Vehicle driving control method and device and storage medium

Abstract

The invention discloses a vehicle driving control method and device and a storage medium, and belongs to the technical field of vehicle control. The method comprises: collecting a video image of a road in front of a vehicle; extracting edge information of the preceding vehicle from the video image, wherein the edge information of the preceding vehicle includes a bottom edge line of the preceding vehicle; calculating a vertical run-out value of the bottom edge line of the front vehicle; determining the pothole degree of the road in front of the vehicle based on the vertical run-out value, wherein the pothole degree comprises no pothole, light pothole and heavy pothole; and adjusting vertical control of the vehicle in response to the fact that the potholes are light potholes or heavy potholes. According to the method, preparation for coping with the front bumpy road surface is made in advance, prediction and adaptation of the vehicle to the bumpy degree of the road surface are achieved, and under the condition that cost and energy consumption are controlled, the safety and stability of the vehicle running under the bumpy road surface are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of vehicle control technology, and in particular, to a method, device and storage medium for controlling vehicle travel. Background Art

[0002] During vehicle driving, it is necessary to predict the road surface condition in front of the vehicle, adjust the vertical control of the vehicle according to the pothole degree and the existence of obstacles in the road ahead, and help the vehicle to drive smoothly. In the related technology, the detection of the pothole degree of the road ahead of the vehicle through laser radar and sensors has problems such as high cost and high energy consumption. In addition, when the distance to the vehicle in front is close, the use of laser radar is limited and road surface information cannot be collected. Therefore, how to predict the road surface condition in front of the vehicle under various working conditions while controlling cost and energy consumption, ensure the smooth driving of the vehicle, and thus ensure the safety and stability of the vehicle, is a problem that needs to be solved. Summary of the invention

[0003] The embodiment of the present application provides a vehicle driving control method, device and storage medium, which can be used to ensure the smooth driving of the vehicle while controlling the cost and energy consumption. The technical solution is as follows:

[0004] On the one hand, an embodiment of the present application provides a method for controlling vehicle travel, the method comprising:

[0005] Collecting video images of the road ahead of the vehicle;

[0006] Extracting edge information of the front vehicle from the video image, wherein the edge information of the front vehicle includes a bottom edge line of the front vehicle;

[0007] Calculating the vertical runout of the bottom edge line of the front vehicle;

[0008] Determining a pothole degree of the road ahead of the vehicle based on the vertical runout, the pothole degree including no potholes, slight potholes, and severe potholes;

[0009] In response to the pothole degree being the light pothole or the heavy pothole, vertical control of the vehicle is adjusted.

[0010] In another aspect, a vehicle driving control device is provided, the device comprising:

[0011] A collection module, used to collect video images of the road ahead of the vehicle;

[0012] An extraction module, used to extract edge information of the front vehicle from the video image, wherein the edge information of the front vehicle includes a bottom edge line of the front vehicle;

[0013] A calculation module, used for calculating the vertical runout of the bottom edge line of the front vehicle;

[0014] A determination module, configured to determine a pothole degree of the road ahead of the vehicle based on the vertical runout, wherein the pothole degree includes no pothole, slight pothole, and severe pothole;

[0015] The adjustment module is used for adjusting the vertical control of the vehicle in response to the pothole degree being the mild pothole or the severe pothole.

[0016] On the other hand, a non-temporary computer-readable storage medium is also provided, characterized in that a computer program is stored in the computer-readable storage medium, and the computer program is loaded and executed by a processor to implement any of the above-mentioned vehicle driving control methods.

[0017] On the other hand, a computer program product is also provided, the computer program product comprising computer instructions, and the computer instructions, when executed by a processor, implement the steps of any of the above-mentioned vehicle driving control methods.

[0018] The technical solution provided by this application brings at least the following beneficial effects:

[0019] The present application collects video images of the road ahead of a vehicle and extracts edge information of the vehicle ahead from the video images, wherein the edge information of the vehicle ahead includes the bottom edge line of the vehicle ahead, so as to predict the pothole degree of the road where the vehicle is located according to the bottom edge line of the vehicle ahead; then calculates the vertical run-out amount of the bottom edge line of the vehicle ahead, and determines the pothole degree of the road ahead of the vehicle based on the vertical run-out amount, thereby realizing fast and convenient detection of the pothole degree of the road ahead; in response to whether the pothole degree is mild or severe, adjusts the vertical control of the vehicle, prepares in advance to deal with the pothole-prone road ahead, realizes the prediction and adaptation of the vehicle to the pothole degree of the road, and improves the safety and stability of the vehicle driving on pothole-prone roads under various working conditions while controlling costs and energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 It is a schematic diagram of an implementation environment provided by an embodiment of the present application;

