vehicle

Abstract

This invention provides a detection device and a cargo load factor estimation system that offer high durability, reliably detect the opening and closing status of cargo compartment doors, and suppress the reduction of design freedom around the cargo compartment doors. The detection device includes: a calculation unit that acquires a color image of the interior of a cargo compartment equipped with a freely opening and closing door, and calculates a determination value based on color information of multiple pixels contained in the color image; and a determination unit that determines the door is open if the determination value is greater than a predetermined threshold, and determines the door is closed if the determination value is less than the threshold.

Description

Technical Field

[0001] This invention relates to a detection device and a cargo load factor estimation system for use in vehicles equipped with cargo compartments. Background Technology

[0002] Previously, vehicles equipped with cargo compartments for loading goods were known. In addition, sensors for detecting the opening and closing status of doors (hereinafter also referred to as "cargo compartment doors") provided at the opening of the cargo compartment were known (for example, see Patent Documents 1 and 2).

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 4-292772

[0006] Patent Document 2: Japanese Patent Application Publication No. 7-331960 Summary of the Invention

[0007] The problem the invention aims to solve

[0008] However, the aforementioned sensors are limited to locations susceptible to impact when the cargo door is opened and closed (e.g., around the cargo door). Therefore, the durability and stability of the sensors themselves or the wiring connected to them may be reduced. Furthermore, placing the sensors around the cargo door reduces the design flexibility of that area.

[0009] One objective of this invention is to provide a detection device and a cargo load factor estimation system that are highly durable, capable of reliably detecting the opening and closing status of cargo compartment doors, and capable of suppressing the reduction of design freedom around the cargo compartment doors.

[0010] Solution for solving the problem

[0011] One aspect of the detection device of the present invention includes: a calculation unit that acquires a color image of the interior of a cargo compartment provided with a door that can be opened and closed freely, and calculates a determination value based on color information of a plurality of pixels contained in the color image; and a determination unit that determines that the door is in an open state when the determination value is greater than a predetermined threshold, and determines that the door is in a closed state when the determination value is less than the threshold.

[0012] One aspect of the present invention provides a cargo load factor estimation system comprising a detection device and a cargo load factor estimation device. The detection device includes: a calculation unit that acquires a color image of the interior of a cargo compartment equipped with a freely opening and closing door, and calculates a determination value based on color information of a plurality of pixels contained in the color image; and a determination unit that determines that the door is in an open state if the determination value is greater than a predetermined threshold, and determines that the door is in a closed state if the determination value is less than the threshold. When the determination unit determines that the door is in a closed state, the cargo load factor estimation device estimates the cargo load factor of a vehicle equipped with the cargo compartment.

[0013] Invention Effects

[0014] According to the present invention, the opening and closing status of the cargo box door is highly durable and can be reliably detected, and the reduction of design freedom around the cargo box door can be suppressed. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating the interior of a vehicle and cargo compartment according to an embodiment of the present invention.

[0016] Figure 2 This is a block diagram illustrating an example of the structure of a cargo rate estimation system according to an embodiment of the present invention.

[0017] Figure 3 This is an example of an image showing the interior of a cargo compartment according to an embodiment of the present invention.

[0018] Figure 4 This is a diagram illustrating an example of the structure of the determination region in an embodiment of the present invention.

[0019] Figure 5 This is a flowchart illustrating an example of the operation of the detection device according to an embodiment of the present invention. Detailed Implementation

[0020] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. It should be noted that common components in the various figures are labeled with the same reference numerals, and descriptions of these components are omitted where appropriate.

[0021] First, use Figure 1 The vehicle V in this embodiment will be described. Figure 1 This is a schematic side view of the vehicle V and the cargo compartment.

[0022] like Figure 1 As shown, vehicle V is a truck equipped with a cab 1 and a cargo box 2. It should be noted that vehicle V is not limited to trucks and can also be other types of vehicles.

[0023] Cargo compartment 2 is, for example, box-shaped, with an opening on its rear side (illustration omitted; the same applies below). Loading and unloading of goods is carried out through this opening. Figure 1 As an example, a plurality of cargo 5 are shown in a state in which multiple cargoes 5 are arranged near the inner wall surface 6 of the cargo compartment 2 (specifically, the inner wall surface of the front side wall in the cargo compartment 2).

[0024] At the rear of the cargo compartment 2, a door 3 that can be opened and closed freely is provided in a manner corresponding to the position of the opening. For example, the door 3 can be a door that opens by rotating from the center of the opening to the left or right with the left and right ends of the opening as the axis (a so-called left-right opening door), but it is not limited to this.

