Driving control strategy determination method, electronic equipment, vehicle, medium and product

Abstract

The invention discloses a method for determining a driving control strategy, electronic equipment, a vehicle, a medium and a product, and relates to the technical field of vehicles, the method for determining the driving control strategy is applied to the vehicle comprising an image acquisition device, and specifically comprises the following steps: shooting target image data comprising a road in front of the vehicle through the image acquisition device, determining a transmissivity parameter corresponding to each pixel point in the target image data; under the condition that the vehicle is detected to be in a low-visibility environment in combination with the transmissivity parameter, determining a low-visibility area and a drivable area contained in the target image data; and determining a target weather type corresponding to the vehicle in combination with the low-visibility region and the drivable region, and determining a matched target control strategy according to the target weather type. By adopting the method and the device, the electronic equipment can obtain the accurate weather type, so that the proper control strategy is screened, and the driving safety risk in the vehicle driving process is reduced.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method for determining a driving control strategy, an electronic device, a vehicle, a storage medium, and a computer program product. Background Art

[0002] With the continuous development of the automobile industry, vehicles have become the preferred means of transportation for more and more users in their daily travel.

[0003] In related technologies, a vehicle usually relies on its own configured image acquisition device to capture the surrounding environment to obtain image data, and then determines the weather type of the current environment based on the brightness changes of the image data.

[0004] However, when the ambient light around the vehicle changes significantly, the vehicle is likely to identify the wrong weather type after acquiring image data, resulting in a lag in safety control strategies and significantly increasing driving safety risks. Summary of the Invention

[0005] The main purpose of this application is to provide a method for determining a driving control strategy, an electronic device, a vehicle, a storage medium and a computer program product, aiming to solve the technical problem in related technologies that vehicles easily identify the wrong weather type.

[0006] To achieve the above objectives, the present application proposes a method for determining a driving control strategy, which is applied to a vehicle including an image acquisition device, and includes:

[0007] capturing target image data containing the road ahead of the vehicle through the image acquisition device, and determining the transmittance parameter corresponding to each pixel point in the target image data;

[0008] When it is detected that the vehicle is in a low-visibility environment in combination with the transmittance parameter, determining a low-visibility area and a drivable area included in the target image data;

[0009] The target weather type corresponding to the vehicle is determined in combination with the low visibility area and the drivable area, and a matching target control strategy is determined according to the target weather type.

[0010] In one embodiment, after the step of determining the transmittance parameter corresponding to each pixel point in the target image data, the method includes:

[0011] Obtaining a preset transmittance threshold, and comparing each of the transmittance parameters with the transmittance threshold to obtain a plurality of comparison results;

[0012] Determining a plurality of target pixel points according to the plurality of comparison results, wherein the target pixel points are pixel points whose transmittance parameters do not reach the transmittance threshold;

[0013] Determine the number of low-transmittance pixel points corresponding to the plurality of target pixel points, and when it is detected that the number of low-transmittance pixel points reaches a preset threshold, determine that the vehicle is in a low-visibility environment, and execute the step of determining the low-visibility area and the drivable area contained in the target image data.

[0014] In one embodiment, after the step of determining the transmittance parameter corresponding to each pixel point in the target image data, the method further includes:

[0015] Determining each cluster area in the target image data, and determining a transmission change parameter between adjacent cluster areas based on each of the transmittance parameters;

[0016] When it is detected that at least one transmission change parameter reaches a preset change parameter threshold, it is determined that the vehicle is in the low visibility environment, and the step of determining the low visibility area and the drivable area included in the target image data is performed.

[0017] In one embodiment, the step of determining the target weather type corresponding to the vehicle based on the low visibility area and the drivable area includes:

[0018] Determining the coordinates of each first pixel included in the low-visibility area, and determining the coordinates of each second pixel included in the drivable area;

[0019] traversing each of the first pixel coordinates and each of the second pixel coordinates to determine each overlapping pixel coordinate, and determining an overlapping area ratio according to each of the overlapping pixel coordinates;

[0020] When it is detected that the overlapping area ratio reaches a preset overlapping ratio threshold, determining that the target weather type corresponding to the vehicle is a heavy fog weather type;

[0021] When it is detected that the overlapping area ratio does not reach the overlapping ratio threshold, it is determined that the target weather type corresponding to the vehicle is a normal weather type.