[0022] Figure 2is a flow chart of a vehicle driving control method provided by an embodiment of the present application;

[0023] Figure 3 It is a control logic flow chart of vehicle driving provided by an embodiment of the present application;

[0024] Figure 4 It is a structural schematic diagram of a vehicle driving control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0026] The present application provides a method for controlling vehicle travel. Figure 1 , which shows a schematic diagram of the implementation environment of the method provided in the embodiment of the present application. The implementation environment may include: DCU (Domain Control Unit, autonomous driving domain controller) 11, a vehicle camera 12, a large screen 13 on the center console and a suspension controller 14.

[0027] In one possible implementation, DCU11 collects a video image of the road ahead of the vehicle through the on-board camera 12, grayscales the video image through OpenCV (open source computer vision library) to convert the video image from a color format to a grayscale format, and then smoothes the grayscale image through a Gaussian filter to obtain a noise-reduced grayscale image.

[0028] Exemplarily, after obtaining the grayscale image after noise reduction, DCU11 calculates the gradient strength and gradient direction of each pixel in the grayscale image after noise reduction by using the Sobel operator to obtain a gradient image corresponding to the grayscale image after noise reduction. DCU11 performs non-maximum suppression on the gradient image to determine edge pixels. DCU11 performs double threshold processing on edge pixels to distinguish strong edge pixels from weak edge pixels. DCU11 removes weak edge pixels where there are no strong edge pixels in the neighborhood, and then connects the strong edge pixels and the retained weak edge pixels to obtain the bottom edge line of the vehicle in front.

[0029] Optionally, a preset line is pre-calibrated in the lens of the vehicle-mounted camera 12, and a spatial coordinate system is established in the camera image. The DCU 11 calculates the instantaneous angle and vertical distance between the bottom edge line of the front vehicle and the preset line in the vertical direction in real time, and then determines the vertical runout of the bottom edge line of the front vehicle based on the instantaneous angle and vertical distance. The DCU 11 can also calculate the height change of the edge line in real time, and determine the vertical runout of the bottom edge line of the front vehicle based on the height change of the edge line.

[0030] In a possible implementation, DCU11 continuously monitors the vertical runout of the bottom edge line of the vehicle. If the number of times the vertical runout is greater than the first height threshold does not exceed a certain number within a certain period of time, DCU11 determines that the pothole degree of the road ahead of the vehicle is non-pothole-free; if the number of times the vertical runout is greater than the first height threshold and less than or equal to the second height threshold exceeds a certain number of times, DCU11 determines that the road ahead has a slight pothole; if the number of times the vertical runout is greater than the second height threshold exceeds a certain number of times, DCU11 determines that the road ahead has a severe pothole. After determining the pothole degree, DCU11 displays the pothole degree of the road ahead of the vehicle through the large screen 13 of the vehicle's center console.

[0031] For example, after determining the pothole degree of the road ahead of the vehicle, if the pothole degree is mild, the suspension controller 14 of the vehicle increases the stiffness of the suspension system, appropriately increases the low-frequency damping, and maintains appropriate high-frequency damping; if the pothole degree is severe, the suspension controller 14 of the vehicle reduces the stiffness of the suspension system, appropriately increases the low-frequency damping, and reduces the high-frequency damping. Among them, the DCU 11, the vehicle-mounted camera 12, the large screen 13 of the center console and the suspension controller 14 establish a communication connection through a wired or wireless network.

[0032] Based on the above Figure 1 In the implementation environment shown, the present application embodiment provides a method for controlling vehicle driving. Figure 2 As shown, taking the method applied to DCU as an example, the method includes steps 201 to 205.

[0033] In step 201, the DCU collects a video image of the road ahead of the vehicle.

[0034] In a possible implementation, the DCU collects a video image of the road ahead of the vehicle through a vehicle-mounted camera, wherein the vehicle-mounted camera is mounted on the front windshield of the vehicle and shoots the road ahead of the vehicle in real time. Optionally, after acquiring the captured video image, the video image is grayed to obtain a gray image corresponding to the video image; and the gray image is smoothed to obtain a gray image after noise reduction.

