific weight measuring unit 98. A rectangle 803 located below the axle 801 represents the weight of the axle 801, and a rectangle 804 located below the axle 802 represents the weight of the axle 802. When there is a difference in the measurement results by the weight measuring devices 105a to 105c, the weight value measured lightest at the bottom of the rectangles 803 to 804 and the weight value measured most heavily at the upper side of the rectangles 803 to 804 Show.

  As shown in FIG. 31, the weight of the axle 801 corresponding to the front wheel of the vehicle 10 does not reach the limit weight. However, it can be seen that the weight of the axle 802 corresponding to the rear wheel of the vehicle 10 has reached the limit weight.

  Next, the flow of the vehicle type discrimination process of the present embodiment configured as described above will be described. FIG. 32 is a flowchart illustrating an example of the procedure of the vehicle type identification process in the vehicle type identification device 3103 of the present embodiment. The process from acquisition of the captured image of the camera in S40 to detection of the axle in S47 is performed in the same manner as S10 to S17 in the vehicle type determination process of the first embodiment described in FIG.

  When the axle detection unit 1095 measures the number of axles and the distance between the axles in S47, the acquisition unit 2091 obtains the weight of the vehicle 10 and the time when the weight of the vehicle 10 is measured from the weight measuring devices 105a to 105c in S48. Acquired and sent to the weight measurement unit 98 by axle.

  In S49, the axle-specific weight measurement unit 98 collates the weight of the vehicle 10 acquired from the acquisition unit 2091, the time when the weight of the vehicle 10 was measured, and the axle detection result acquired from the axle detection unit 1095.

  In S50, the axle-specific weight measuring unit 98 measures the weight of each axle. In addition, when detecting the axle floating on the road surface, the weight measuring unit 98 for each axle measures the number of axles excluding the axle and the distance between the axles.

  In S51, the determination unit 1096 determines the vehicle type classification and the vehicle type of the vehicle 10 based on the measurement results of the number of axles and the distance between the axles acquired from the axle-based weight measurement unit 98.

  In S52, the overload determination unit 99 determines whether or not the vehicle 10 is overloaded. Further, the overload determination unit 99 determines whether or not the weight of each axle has reached the limit weight.

  In S53, the determination unit 1096 outputs the vehicle type determination result to an external device (not shown). The overload determination unit 99 outputs an overload determination result, a determination result as to whether or not each axle has reached a limit weight, and a weight measurement result for each axle to an external device (not shown).

  Thus, according to the vehicle type identification device 3103 of this embodiment, the acquisition unit 2091 acquires the weight of the vehicle 10 from the weight measuring devices 105a to 105c, and the overload determination unit 99 determines the vehicle type determination result of the determination unit 1096. Since it is determined whether the vehicle 10 is overloaded based on the weight of the vehicle 10, the overloaded vehicle 10 can be determined efficiently. Although the upper limit value of the weight naturally varies depending on the vehicle type classification and the vehicle type, according to the vehicle type identification device 3103 of this embodiment, after the determination unit 1096 determines the vehicle type classification and the vehicle type, the overload determination unit 99 is overloaded. By performing the determination, it is possible to appropriately determine whether or not the vehicle is overloaded.

  Further, according to the vehicle type identification device 3103 of the present embodiment, the acquisition unit 2091 acquires the weight of the vehicle 10 and the time when the weight of the vehicle 10 is measured from the weight measuring devices 105a to 105c, and the weight measurement for each axle is performed. The unit 98 measures the weight of each axle by comparing the weight of the vehicle 10 and the time when the weight of the vehicle 10 is measured with the axle detection result of the axle detection unit 1095, and thus grasps the load applied to each axle. can do. Therefore, according to the vehicle type discriminating apparatus 3103 of the present embodiment, it is determined whether the vehicle 10 is in a dangerous state for traveling even if the total load amount of the load is within the standard or the vehicle 10 with a bad load balance of the load. be able to.

  Furthermore, according to the vehicle type discriminating apparatus 3103 of this embodiment, the axle-based weight measuring unit 98 determines whether the axle is floating from the road surface based on the measurement result of the axle weight, and the axle floating from the road surface is determined. The number of axles and the distance between the axles are measured, and the discrimination unit 1096 discriminates the vehicle type classification and vehicle type of the vehicle 10 based on the measurement result of the axle-specific weight measurement unit 98. Even if the distance between the lifted axle and the road surface is small, the vehicle type classification and vehicle type determination can be performed with high accuracy.

  As described above, according to the vehicle type identification systems 1a to 1d and the vehicle type identification devices 103 to 3103 of the first to fourth embodiments, the accuracy of the vehicle type classification and the vehicle type determination of the vehicle 10 can be improved.

  The vehicle type discriminating devices 103 to 3103 according to the first to fourth embodiments are ordinary computer equipped with a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), an HDD (Hard Disk Drive), and the like. It has a hardware configuration.

  Moreover, the vehicle type discrimination | determination process performed with the vehicle type discrimination | determination apparatuses 103-3103 of the said Embodiment 1-4 is provided as a computer program product as a vehicle type discrimination | determination program previously incorporated in ROM etc. The vehicle type identification program executed by the vehicle type identification devices 103 to 3103 of the first to fourth embodiments is a file in an installable format or an executable format, and is a CD-ROM, flexible disk (FD), CD-R, DVD ( The program may be recorded on a computer readable recording medium such as Digital Versatile Disk) and provided as a computer program product.