[0025] At the rear of the cargo compartment 2, a camera 4 is installed capable of capturing images of the interior of the cargo compartment 2 (excluding the door 3 and openings). The camera 4 transmits the images of the interior of the cargo compartment 2 (hereinafter referred to as "cargo compartment images") obtained by capturing the images to the detection device 100 described later (see reference 100). Figure 2 Send. It should be noted, as an example, in Figure 1 The image shows a camera 4 mounted on the ceiling of the cargo compartment 2, but the location of the camera 4 is not limited to this.

[0026] The image inside the cargo compartment is a color image in which each pixel constituting the image has color information. In this embodiment, the example will be an RGB image with R (red), G (green), and B (blue) elements, and the color information is RGB values. Details regarding the image inside the cargo compartment will be discussed later. Figure 3 , Figure 4 describe.

[0027] Additionally, camera 4 is integrated with a depth sensor (not shown). A depth sensor is a sensor capable of measuring the distance from itself to a person or object in two dimensions. The detection results from the depth sensor are output to the cargo rate estimation device 200 (see reference 200). Figure 2 ).

[0028] Although Figure 1 The illustrations are omitted, but vehicle V is also equipped with the detection device 100 and the cargo rate estimation device 200 (described later). Figure 2 ).

[0029] The above provides an explanation of vehicle V.

[0030] Next, use Figure 2 The cargo load factor estimation system S of this embodiment will be described. Figure 2 This is a block diagram representing an example of the configuration of the load factor inference system S.

[0031] Figure 2 The load factor inference system S shown is mounted on Figure 1 The vehicle shown is V.

[0032] Although figures are omitted, the detection device 100 and the load factor estimation device 200 are respectively implemented as hardware, including, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory) storing a computer program, and RAM (Random Access Memory). The functions of the devices described below are implemented by executing the computer program read from the ROM by the CPU in the RAM. For example, the detection device 100 and the load factor estimation device 200 may also be implemented by an ECU (Electronic Control Unit).

[0033] The detection device 100 is a device that calculates a judgment value (details of which will be described later) based on the image inside the cargo compartment acquired from the camera 4, and determines whether the door 3 is in an open or closed state by comparing the judgment value with a threshold.

[0034] like Figure 2 As shown, the detection device 100 has a calculation unit 110 and a determination unit 120.

[0035] The computing unit 110 acquires images of the interior of the cargo compartment from the camera 4.

[0036] Here, use Figure 3 An example of an image of the interior of the cargo compartment captured by camera 4 will be described. Figure 3 This is an example of an image showing the interior of a cargo compartment.

[0037] like Figure 3 As shown, the image inside the cargo compartment contains representations of... Figure 1 Images of the cargo 5 and the inner wall 6 are shown.

[0038] In addition, such as Figure 3 As shown, a determination area 7 containing multiple pixels is set in the image inside the cargo compartment. Here, as an example, the determination area 7 is set in the central part of the image inside the cargo compartment (more specifically, in the image of the central part of the inner wall surface 6).

[0039] The settings (including changes) for the determination area 7 are performed in advance by a computer program, for example. When making these settings, the usage of the user (e.g., the occupants of vehicle V, or the personnel performing loading and unloading operations) and the required accuracy are taken into account.

[0040] The above provides an example of an image of the interior of a cargo compartment.

[0041] Next, use Figure 4 right Figure 3 The determination shown is illustrated using region 7. Figure 4 This is a diagram that schematically illustrates an example of the composition of region 7 used for judgment.

[0042] like Figure 4 As shown, the determination area 7 includes pixels A, B, C, and D.

[0043] In addition, such as Figure 4 As shown, pixels A through D have RGB values. For example, the RGB value of pixel A is (23, 11, 1). For example, the RGB value of pixel B is (18, 12, 1). For example, the RGB value of pixel C is (19, 13, 0). For example, the RGB value of pixel D is (20, 12, 2).

[0044] The above explains the determination area 7.

[0045] Now, return to Figure 2 Explanation.

[0046] Computing unit 110 Figure 3 The identification and determination area 7 in the image of the cargo compartment shown is used to calculate the determination value based on the RGB values ​​of pixels A to D contained in the determination area 7.

[0047] The judgment value is a value compared with a predetermined threshold, such as a statistical measure of multiple pixels (mean, variance, total, etc.).