[0022] In one embodiment, the vehicle further includes a vibration wave sensor. After the step of determining the transmittance parameter corresponding to each pixel point in the target image data, the method further includes:

[0023] When detecting that the vehicle is in a low visibility environment, acquiring an initial vibration wave signal on the vehicle through the vibration wave sensor;

[0024] Processing the initial vibration wave signal to obtain an average impact energy density and a single impact energy density;

[0025] When it is detected that the average impact energy density reaches a preset first energy density threshold, determining that the target weather type is a sandstorm weather type, and determining a matching target control strategy according to the sandstorm weather type;

[0026] When it is detected that the single impact energy density reaches a preset second energy density threshold, the target weather type is determined to be a hail weather type, and a matching target control strategy is determined based on the hail weather type, wherein the second energy density threshold is greater than the first energy density threshold.

[0027] In one embodiment, after the step of determining the transmittance parameter corresponding to each pixel point in the target image data, the method further includes:

[0028] In the case where it is detected that the vehicle is in a low visibility environment, detecting vehicle chassis parameters of the vehicle;

[0029] determining a driving state of the vehicle according to the vehicle chassis parameters, wherein the driving state is a slipping state or a normal state;

[0030] When it is detected that the driving state is the slipping state, it is determined that the target weather type corresponding to the vehicle is a freezing rain weather type, and a matching target control strategy is determined according to the freezing rain weather type.

[0031] In one embodiment, the vehicle chassis parameters include a plurality of wheel end torque parameters, and the step of determining the driving state of the vehicle based on the vehicle chassis parameters includes at least one of the following:

[0032] determining a first target torque parameter among the plurality of wheel end torque parameters, and determining that the driving state of the vehicle is the slipping state when it is detected that the first target torque parameter reaches a preset first torque threshold, wherein the first target torque parameter is the wheel end torque parameter with the largest value;

[0033] dividing the plurality of wheel-end torque parameters into a plurality of torque parameter groups, and determining that the driving state is the slipping state when detecting that at least one torque parameter group reaches a preset second torque threshold, wherein the second torque threshold is greater than the first torque threshold;

[0034] When a second target torque parameter is detected among the plurality of wheel end torque parameters, the driving state is determined to be the slip state, wherein a torque direction of the second target torque parameter is opposite to a torque direction of the other wheel end torque parameters.

[0035] In one embodiment, the vehicle chassis parameters further include a plurality of wheel speed parameters, and the step of determining the driving state of the vehicle based on the vehicle chassis parameters further includes:

[0036] Determining a target wheel speed parameter and calculating wheel speed differences corresponding to the target wheel speed parameter, wherein the target wheel speed parameter is the wheel speed parameter with the largest value;

[0037] Determining a target wheel speed difference and obtaining a preset first wheel speed difference threshold, wherein the target wheel speed difference is a wheel speed difference with a maximum value;

[0038] When it is detected that the target wheel speed difference reaches the first wheel speed difference threshold, the driving state is determined to be the slipping state.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for determining the driving control strategy as described above.

[0040] In addition, to achieve the above objectives, the present application also proposes a vehicle, which includes the electronic device as described above.

[0041] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the method for determining the driving control strategy as described above are implemented.

[0042] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the method for determining the driving control strategy as described above.

[0043] The method for determining a driving control strategy provided in an embodiment of the present application is applied to a vehicle including an image acquisition device, wherein the image acquisition device captures target image data including a road in front of the vehicle and determines the transmittance parameters corresponding to each pixel point in the target image data; when it is detected that the vehicle is in a low-visibility environment based on the transmittance parameters, the low-visibility area and the drivable area included in the target image data are determined; the target weather type corresponding to the vehicle is determined based on the low-visibility area and the drivable area, and a matching target control strategy is determined based on the target weather type.

[0044] In this embodiment, when the electronic device is running, it first controls the image acquisition device configured on the vehicle to capture target image data containing the road in front of the vehicle through the image acquisition device. The electronic device then processes each pixel in the target image data to determine the transmittance parameter corresponding to each pixel. Afterwards, when the electronic device identifies that the vehicle is in a low-visibility environment based on each transmittance parameter, it extracts the image features of the target image data and obtains the drivable area contained in the target image data based on the image features. At the same time, the electronic device identifies the low-visibility area contained in the target image data in combination with each transmittance parameter. Finally, the electronic device determines the target weather type corresponding to the vehicle's environment in combination with the drivable area and the low-visibility area, and determines the target control strategy to be adopted based on the target weather type.

