Detection Method, System, Device, Medium and Vehicle for Static State of Road Mining Vehicle

Abstract

The present invention relates to the field of intelligent driving technologies, and particularly to a method, system, device, medium and vehicle for detecting the stationary state of a road sampling vehicle. The detection method includes: S1, obtaining images frame by frame from the video stream captured by an in-vehicle camera; S2, using a CNN network model to identify at least one static target from the frame-by-frame images and extracting the detection box of each static target; S3, adopting the Hungarian algorithm to match the detection boxes of two adjacent frames of images, obtaining the detection boxes associated with the same static target in two adjacent frames of images, and judging whether the road sampling vehicle is in a stationary state according to the scale transformation relationship between the detection boxes associated with the same static target. The detection method for the stationary state of the road sampling vehicle in the present invention has high accuracy, low implementation cost, is easy to implement, and is convenient for subsequent effective data deduplication processing.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent driving, and particularly to a method, system, device, medium and vehicle for detecting the stationary state of a road sampling vehicle. Background Art

[0002] With the continuous development of technologies such as autonomous driving and intelligent transportation, more and more vehicles are equipped with an Advanced Driver Assistance System (ADAS). The fundamental of intelligent driving lies in safety. How to ensure the safety of a vehicle during driving is still the research focus of intelligent driving technology. In an intelligent driving assistance system, when the vehicle is in a stationary state, the collected data will contain duplicate data or data with high similarity. Since sending duplicate or highly similar data (such as image information) into a neural network model for training will occupy network training resources and cause interference to the training of the neural network model, affecting the training effect of the model. Therefore, removing duplicate or highly similar data during the data screening process, that is, sending appropriate and cleaned data into the neural network model before training can effectively improve the detection ability of the model and reduce the probability of the model having an overfitting problem.

[0003] To reduce data duplication, data screening or processing will be performed. During the data screening process, if the pictures collected when the road sampling vehicle is stationary need to be removed, it is necessary to judge whether the road sampling vehicle is in a stationary or moving state, and then screen out the road sampling data collected by the road sampling vehicle in the stationary state. In the prior art, to judge whether a road sampling vehicle is in a stationary state, usually the obtained video data is frame-processed, and the images at adjacent moments in the video sequence are differentially processed to identify moving vehicles. That is, the frame difference method is used to calculate the difference between two adjacent frames of images, and the moving area in the image is extracted through thresholding processing, and then it is judged whether the current road sampling vehicle is in a stationary state. Since there are differences between adjacent frames of images, it is impossible to accurately determine the threshold, and the accuracy of the detection result of the stationary state of the road sampling vehicle is low, so that during data screening, the data cannot be effectively de-duplicated. In addition, an ultrasonic sensor device is added to the vehicle to detect whether the road sampling vehicle is in a stationary state. The accuracy of the detection result is related to the selected ultrasonic sensor device. For an ultrasonic sensor with high precision, the detection accuracy is good, but the cost of system implementation is high. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: to solve the technical problem of low accuracy of the detection result of the stationary state of a road sampling vehicle in the prior art, the present invention provides a method for detecting the stationary state of a road sampling vehicle, which has high detection result accuracy, low implementation cost, is easy to implement, and is convenient for subsequent effective data de-duplication processing.

[0005] The technical solution adopted by the present invention to solve its technical problem is: a method for detecting the stationary state of a road sampling vehicle, specifically including the following steps:

[0006] S1, obtain images frame by frame from the video stream captured by the vehicle-mounted camera;

[0007] S2, use the CNN network model to identify at least one static target from the frame-by-frame images, and extract the detection box of each said static target;

[0008] S3, adopt the Hungarian algorithm to match the detection boxes of two adjacent frames of images, obtain the detection boxes associated with the same said static target in two adjacent frames of images, and judge whether the road-sampling vehicle is in a stationary state according to the scale transformation relationship between the detection boxes associated with the same said static target.

[0009] Further, specifically, in the step S2, the Kalman filtering algorithm is also used to optimize each said detection box.