[0048] For example, the decision value could be the average of the R, G, and B values ​​for pixels A through D. Alternatively, the decision value could be the average of the total RGB values ​​(the sum of the R, G, and B values) for pixels A through D. Or, the decision value could be the median value of the R, G, and B values ​​for pixels A through D. Or, it could be the sum of the R, G, and B values ​​for pixels A through D.

[0049] The threshold can be considered as the value at which gate 3 is closed, and is set based on the results of pre-implemented experiments or simulations.

[0050] The determination unit 120 compares the determination value calculated by the calculation unit 110 with the predetermined threshold.

[0051] If the value used for determination is greater than the threshold, the determination unit 120 determines that the door 3 is in the open state.

[0052] If the determination value is below the threshold, the determination unit 120 determines that the door 3 is in a closed state. Furthermore, in this case, the determination unit 120 sends (outputs) the determination result information indicating that the door 3 is in a closed state to the load factor estimation device 200.

[0053] The cargo rate estimation device 200 is a device that estimates the cargo rate based on the detection results of a depth sensor after receiving a determination result information indicating that the door 3 is in a closed state from the detection device 100 (specifically, the determination unit 120). The cargo rate is the ratio of the volume of the cargo 5 configured in the cargo compartment 2 to the maximum cargo volume of the vehicle V.

[0054] The method for inferring the load factor using the load factor estimation device 200 can employ known techniques. Examples of known techniques include, for instance, Japanese Patent Application Publication No. 2003-35527.<URL:https: / / creanovo.de / portfolio / wabco-cargocam / > ,<URL:https: / / www.ncos.co.jp / news / news_210113.html> The methods disclosed include, but are not limited to, those mentioned above.

[0055] In addition, the structure of the load factor inference system S and the detection device 100 is described.

[0056] Next, use Figure 5 The operation of the detection device 100 will be explained. Figure 5 This is a flowchart illustrating an example of the operation of the detection device 100. Figure 5 The process, for example, begins when vehicle V stops and is repeated while vehicle V is stopped.

[0057] First, the computing unit 110 acquires an image of the interior of the cargo compartment from the camera 4 (step S1).

[0058] Next, the calculation unit 110 calculates based on the images inside the cargo compartment (e.g., Figure 3 The determination of the multiple pixels (e.g., in region 7 of the image shown inside the cargo compartment) is based on the area containing the specified region. Figure 4 The determination value is calculated using the RGB values ​​of pixels A through D shown (step S2). It should be noted that the determination value can also be any of the determination values ​​described above.

[0059] Next, the determination unit 120 determines whether the calculated determination value is below the threshold (step S3).

[0060] If the value is determined to be below the threshold (step S3: "No"), the process returns to step S1.

[0061] If the value used for determination is below the threshold (step S3: "Yes"), the determination unit 120 determines that the door 3 is in the closed state (step S4).

[0062] Next, the determination unit 120 sends the determination result information indicating that the door 3 is in the closed state to the load factor estimation device 200 (step S5). Upon receiving the determination result information, the load factor estimation device 200 estimates the load factor.

[0063] The operation of the detection device 100 has been explained above.

[0064] As detailed above, the detection device 100 of this embodiment is characterized by acquiring a colored image of the interior of the cargo compartment, calculating a determination value based on the color information (e.g., RGB values) of a plurality of pixels contained in the image of the interior of the cargo compartment, and determining that the door 3 is in an open state when the determination value is greater than a threshold value, and determining that the door 3 is in a closed state when the determination value is less than the threshold value.

[0065] Therefore, the detection device 100 of this embodiment can detect the opening and closing state of the door 3 without using sensors installed around the cargo door, such as those in Patent Documents 1 and 2. Thus, it has high durability and can reliably detect the opening and closing state of the cargo door, and can suppress the reduction of design freedom around the cargo door.

[0066] Furthermore, the cargo load inference system S of this embodiment is characterized in that when the detection device 100 determines that the door 3 is in a closed state, the cargo load inference device 200 infers the cargo load rate.

[0067] If the load factor is inferred while the operator is inside compartment 2, the operator will be treated the same as the operator carrying cargo 5, which will reduce the accuracy of the load factor inference. In the load factor inference system S of this embodiment, the load factor is inferred when the door 3 is closed (i.e., when the operator is not inside compartment 2), thus improving the inference accuracy.

[0068] Furthermore, in the cargo rate inference system S of this embodiment, the camera 4 and the depth sensor are integrated, thus simplifying the structure.

[0069] It should be noted that the present invention is not limited to the embodiments described above, and various modifications can be made without departing from its spirit. Modifications will now be described.