[0045] In this way, the present application solves the technical problem in the related art that vehicles easily identify the wrong weather type. That is, the present application calculates the transmittance parameters of each pixel point in the image data, so that the electronic device can fully consider the physical attenuation characteristics of fog to light, avoid determining the target weather type based on the overall brightness of the image data, and thus enable the electronic device to obtain the accurate weather type, thereby screening the appropriate control strategy to reduce the driving safety risk during vehicle driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0048] Figure 1 A flowchart of a method for determining a driving control strategy according to a first embodiment of the present invention is provided;

[0049] Figure 2 The target image data involved in the first embodiment of the method for determining the driving control strategy of the present application;

[0050] Figure 3 A schematic diagram of a simplified flow chart of a method for determining a driving control strategy for this application;

[0051] Figure 4 This is a schematic diagram of the module structure of a device for determining a driving control strategy according to an embodiment of the present application;

[0052] Figure 5A device structure schematic diagram of a hardware running environment involved in a determination method of a driving control strategy in an embodiment of the present application.

[0053] The object implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are merely intended to explain the technical solutions of the present application, and are not intended to limit the present application.

[0055] In order to better understand the technical solutions of the present application, the specific embodiments will be described in detail below with reference to the drawings and the accompanying drawings.

[0056] In the present embodiment, for the convenience of description, the following electronic devices configured in the vehicle and internally configured with an image processing module, or mobile terminals, data storage control terminals, PC terminals and the like connected to the electronic control unit matched with the electronic devices are taken as the execution subject for elaboration.

[0057] Based on the above electronic device, the overall concept of the determination method of the driving control strategy of the present application is proposed.

[0058] With the continuous development of the automobile industry, vehicles have become the preferred means of transportation for more and more users in daily travel. In the related art, the vehicle usually relies on the image acquisition device configured therein to capture the surrounding environment to obtain image data, so as to determine the weather type of the current environment according to the brightness change of the image data. However, when the ambient light around the vehicle changes greatly, the vehicle is likely to identify the wrong weather type after obtaining the image data, thereby causing the safety control strategy to lag, significantly increasing the driving safety risk.

[0059] In view of the above phenomenon, the present application provides a determination method of a driving control strategy, which is applied to a vehicle comprising an image acquisition device, and the method comprises: taking target image data comprising a road in front of the vehicle by the image acquisition device, and determining a respective transmittance parameter corresponding to each pixel point in the target image data; in the case that the vehicle is detected to be in a low-visibility environment in combination with the transmittance parameter, determining a low-visibility region and a drivable region contained in the target image data; determining a target weather type corresponding to the vehicle in combination with the low-visibility region and the drivable region, and determining a matched target control strategy according to the target weather type.

[0060] In this way, the present application solves the technical problem in the related art that vehicles easily identify the wrong weather type. That is, the present application calculates the transmittance parameters of each pixel point in the image data, so that the electronic device can fully consider the physical attenuation characteristics of fog to light, avoid determining the target weather type based on the overall brightness of the image data, and thus enable the electronic device to obtain the accurate weather type, thereby screening the appropriate control strategy to reduce the driving safety risk during vehicle driving.

[0061] Based on the overall concept of the method for determining the driving control strategy of the present application, the embodiment of the present application provides a method for determining the driving control strategy, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for determining a driving control strategy of the present application. In this embodiment, the method for determining a driving control strategy is applied to a vehicle including an image acquisition device, and the method for determining a driving control strategy includes steps S10 to S30:

[0062] Step S10: capturing target image data including the road ahead of the vehicle through an image acquisition device, and determining the transmittance parameter corresponding to each pixel point in the target image data;

[0063] In this embodiment, while the vehicle is driving, the electronic device configured on the vehicle first controls the image acquisition device configured on the vehicle to capture target image data containing the road ahead through the image acquisition device. The electronic device inputs the target image data into the image processing module configured on the vehicle, and the image processing module processes the target image data to calculate the transmittance parameters corresponding to each pixel point.

[0064] For example, see Figure 2 , Figure 2 For the target image data involved in the embodiment of the method for determining the driving control strategy of the present application, when the vehicle is driving, the electronic device configured on the vehicle first controls the camera configured on the vehicle to make the camera capture a frame such as Figure 2 As shown, the electronic device includes target image data of the road ahead of the vehicle. The target image data is input into the image processing module configured therein. The image processing module performs denoising and white balance adjustment on the target image data to obtain a standardized RGB matrix, and calls a preset transmittance calculation formula:

[0065]

[0066] The standardized RGB matrix is ​​processed to determine the transmittance parameter t(x) corresponding to each pixel point on the target image data.