[0010] Further, specifically, the step S3 specifically includes the following steps:

[0011] S31, establish an image coordinate system;

[0012] S32, set the detection box of the static target in the t-th frame of image as the reference box, and the detection box of the static target in the (t + 1)-th frame of image as the predicted reference box;

[0013] Calculate the intersection area A1 and the union area A2 between the reference box and the predicted reference box, and calculate the intersection over union between two adjacent frames of images according to the intersection area and the union area. The calculation formula is:

[0014] Calculate the negative value -IOU of the intersection over union;

[0015] S33, use the negative value of the intersection over union as the matching condition of the Hungarian algorithm to obtain the matching relationship between the t-th frame of image and the (t + 1)-th frame of image, obtain the detection boxes associated with the same said static target in the t-th frame of image and the (t + 1)-th frame of image according to the matching relationship, read and process the height and width of the detection box, and judge whether the road-sampling vehicle is in a stationary state.

[0016] Further, specifically, in the step S33, judging whether the road-sampling vehicle is in a stationary state specifically includes the following steps: calculate the difference between the height and width of the detection boxes of the same said static target in the t-th frame of image and the (t + 1)-th frame of image;

[0017] If the result of the difference is within the threshold range, judge that the road-sampling vehicle is in a stationary state;

[0018] On the contrary, if the result of the difference is not within the threshold range, judge that the road-sampling vehicle is in a moving state.

[0019] Further, specifically, in the step S3, it further includes: S34, obtaining all the same static targets and the detection frames associated with the static targets in the t-th frame image and the (t + 1)-th frame image, calculating the difference between the frame height and the frame width of the detection frames of all the same static targets, comparing the result of the difference with the threshold, and then using a voting mechanism to determine whether the road sampling vehicle is in a stationary state.

[0020] Further, specifically, the static targets are trees, traffic lights or traffic signs.

[0021] A detection system adopting the above-mentioned detection method for the stationary state of a road sampling vehicle, including:

[0022] An acquisition module: obtaining images frame by frame from the video stream captured by an in-vehicle camera;

[0023] An extraction module: using a CNN network model to identify at least one static target from the frame-by-frame images and extracting the detection frame of each static target;

[0024] A matching module: using the Hungarian algorithm to match the detection frames of two adjacent frame images, obtaining the detection frames associated with the same static target in two adjacent frame images, and determining whether the road sampling vehicle is in a stationary state according to the scale transformation relationship between the detection frames associated with the same static target.

[0025] A computer device, including:

[0026] A processor;

[0027] A memory for storing executable instructions;

[0028] Wherein, the processor is used to read the executable instructions from the memory and execute the executable instructions to implement the above-mentioned detection method for the stationary state of a road sampling vehicle.

[0029] A computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to implement the above-mentioned detection method for the stationary state of a road sampling vehicle.

[0030] A vehicle, including the above-mentioned detection system for the stationary state of a road sampling vehicle.

[0031] The beneficial effects of the present invention are as follows. The detection method for the stationary state of the road mining vehicle of the present invention determines whether the road mining vehicle is in a stationary state by comparing whether the size of the same static target in the image coordinate system changes between two adjacent frames of images. The accuracy of the detection result is high, the implementation cost is low, and it is easy to implement, which is convenient for subsequent effective data deduplication processing. In addition, voting is performed on the size change of the same static target in two adjacent frames of images, which improves the robustness of the detection method and further improves the accuracy of the detection result. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention will be further described below with reference to the drawings and embodiments.

[0033] Figure 1 is a schematic flowchart of Embodiment 1 of the present invention.

[0034] Figure 2 is a schematic diagram of the front and rear frames of the stationary state and the moving state in Embodiment 1 of the present invention.

[0035] Figure 3 is a schematic diagram of the reference frame and the predicted reference frame in the coordinate system in Embodiment 1 of the present invention.

[0036] Figure 4 is a schematic diagram of the intersection over union calculation in Embodiment 1 of the present invention.

[0037] Figure 5 is a schematic diagram of the detection frame matching in Embodiment 1 of the present invention.

[0038] Figure 6 is a schematic structural diagram of Embodiment 2 of the present invention.

[0039] Figure 7 is a schematic hardware structure diagram of Embodiment 1 of the present invention.