[0035] Optionally, grayscale processing is performed on the video image, including: DCU grayscale processing is performed on the video image through OpenCV, so that the video image is converted from a color format to a grayscale format. After obtaining the grayscale image corresponding to the video image, smoothing the grayscale image is performed, including: DCU smoothing the grayscale image through a Gaussian filter to obtain a grayscale image after noise reduction.

[0036] In step 202 , the DCU extracts edge information of the front vehicle from the video image, where the edge information of the front vehicle includes a bottom edge line of the front vehicle.

[0037] In a possible implementation, the edge information of the vehicle ahead includes the bottom edge line of the vehicle ahead. After acquiring the grayscale image after noise reduction, the DCU extracts the edge information of the vehicle ahead from the video image, including: detecting the gradient intensity of each pixel in the grayscale image after noise reduction to obtain a gradient image corresponding to the grayscale image after noise reduction, the gradient image including the gradient intensity of each pixel; performing non-maximum suppression on the gradient image to determine the edge pixels of the gradient image; performing double threshold processing on the edge pixels to determine strong edge pixels and weak edge pixels; and determining the edge information of the vehicle ahead based on the weak edge pixels and the strong edge pixels.

[0038] Exemplarily, the gradient intensity and gradient direction of each pixel in the denoised grayscale image are calculated by the Sobel operator to obtain a gradient image corresponding to the denoised grayscale image, wherein the gradient image includes the gradient intensity and gradient direction of each pixel.

[0039] Optionally, after the gradient image is acquired, non-maximum suppression is performed on the gradient image, including: if the gradient intensity of the current pixel is greater than the intensity of the two neighboring pixels in the gradient direction, the intensity of the pixel is retained; if the gradient intensity of the current pixel is less than or equal to the intensity of at least one of the two neighboring pixels in the gradient direction, the intensity of the pixel is set to 0. Each pixel is processed in the same way to determine the edge pixel.

[0040] In a possible implementation, after edge pixels are determined, dual threshold processing is performed on the edge pixels, including: in response to the gradient strength of the edge pixels being greater than or equal to a first threshold, the edge pixels are treated as strong edge pixels; in response to the gradient strength of the edge pixels being less than the first threshold and greater than or equal to a second threshold, the edge pixels are treated as weak edge pixels, wherein the first threshold is greater than the second threshold.

[0041] Optionally, after determining the edge pixel, the DCU compares the gradient strength of each edge pixel with the first threshold and the second threshold. If the gradient strength of the edge pixel is greater than or equal to the first threshold, the edge pixel is regarded as a strong edge pixel; if the gradient strength of the edge pixel is less than the first threshold and greater than or equal to the second threshold, the edge pixel is regarded as a weak edge pixel. Exemplarily, the first threshold and the second threshold can be set based on experience, and it is required to satisfy that the first threshold is greater than the second threshold.

[0042] In a possible implementation, after determining the strong edge pixels and the weak edge pixels, if there is a strong edge pixel in the neighborhood of any weak edge pixel, the weak edge pixel is retained; if there is no strong edge pixel in the neighborhood of any weak edge pixel, the weak edge pixel is removed. The strong edge pixel and the retained weak edge pixel are connected to obtain the bottom edge line of the front vehicle.

[0043] In step 203, the DCU calculates the vertical runout of the bottom edge line of the leading vehicle.

[0044] Exemplarily, after determining the edge information of the vehicle in front, the DCU calculates the vertical jump of the bottom edge line of the vehicle in front based on the edge information, including: calculating the vertical jump of the bottom edge line of the vehicle in front based on the instantaneous angle and vertical distance between the edge line and a preset line of the road surface where the vehicle is located, the preset line is located at a fixed position in the picture of the video image; and / or calculating the vertical jump of the bottom edge line of the vehicle in front based on the height change of the edge line.

[0045] In a possible implementation, a preset line is pre-calibrated in the lens of the vehicle-mounted camera, that is, the preset line is located at a fixed position in the screen of the video image. The DCU obtains the instantaneous angle and vertical distance between the edge line and the preset line of the road surface where the vehicle is located in real time, including: establishing a spatial coordinate system in the screen of the camera, and calculating the instantaneous angle and vertical distance between the bottom edge line of the front vehicle and the preset line in the vertical direction in real time. Based on the instantaneous angle and vertical distance, the vertical runout of the bottom edge line of the front vehicle is determined according to the corresponding relationship between the instantaneous angle and vertical distance and the vertical runout.