[0067] It should be noted that in the transmittance calculation formula, A is the atmospheric light component, ω is the correction coefficient, and Ω(x) is the local area centered on the pixel point x.

[0068] It is understandable that the electronic device can first obtain the atmospheric scattering model:

[0069] I(x)=J(x)t(x)+A(1-t(x)), where I(x) is the foggy image and J(x) is the fog-free image;

[0070] After obtaining the atmospheric scattering model, the electronic device determines, based on the atmospheric scattering model and the preset foggy image I(x) and fog-free image J(x), that the foggy image I(x) is composed of the light reflected by the object after being attenuated by the fog and the atmospheric light reflected by the fog.

[0071] At the same time, the electronic device processes the fog-free image J(x) to determine that the dark channel of the non-sky area of ​​the fog-free image J(x) is close to zero, that is:

[0072] J dark (x) = min y∈Ω(x) (min c∈(r,g,b) J c (y))→0;

[0073] Therefore, electronic devices can combine the dark channel prior algorithm and the atmospheric scattering model and then perform normalization processing to obtain a transmittance calculation formula.

[0074] In this way, the electronic device can process the target image data to determine the transmittance parameters corresponding to each pixel point in the target image data, thereby fully considering the physical attenuation characteristics of fog on light to determine the clear areas and low visibility areas in the image data.

[0075] In a feasible implementation, after the above step S10, the method for determining the driving control strategy of the present application may further include steps S101 to S103:

[0076] Step S101: obtaining a preset transmittance threshold, and comparing each transmittance parameter with the transmittance threshold to obtain a plurality of comparison results;

[0077] Step S102: determining a plurality of target pixel points according to the plurality of comparison results, wherein the target pixel points are pixel points whose transmittance parameters do not reach the transmittance threshold;

[0078] Step S103: Determine the number of low-transmittance pixel points corresponding to the plurality of target pixel points, and when it is detected that the number of low-transmittance pixel points reaches a preset threshold, determine that the vehicle is in a low-visibility environment, and execute the step of determining the low-visibility area and the drivable area contained in the target image data.

[0079] In this embodiment, after determining the transmittance parameters corresponding to each pixel point, the electronic device can first read the storage module configured by itself to obtain a preset transmittance threshold, and compare each transmittance parameter with the transmittance threshold to obtain multiple comparison results. Afterwards, the electronic device traverses the multiple comparison results, and thereby determines, in each pixel point, a plurality of abnormal pixel points whose transmittance parameters do not reach the transmittance threshold based on the multiple comparison results. Finally, the electronic device determines the number of low-transmittance pixels among the multiple abnormal pixels and obtains a preset pixel number threshold. When the electronic device detects that the number of low-transmittance pixels reaches the pixel number threshold, the electronic device determines that the vehicle is in a low-visibility environment. Similarly, when the electronic device detects that the number of low-transmittance pixels does not reach the pixel number threshold, the electronic device determines that the vehicle is in a normal driving environment.

[0080] Exemplarily, for example, after determining the transmittance parameter t(x) corresponding to each pixel point, the electronic device may first read the above-mentioned storage module to obtain a preset transmittance threshold α, and the electronic device then compares each transmittance parameter t(x) with the transmittance threshold α to obtain multiple comparison results. Afterwards, the electronic device traverses the multiple comparison results, and thereby determines, based on the multiple comparison results, abnormal pixels whose transmittance parameter t(x) is less than the transmittance threshold α in each pixel point. Finally, the electronic device determines the number of low-transmittance pixels of the abnormal pixels, and determines 70% of the total number of pixels in the target image data as the pixel number threshold. The electronic device thus determines that the vehicle is in a low-visibility environment when it detects that the number of low-transmittance pixels reaches the pixel number threshold. Similarly, when it detects that the number of low-transmittance pixels does not reach the pixel number threshold, the electronic device determines that the vehicle is in a normal driving environment.

[0081] In this way, the electronic device can process the target image data to determine the transmittance parameters corresponding to each pixel point in the target image data, thereby fully considering the physical attenuation characteristics of fog on light to determine the clear areas and low visibility areas in the image data.