  Furthermore, the vehicle type identification program executed by the vehicle type identification devices 103 to 3103 of the above-described first to fourth embodiments is stored on a computer connected to a network such as the Internet and provided as a computer program product by being downloaded via the network. You may comprise so that it may do. Moreover, you may comprise so that the vehicle type discrimination | determination program performed with the vehicle type discrimination | determination apparatuses 103-3103 of the said Embodiment 1-4 may be provided or distributed via networks, such as the internet, as a computer program product.

  The vehicle type determination program executed by the vehicle type determination devices 103 to 3103 of the first to fourth embodiments has a module configuration including the above-described units, and as actual hardware, a CPU (processor) determines the vehicle type from the ROM. By reading and executing the program, the above-described units are loaded onto the RAM, and the units are generated on the RAM.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

1a, 1b, 1c, 1d Vehicle type discrimination system 10 Vehicle 20 Vehicle type discrimination information 90, 1090 Storage unit 91, 1091, 2091 Acquisition unit 92, 1092, 2092 Image generation unit 93 Whole view image estimation unit 94 Interpolation unit 95, 1095 Axle detection unit 96, 1096 discriminating unit 97 counter 98 axle-specific weight measuring unit 99 overloading judging unit 101 camera 102 laser scanner 103, 1103, 2103, 3103 vehicle type discriminating device 104 speedometer 105, 105a, 105b, 105c weight measuring device T predetermined position

Claims (8)

The detection point distance detected by irradiating the laser beam to the vehicle passing through the predetermined position detected by the scanner unit and the reflection intensity of the laser reflected at the detection point are acquired, and the predetermined position is imaged from the imaging unit. An acquisition unit for acquiring a captured image obtained by
An image generation unit for generating a reflectance image that is an image of the predetermined position including the detection point and in which the density of the detection point is changed according to the reflection intensity of the laser reflected at the detection point. When,
A panoramic image estimation unit that estimates a panoramic image that is an image showing the overall shape of the vehicle based on the captured image;
Interpolation for executing the matching process for matching the feature points of the panoramic image and the detection points of the reflectance image, and interpolating the feature points of the panoramic image based on the result of the matching process and the detection point distance And
An axle detection unit that detects an axle from the panoramic image obtained by interpolating the feature points, and measures a number of the axles and a distance between the axles;
A discriminator for discriminating the type of the vehicle based on the number of the axles and the distance between the axles;
A vehicle type identification device. The image generation unit performs a normalization process on the reflectance image so that the aspect ratio of the reflectance image matches the actual aspect ratio of the vehicle,
The panoramic image estimation unit estimates the panoramic image from a plurality of still images continuously captured by the imaging unit,
The interpolation unit performs the matching process for matching the feature points of the panoramic image and the detection points of the reflectance image subjected to the normalization process;
The vehicle type identification device according to claim 1. The determination unit determines that the vehicle is an oversized vehicle when it is determined that the vehicle has three or more axles and the distance between the axles is more than a predetermined distance. Determine that there is,
The vehicle type identification device according to claim 1. The acquisition unit further acquires the speed of the vehicle from the speed measurement unit,
The image generation unit performs the normalization processing of the reflectance image based on the speed of the vehicle acquired by the acquisition unit.
The vehicle type identification device according to claim 1. The acquisition unit further acquires the weight of the vehicle from a weight measurement unit that measures the weight of the vehicle,
An overload determination unit that determines that the vehicle is overloaded when it is determined that the weight of the vehicle acquired by the acquisition unit exceeds an overload reference value determined for the vehicle type determined by the determination unit. Further comprising
The vehicle type identification device according to claim 1. The acquisition unit further acquires a time when the weight of the vehicle is measured from the weight measurement unit,
The number of axles detected by the axle detection unit, the distance between the axles, the time when the axle was scanned by the scanner unit, the weight of the vehicle acquired by the acquisition unit, and the time when the weight of the vehicle was measured And further comprising an axle-by-axle weight measuring unit that measures the weight of each axle by checking,
The overload determination unit determines whether the weight of each axle of the vehicle measured by the axle-specific weight measurement unit reaches a limit weight that is an upper limit value of the weight of each axle.
The vehicle type identification device according to claim 5. The axle-by-axle weight measuring unit determines the number of the axles excluding the axles floating from the road surface and the axles when it is determined from the weight measurement result for each axle that the axles are floating from the road surface. Measure the distance between
The discriminating unit discriminates the vehicle type of the vehicle based on the number of the axles measured by the axle-specific weight measuring unit and the distance between the axles.
The vehicle type identification device according to claim 6. From the scanner unit, the detection point distance detected by irradiating the vehicle with a laser to the vehicle passing through the predetermined position and the reflection intensity of the laser reflected at the detection point are acquired, and the predetermined position is imaged from the imaging unit An acquisition step of acquiring the obtained captured image;
An image generation step of generating a reflectance image that is an image of the predetermined position including the detection point, and is an image in which the density of the detection point is varied according to the reflection intensity of the laser reflected at the detection point. When,
A panoramic image estimation step for estimating a panoramic image, which is an image representing the shape of the entire vehicle, based on the captured image;
Interpolation for executing the matching process for matching the feature points of the panoramic image and the detection points of the reflectance image, and interpolating the feature points of the panoramic image based on the result of the matching process and the detection point distance Steps,
An axle detection step for detecting an axle from the panoramic image interpolating the feature points and measuring the number of axles and the distance between the axles;
A determination step of determining a vehicle type of the vehicle based on the number of the axles and the distance between the axles;
Discriminating method including car.