[0040] In the figure, 20 is the detection system; 200 is the acquisition module; 201 is the extraction module; 202 is the matching module; 10 is the electronic device; 1002 is the processor; 1004 is the memory; 1006 is the transmission device. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The present invention will now be described in further detail with reference to the drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0042] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, features defined as "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0043] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0044] Embodiment 1

[0045] As Figure 1 shown, the embodiment of the present application provides a method for detecting the stationary state of a road collection vehicle, which specifically includes the following steps:

[0046] S1, obtaining images frame by frame from the video stream captured by an in-vehicle camera;

[0047] S2, using a CNN network model to identify at least one static target from the frame-by-frame images and extracting the detection box of each static target;

[0048] In step S2, the Kalman filtering algorithm is also used to optimize each detection box. The detection boxes extracted by the CNN network model will have a certain degree of dynamic changes. After the detection boxes are smoothed by the Kalman filtering algorithm, the processed detection boxes are more stable, which can further improve the accuracy of the detection results. It should be noted that the static targets are trees, traffic lights, or traffic signs, but are not limited thereto, and can also be other static objects on the traffic road. As Figure 2 shown, Figure 2 (a) are two adjacent frames of images in the stationary state, Figure 2(b) Images of two adjacent frames in the motion state. The dashed boxes in the figure represent static targets that can be used as references, and the solid boxes are the static targets detected by the CNN network model and the corresponding detection boxes.

[0049] S3. Use the Hungarian algorithm to match the detection boxes of two adjacent frames of images, obtain the detection boxes associated with the same static target in two adjacent frames of images, and determine whether the road sampling vehicle is in a stationary state according to the scale transformation relationship between the detection boxes associated with the same static target.

[0050] In this embodiment, step S3 specifically includes the following steps:

[0051] S31. Establish an image coordinate system;

[0052] S32. Set the detection box of the static target in the t-th frame of image as the reference box, represented by [x1, y1, x2, y2], and the detection box of the static target in the (t + 1)-th frame of image as the predicted reference box, represented by [x3, y3, x4, y4], as Figure 3 shown; calculate the intersection area A1 and the union area A2 between the reference box and the predicted reference box, and calculate the intersection over union (IOU) between two adjacent frames of images according to the intersection area and the union area. The calculation formula is: Furthermore, calculate the negative value of the intersection over union, -IOU.

[0053] As Figure 4 shown; when there are multiple static targets identified by the CNN network model, there will also be multiple corresponding detection boxes. Calculate the intersection over union between all detection boxes.

[0054] S33. Use the negative value of the intersection over union, -IOU, as the matching condition of the Hungarian algorithm to obtain the matching relationship between the t-th frame of image and the (t + 1)-th frame of image. Specifically, obtain the negative value of the intersection over union of the detection box of the t-th frame of image and the detection box of the (t + 1)-th frame of image, and form a matrix m represents the number of detection boxes in the t-th frame of image, n represents the number of detection boxes in the (t + 1)-th frame of image, Amn is the value of the negative value of the intersection over union, -IOU, between any detection box in the t-th frame of image and any detection box in the (t + 1)-th frame of image. Input the obtained matrix into the Hungarian algorithm for calculation, and extract the minimum value of all result sums based on the calculation result. This minimum value is the output value of the Hungarian algorithm, and the output value is the matching relationship between the t-th frame of image and the (t + 1)-th frame of image. Obtain the detection boxes associated with the same static target in the t-th frame of image and the (t + 1)-th frame of image according to the matching relationship, read and process the height and width of the detection boxes, and determine whether the road sampling vehicle is in a stationary state.

[0055] Further, determining whether the road sampling vehicle is in a stationary state specifically includes the following steps: calculating the difference between the height and width of the detection boxes of the same static target in the t-th frame image and the (t + 1)-th frame image; if the difference result is within the threshold range, it is determined that the road sampling vehicle is in a stationary state; conversely, if the difference result is not within the threshold range, it is determined that the road sampling vehicle is in a moving state. Specifically, as Figure 5 shown, the height of the detection box of the static target in the t-th frame image is H1 and the width is W1, the height of the detection box of the same static target in the (t + 1)-th frame image is H2 and the width is W2. Calculate the difference between the height H1 and the height H2, and the difference between the width W1 and the width W2. If the result is within the threshold range, it is determined that the height H2 = H1 and the width W2 = W1, and the road sampling vehicle is in a stationary state. To improve the accuracy of detection, the threshold range is between 0.01 and 0.05.