[0046] Optionally, in another possible implementation, the height variation of the edge line can also be calculated in real time, and based on the height variation of the edge line, the vertical runout of the bottom edge line of the front vehicle can be determined according to the corresponding relationship between the height variation of the edge line and the vertical runout. Exemplarily, the corresponding relationship between the instantaneous angle and the vertical distance and the vertical runout, and the corresponding relationship between the height variation of the edge line and the vertical runout can be determined based on experiments.

[0047] In step 204 , the DCU determines the pothole degree of the road ahead of the vehicle based on the vertical runout amount, where the pothole degree includes no potholes, slight potholes, and severe potholes.

[0048] In a possible implementation, the pothole degree includes no potholes, slight potholes, and severe potholes. The vertical jump amount of the bottom edge line of the vehicle is continuously monitored. If the number of times the vertical jump amount is greater than the first height threshold does not exceed a certain number within a certain period of time, it is determined that the pothole degree of the road ahead of the vehicle is no potholes; if the number of times the vertical jump amount is greater than the first height threshold and less than or equal to the second height threshold exceeds a certain number of times, it is determined that the road ahead has slight potholes; if the number of times the vertical jump amount is greater than the second height threshold exceeds a certain number of times, it is determined that the road ahead has severe potholes.

[0049] For example, after determining the pothole degree, the pothole degree of the road ahead of the vehicle is displayed on the large screen of the vehicle center console, so as to prompt the driver to prepare in advance. Optionally, the first height threshold, the certain number of times, the first height threshold and the second height threshold can be set according to experience, and it is required to satisfy that the second height threshold is greater than the first height threshold.

[0050] In step 205 , in response to the pothole level being a mild pothole or a severe pothole, the DCU adjusts the vertical control of the vehicle.

[0051] Optionally, after determining the degree of potholes on the road ahead of the vehicle, in response to whether the potholes are mild or severe, the DCU adjusts the vertical control of the vehicle, including: if the potholes are mild, increasing the stiffness of the suspension system through the vehicle's suspension controller, appropriately increasing the low-frequency damping, and keeping the high-frequency damping unchanged; if the potholes are severe, reducing the stiffness of the suspension system through the vehicle's suspension controller, appropriately increasing the low-frequency damping, and reducing the high-frequency damping.

[0052] For example, when the potholes are mild, the stiffness of the suspension system is increased to improve the responsiveness of the suspension, so that the vehicle can better maintain contact with the road, reduce body sway, improve handling and stability, and enable the vehicle to better cope with slight road surface changes. By increasing low-frequency damping, the movement of the vehicle chassis is controlled so that the vehicle can remain stable in the face of slight bumps. And by maintaining appropriate high-frequency damping, the vibration caused by mild potholes is absorbed to improve ride comfort.

[0053] In one possible implementation, when the potholes are severe, the suspension system can be made more flexible by reducing its stiffness, thereby more effectively absorbing impacts and preventing the chassis of the vehicle from being subjected to excessive impacts, which helps to reduce the vibration of the occupants and improve ride comfort. By reducing high-frequency damping, the suspension system can move more flexibly, thus better adapting to severely potholed roads and effectively avoiding the pitch and roll of the vehicle.

[0054] Optionally, the degree of potholes on the road ahead of the vehicle is continuously monitored. If it is recognized that the vehicle has driven off a section with mild potholes to a flat section, the stiffness of the suspension system is appropriately reduced through the suspension controller, the low-frequency damping is appropriately reduced, and the high-frequency damping remains unchanged; if it is recognized that the vehicle has driven off a section with severe potholes to a flat section, the stiffness of the suspension system is increased to the level before the reduction, the high-frequency damping is increased to the level before the reduction, and the low-frequency damping is reduced to the level before the increase through the suspension controller.

[0055] In summary, Figure 3A control logic flow chart of vehicle driving is provided as an example for illustration. Among them, the execution subject can be DCU. Step 301, the vehicle-mounted camera collects the video image of the road in front of the vehicle. Step 302, extract the edge information of the vehicle in front. Step 303, calculate the vertical jump of the bottom edge line of the vehicle in front. Step 304, determine the degree of potholes in the road in front of the vehicle. Step 305, determine whether the degree of potholes is mild or severe. If the degree of potholes is mild or severe, go to step 306; if the degree of potholes is neither mild nor severe, go to step 307. Step 306, adjust the stiffness and damping of the suspension system through the vehicle's suspension controller. Step 307, determine whether there are no potholes on the road in front of the vehicle.