[0070] [Variation Example 1]

[0071] In this embodiment, the case in which the detection device 100 and the load factor estimation device 200 are set up separately is described as an example, but it is not limited to this.

[0072] For example, the detection device 100 may also have the function of the load factor inference device 200 (which may also be called a "load factor inference unit"). In this case, the camera 4 and the detection device 100 may also be referred to as a "load factor inference system".

[0073] Furthermore, in this embodiment, the example described is of the detection device 100 and the load factor estimation device 200 being mounted on a vehicle V, but the implementation is not limited to this. For example, the detection device 100 and the load factor estimation device 200 may also be implemented by a computer (e.g., a server) installed outside the vehicle V. In this case, for example, the image of the cargo compartment and the determination result information may be sent to the aforementioned computer by a communication device (not shown) mounted on the vehicle V.

[0074] [Variation Example 2]

[0075] In the implementation method, such as Figure 3 As shown, the example illustrates the case where only a rectangular determination area 7 is set in the central part of the image inside the cargo compartment. However, the position, shape, size, and number of determination areas 7 are not limited to this. Figure 3 As shown. However, preferably, the determination area 7 is an area in the image inside the cargo compartment that is prone to differences in brightness due to the opening and closing of the door 3.

[0076] For example, the determination area 7 could also be an image of the entire cargo compartment.

[0077] Alternatively, for example, the determination area 7 could be the area corresponding to the portion of the cargo compartment 2 where light first reaches when the door 3 is opened. In this case, even if the door 3 is only slightly open, it can be determined that the compartment is open, thus further improving the determination speed.

[0078] [Variation Example 3]

[0079] In one embodiment, the determination unit 120 may also send the determination result information indicating whether the door 3 is in an open or closed state to a device other than the load factor inference device 200.

[0080] For example, the determination unit 120 may also send the determination result information indicating whether the door 3 is in an open or closed state to a notification device (not shown; for example, a display, speaker, etc.) installed in the driver's cab 1. In this case, the notification device displays an image or outputs voice indicating whether the door is in an open or closed state. Thus, the user (for example, a passenger of the vehicle V, or a person performing loading or unloading operations, etc.) can identify the open or closed state of the door 3.

[0081] The above descriptions of the variations have been provided. It should be noted that the above variations can also be combined appropriately.

[0082] This application is based on Japanese patent application (Japan Patent Application No. 2021-049744) filed on March 24, 2021, the contents of which are incorporated herein by reference.

[0083] Industrial applicability

[0084] The detection device and load factor estimation system of the present invention are useful for vehicles equipped with cargo boxes.

[0085] Explanation of reference numerals in the attached figures

[0086] 1. Driver's cab

[0087] 2 cargo compartments

[0088] 3 doors

[0089] 4 cameras

[0090] 5. Cargo

[0091] 6. (Front side wall) Inner wall surface

[0092] 100 Detection Device

[0093] 110 Computing Department

[0094] 120 Judgment Department

[0095] 200 Load Factor Determination Device

[0096] S Load factor inference system

[0097] V vehicle

Claims

1. A vehicle comprising a cab and a box-shaped cargo box with an openable and closable door located behind the cab, the vehicle further comprising a detection device and a cargo load factor estimation device, the detection device comprising: A camera, integrated with a depth sensor, is installed inside the cargo compartment and captures images of the interior of the cargo compartment. The computing unit acquires a color image of a predetermined determination area inside the cargo compartment from the camera, and calculates a determination value based on the color information of multiple pixels contained in the color image. The determination value is a statistical measure of the multiple pixels. as well as The determination unit determines that the door is open if the determination value is greater than a predetermined threshold, and determines that the door is closed if the determination value is less than the threshold. The load factor estimation device acquires detection information related to the distance to the cargo inside the cargo compartment via the depth sensor, and infers the load factor of the cargo configured inside the cargo compartment based on this detection information. The cargo compartment is not equipped with a door opening / closing sensor for detecting the opening / closing status of the door.

2. The vehicle as claimed in claim 1, wherein, The calculation unit identifies the determination region that is locally defined in the color image, and calculates the determination value based on the color information of the plurality of pixels contained in the determination region.

3. The vehicle as claimed in claim 1, wherein, The determination value is a statistical measure based on the RGB values ​​that serve as color information.

4. The vehicle as claimed in claim 1, wherein, The cargo load factor estimation device estimates the cargo load factor when it determines that the door is in a closed state.

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