[0056] In this embodiment, step S3 further includes: S34, obtaining all the same static targets and the detection boxes associated with the static targets in the t-th frame image and the (t + 1)-th frame image, calculating the difference between the height and width of the detection boxes of all the same static targets, comparing the difference result with the threshold, and using a voting mechanism to determine whether the road sampling vehicle is in a stationary state, further improving the accuracy of the detection result.

[0057] The detection method for the stationary state of the road sampling vehicle of the present invention determines whether the road sampling vehicle is in a stationary state by comparing whether the size of the same static target in the image coordinate system changes between two adjacent frames of images. The accuracy of the detection result is high, the implementation cost is low, and it is easy to implement, facilitating subsequent effective data deduplication processing. In addition, voting on the size change of the same static target in two adjacent frames of images improves the robustness of the detection method and further improves the accuracy of the detection result.

[0058] Embodiment 2

[0059] This application embodiment provides a detection system that uses the above road sampling vehicle stationary state detection method. Figure 6 As shown in the structural schematic diagram of the detection system, it specifically includes:

[0060] Acquisition module 200: Obtaining images frame by frame from the video stream captured by the vehicle-mounted camera;

[0061] Extraction module 201: Using a CNN network model to identify at least one static target from the frame-by-frame images and extracting the detection box of each static target;

[0062] Matching module 202: Using the Hungarian algorithm to match the detection boxes of two adjacent frames of images, obtaining the detection boxes associated with the same static target in two adjacent frames of images, and determining whether the road sampling vehicle is in a stationary state according to the scale transformation relationship between the detection boxes associated with the same static target.

[0063] It should be noted that when the detection system provided in the above embodiments realizes its functions, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiments and the method embodiments belong to the same concept. For the specific implementation process, please refer to the method embodiments and will not be elaborated here.

[0064] Embodiment 3

[0065] An embodiment of the present application provides a computer device, which includes a processor and a memory. At least one instruction or at least one program segment is stored in the memory, and the at least one instruction or the at least one program segment is loaded and executed by the processor to implement a detection method for the stationary state of a road sampling vehicle as provided in the above method embodiment.

[0066] Figure 7 The figure shows a schematic hardware structure diagram of a device for implementing a detection method for the stationary state of a road sampling vehicle provided in an embodiment of the present application. The device can participate in forming or include the device or system provided in the embodiment of the present application. As Figure 7 shown, the computer device 10 may include one or more processors 1002 (the processor may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 1004 for storing data, and a transmission device 1006 for communication functions. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 7 the structure shown is only schematic and does not limit the structure of the above electronic device. For example, the computer device 10 may further include more or fewer components than Figure 7 shown, or have a different configuration from Figure 7 shown.

[0067] It should be noted that the above one or more processors and / or other data processing circuits are generally referred to as "data processing circuits" in this article. The data processing circuit may be embodied in whole or in part as software, hardware, firmware, or any combination thereof. In addition, the data processing circuit may be a single independent processing module, or be incorporated in whole or in part into any one of other elements in the computer device 10 (or mobile device). As involved in the embodiments of the present application, the data processing circuit is a processor control (such as the selection of a variable resistance terminal path connected to an interface).

[0068] The memory 1004 can be used to store software programs and modules of application software, such as the program instruction / data storage device corresponding to a detection method for the stationary state of a road sampling vehicle in an embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 1004, that is, to implement the above-mentioned method. The memory 1004 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 1004 may further include a memory remotely disposed relative to the processor, and these remote memories may be connected to the computer device 10 through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0069] The transmission device 1006 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by a communication provider of the computer device 10. In one instance, the transmission device 1006 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 1006 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0070] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables a user to interact with the user interface of the computer device 10 (or mobile device).

[0071] Embodiment 4

[0072] The embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium can be disposed in a server to store at least one instruction or at least one segment of a program related to a detection method for the stationary state of a road sampling vehicle in the method embodiment. The at least one instruction or the at least one segment of the program is loaded and executed by the processor to implement the detection method for the stationary state of a road sampling vehicle provided in the above method embodiment.

[0073] Optionally, in this embodiment, the above storage medium may be located in at least one of multiple network servers in a computer network. Optionally, in this embodiment, the above storage medium may include but is not limited to: USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs, and other media that can store program codes.