[0056] The embodiment of the present application collects a video image of the road ahead of a vehicle and extracts edge information of the vehicle ahead from the video image, wherein the edge information of the vehicle ahead includes a bottom edge line of the vehicle ahead, so as to predict the pothole degree of the road where the vehicle is located according to the bottom edge line of the vehicle ahead; then calculates the vertical runout of the bottom edge line of the vehicle ahead, and determines the pothole degree of the road ahead of the vehicle based on the vertical runout, thereby realizing fast and convenient detection of the pothole degree of the road ahead; in response to whether the pothole degree is mild or severe, adjusts the vertical control of the vehicle, prepares in advance for dealing with the pothole road ahead, realizes the prediction and adaptation of the vehicle to the pothole degree of the road, and improves the safety and stability of the car driving on pothole road conditions under various working conditions while controlling cost and energy consumption.

[0057] See also Figure 4 , an embodiment of the present application provides a vehicle driving control device, the device comprising:

[0058] The acquisition module 401 is used to acquire a video image of the road ahead of the vehicle;

[0059] An extraction module 402 is used to extract edge information of the front vehicle from the video image, where the edge information of the front vehicle includes a bottom edge line of the front vehicle;

[0060] A calculation module 403 is used to calculate the vertical runout of the bottom edge line of the front vehicle;

[0061] A determination module 404 is used to determine the pothole degree of the road ahead of the vehicle based on the vertical runout, where the pothole degree includes no pothole, slight pothole and severe pothole;

[0062] The adjustment module 405 is used to adjust the vertical control of the vehicle in response to the pothole degree being a slight pothole or a severe pothole.

[0063] In one possible implementation, the calculation module 403 is used to calculate the vertical jump based on the instantaneous angle and vertical distance between the edge line and a preset line of the road surface where the vehicle is located, and the preset line is located at a fixed position in the picture of the video image; and / or calculate the vertical jump based on the height change of the edge line.

[0064] In a possible implementation, the extraction module 402 is further configured to perform grayscale processing on the video image to obtain a grayscale image corresponding to the video image; and perform smoothing processing on the grayscale image to obtain a grayscale image after noise reduction.

[0065] In one possible implementation, the extraction module 402 is used to detect the gradient intensity of each pixel in the grayscale image after denoising, obtain a gradient image corresponding to the grayscale image after denoising, and the gradient image includes the gradient intensity of each pixel; perform non-maximum suppression on the gradient image to determine the edge pixels of the gradient image; perform double threshold processing on the edge pixels to determine strong edge pixels and weak edge pixels; and determine the edge information of the vehicle ahead based on the weak edge pixels and the strong edge pixels.

[0066] In one possible implementation, the extraction module 402 is used to, in response to the gradient strength of the edge pixel being greater than or equal to a first threshold, treat the edge pixel as a strong edge pixel; in response to the gradient strength of the edge pixel being less than the first threshold and greater than or equal to a second threshold, treat the edge pixel as a weak edge pixel, and the first threshold is greater than the second threshold.

[0067] In a possible implementation, the adjustment module 405 is configured to adjust the stiffness and damping characteristics of the suspension system of the vehicle in response to whether the pothole degree is a mild pothole or a severe pothole.

[0068] The device collects video images of the road ahead of the vehicle and extracts edge information of the vehicle ahead from the video images, wherein the edge information of the vehicle ahead includes the bottom edge line of the vehicle ahead, so as to predict the pothole degree of the road where the vehicle is located according to the bottom edge line of the vehicle ahead; then calculates the vertical run-out amount of the bottom edge line of the vehicle ahead, and determines the pothole degree of the road ahead of the vehicle based on the vertical run-out amount, so as to realize fast and convenient detection of the pothole degree of the road ahead; in response to whether the pothole degree is slight pothole or severe pothole, adjusts the vertical control of the vehicle, prepares in advance for coping with the pothole road ahead, realizes the prediction and adaptation of the vehicle to the pothole degree of the road, and improves the safety and stability of the vehicle driving on pothole road conditions under various working conditions while controlling costs and energy consumption.