[0074] Example 5

[0075] An embodiment of the present invention also provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes a method for detecting the stationary state of a road sampling vehicle provided in the above various optional embodiments.

[0076] Example 6

[0077] An embodiment of the present invention also provides a vehicle, and the vehicle includes a device for detecting the stationary state of a road sampling vehicle for intelligent driving of the vehicle as described above.

[0078] It should be noted that: the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of the present application have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0080] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above embodiments can be completed by hardware, or can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium, and the above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, etc.

[0081] Inspired by the above ideal embodiments of the present invention, through the above description, relevant staff can completely make various changes and modifications without departing from the technical idea of this invention. The technical scope of this invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A detection method for the stationary state of a road mining vehicle, characterized in that, Specifically, it includes the following steps: S1. Obtain images frame by frame from the video stream captured by the vehicle-mounted camera; S2. Use the CNN network model to identify at least one static target from the frame-by-frame images, and extract the detection boxes of each static target; S3. Use the Hungarian algorithm to match the detection boxes of two adjacent frames of images, obtain the detection boxes associated with the same static target in two adjacent frames of images, and judge whether the road sampling vehicle is in a stationary state according to the scale transformation relationship between the detection boxes associated with the same static target; The step S3 specifically includes the following steps: S31. Establish an image coordinate system; S32. Set the detection box of the static target in the t-th frame of image as the reference box, and the detection box of the static target in the (t + 1)-th frame of image as the predicted reference box; Calculate the intersection area A1 and the union area A2 between the reference box and the predicted reference box, and calculate the intersection over union between two adjacent frames of images according to the intersection area and the union area. The calculation formula is as follows: ; Calculate the negative value of the intersection over union - IOU; S33. Use the negative value of the intersection over union - IOU as the matching condition of the Hungarian algorithm to obtain the matching relationship between the t-th frame of image and the (t + 1)-th frame of image, obtain the detection boxes associated with the same static target in the t-th frame of image and the (t + 1)-th frame of image according to the matching relationship, read and process the height and width of the detection boxes, and judge whether the road sampling vehicle is in a stationary state; Among them, judging whether the road sampling vehicle is in a stationary state specifically includes the following steps: Calculate the difference between the height and width of the detection boxes of the same static target in the t-th frame of image and the (t + 1)-th frame of image; If the difference result is within the threshold range, judge that the road sampling vehicle is in a stationary state; On the contrary, if the difference result is not within the threshold range, judge that the road sampling vehicle is in a moving state; S34. Obtain all the same static targets and the detection boxes associated with the static targets in the t-th frame of image and the (t + 1)-th frame of image, calculate the difference between the height and width of the detection boxes of all the same static targets, and use a voting mechanism to judge whether the road sampling vehicle is in a stationary state after comparing the difference result with the threshold.

2. The detection method for the stationary state of a road mining vehicle according to claim 1, characterized in that, In the step S2, the Kalman filter algorithm is also used to optimize each detection box.

3. The detection method for the stationary state of the road mining vehicle according to claim 1, characterized in that, The static target is a tree, a traffic signal or a traffic sign.

4. A detection system using the detection method for the stationary state of a road mining vehicle as described in any one of claims 1 to 3, characterized in that, It includes: Acquisition module (200): Obtain images frame by frame from the video stream captured by the vehicle-mounted camera; Extraction module (201): Use the CNN network model to identify at least one static target from the frame-by-frame images, and extract the detection boxes of each static target; Matching module (202): Use the Hungarian algorithm to match the detection boxes of two adjacent frames of images, obtain the detection boxes associated with the same static target in two adjacent frames of images, and judge whether the road sampling vehicle is in a stationary state according to the scale transformation relationship between the detection boxes associated with the same static target.

5. A computer device, characterized in that, It includes: Processor; Memory, used to store executable instructions; Among them, the processor is used to read the executable instructions from the memory and execute the executable instructions to implement the detection method for the stationary state of the road sampling vehicle as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to implement the method for detecting the stationary state of a road-harvesting vehicle according to any one of claims 1 to 3.

7. A vehicle, characterized in that, It includes the detection system for the stationary state of a road-harvesting vehicle according to claim 4.

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