[0069] It should be noted that the device provided in the above embodiment only uses the division of the above functional modules as an example to implement its functions. In actual applications, the above functions can be assigned 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 and method embodiments provided in the above embodiment belong to the same concept, and their specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0070] In an exemplary embodiment, a computer-readable storage medium is also provided, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor of a computer device so that the computer implements any of the above-mentioned vehicle driving control methods.

[0071] In one possible implementation, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0072] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program includes computer instructions, 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 any of the above-mentioned vehicle driving control methods.

[0073] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions. For example, the video image of the road ahead of the vehicle, the vertical runout of the bottom edge line of the vehicle, and the pothole degree of the road ahead of the vehicle involved in this application are all obtained with full authorization.

[0074] It should be understood that the "plurality" mentioned in this article refers to two or more. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0075] It should be noted that the terms "first", "second", etc. (if any) in the specification and claims of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0076] The above description is only an exemplary embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for controlling vehicle travel, characterized in that: The method comprises: Collecting video images of the road ahead of the vehicle; Extracting edge information of the front vehicle from the video image, wherein the edge information of the front vehicle includes a bottom edge line of the front vehicle; Calculating the vertical runout of the bottom edge line of the front vehicle; Determining a pothole degree of the road ahead of the vehicle based on the vertical runout, the pothole degree including no potholes, slight potholes, and severe potholes; In response to the pothole degree being the light pothole or the heavy pothole, vertical control of the vehicle is adjusted.

2. The method according to claim 1, characterized in that The calculating the vertical runout of the bottom edge line of the front vehicle comprises: Calculating the vertical runout based on an instantaneous angle and a vertical distance between the edge line and a preset line on the road surface where the vehicle is located, the preset line being located at a fixed position in the frame of the video image; and / or The vertical runout is calculated based on the height change of the edge line.

3. The method according to claim 1, characterized in that Before extracting edge information of the front vehicle from the video image, the method further includes: Performing grayscale processing on the video image to obtain a grayscale image corresponding to the video image; The grayscale image is smoothed to obtain a grayscale image after noise reduction.

4. The method according to claim 3, characterized in that The step of extracting edge information of the front vehicle from the video image comprises: Detecting the gradient intensity of each pixel in the grayscale image after denoising to obtain a gradient image corresponding to the grayscale image after denoising, wherein the gradient image includes the gradient intensity of each pixel; Performing non-maximum suppression on the gradient image to determine edge pixels of the gradient image; Performing double threshold processing on the edge pixels to determine strong edge pixels and weak edge pixels; The edge information of the leading vehicle is determined based on the weak edge pixels and the strong edge pixels.

5. The method according to claim 4, characterized in that The performing double threshold processing on the edge pixels to determine strong edge pixels and weak edge pixels includes: In response to the gradient strength of the edge pixel being greater than or equal to a first threshold, treating the edge pixel as a strong edge pixel; In response to the gradient strength of the edge pixel being less than the first threshold and greater than or equal to a second threshold, the edge pixel is regarded as a weak edge pixel, and the first threshold is greater than the second threshold.

6. The method according to claim 1, characterized in that In response to the pothole degree being the mild pothole or the severe pothole, adjusting the vertical control of the vehicle includes: In response to the pothole degree being the light pothole or the heavy pothole, the stiffness and damping characteristics of the suspension system of the vehicle are adjusted.

7. A vehicle driving control device, characterized in that: The device comprises: A collection module, used to collect video images of the road ahead of the vehicle; An extraction module, used to extract edge information of the front vehicle from the video image, wherein the edge information of the front vehicle includes a bottom edge line of the front vehicle; A calculation module, used for calculating the vertical runout of the bottom edge line of the front vehicle; A determination module, configured to determine a pothole degree of the road ahead of the vehicle based on the vertical runout, wherein the pothole degree includes no pothole, slight pothole, and severe pothole; The adjustment module is used for adjusting the vertical control of the vehicle in response to the pothole degree being the mild pothole or the severe pothole.

8. The device according to claim 7, characterized in that The calculation module is used to calculate the vertical jump amount based on the instantaneous angle and vertical distance between the edge line and a preset line on the road surface where the vehicle is located, wherein the preset line is located at a fixed position in the picture of the video image; and / or calculate the vertical jump amount based on the height change of the edge line.

9. A computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the vehicle driving control method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which is loaded and executed by a processor to implement the vehicle driving control method as described in any one of claims 1 to 6.

Images