[0082] In a feasible implementation, in the above step S10, the method for determining the driving control strategy of the present application may further include steps S104 to S105:

[0083] Step S104: determining each cluster area in the target image data, and determining a transmission change parameter between adjacent cluster areas based on each transmittance parameter;

[0084] Step S105: When it is detected that at least one transmission change parameter reaches a preset change parameter threshold, it is determined that the vehicle is in the low visibility environment, and the step of determining the low visibility area and the drivable area included in the target image data is executed.

[0085] It should be noted that the transmission change parameter is an indicator for quantifying the visibility difference between adjacent regions. It can be understood that the larger the transmission change parameter is, the greater the visibility difference between adjacent cluster regions is.

[0086] In this embodiment, after determining the transmittance parameters corresponding to each pixel point, the electronic device may first call an image segmentation algorithm to divide the target image data into different cluster areas, and calculate the transmission parameter mean corresponding to each cluster area based on each transmittance parameter. The electronic device then calculates the transmission change parameter generated between any two adjacent cluster areas based on each transmittance mean. Finally, the electronic device reads the above-mentioned storage module to obtain a preset change parameter threshold, and determines that the vehicle is in a low visibility environment when it detects that at least one transmission change parameter reaches the change parameter threshold. Similarly, when it detects that all transmission change parameters do not reach the change parameter threshold, the electronic device determines that the vehicle is in a normal driving environment.

[0087] Exemplarily, for example, after determining the transmittance parameter t(x) corresponding to each pixel point, the electronic device can also call an image segmentation algorithm to divide the target image data into multiple cluster areas. The electronic device then calculates the transmittance mean corresponding to each cluster area based on the transmittance parameters t(x) contained in each cluster area. The electronic device then calculates the transmittance change parameter generated between any two adjacent cluster areas based on the transmittance mean values. Afterwards, the electronic device reads the above-mentioned storage module to obtain a preset change parameter threshold, and compares each transmission change parameter with the change parameter threshold respectively. When it is detected that at least one transmission change parameter reaches the change parameter threshold, the two cluster areas with larger changes are determined to be transition areas between different media in the air (such as fog and clear air), thereby determining that the vehicle is in a low visibility environment. Similarly, when the electronic device detects that all transmission change parameters do not reach the change parameter threshold, it determines that the vehicle is in a normal driving environment.

[0088] In this way, the electronic device can process the target image data to determine the transmittance parameters corresponding to each pixel point in the target image data, thereby fully considering the physical attenuation characteristics of fog on light to determine the clear areas and low visibility areas in the image data.

[0089] Step S20: when it is detected that the vehicle is in a low-visibility environment in combination with the transmittance parameter, determining the low-visibility area and the drivable area included in the target image data;

[0090] In this embodiment, when the electronic device detects that the vehicle is in a low-visibility scene, it first calls a preset deep learning model to process the target image data to extract the image features contained in the target image data, and then identifies the key elements such as lane lines, road boundaries, obstacles contained in the target image data based on the image features, and then identifies the drivable area contained in the target image data based on each key element. At the same time, the electronic device determines the above-mentioned abnormal pixel points contained in the target image data based on each transmittance parameter, and clusters the abnormal pixel points to determine the low-visibility area contained in the target image data.

[0091] Exemplarily, for example, when the electronic device detects that a vehicle is in a low-visibility scene, it first calls the preset semantic segmentation network U-Net to process the target image data to extract the image features of the target image data, and identifies the key elements such as lane lines, road boundaries, obstacles, etc. contained in the target image data based on the image features. The semantic segmentation network U-Net models each key element and converts the 2D image into a drivable area in 3D space through BEV technology. At the same time, the electronic device determines the abnormal pixel points whose transmittance parameter t(x) is less than the above-mentioned transmittance threshold α in the target image data based on each transmittance parameter t(x), and clusters each abnormal pixel point to obtain a low-visibility area.

[0092] In this way, the electronic device can accurately segment the target image data to determine the passable area and the foggy area within the target image data, and then determine the weather type according to the degree of overlap between the passable area and the foggy area.

[0093] Step S30: determining a target weather type corresponding to the vehicle in combination with the low visibility area and the drivable area, and determining a matching target control strategy according to the target weather type;

[0094] In this embodiment, after determining the low-visibility area and the drivable area contained in the target image data, the electronic device traverses the low-visibility area and the drivable area to determine the overlapping ratio between the low-visibility area and the drivable area, and then determines the target weather type corresponding to the vehicle based on the overlapping ratio. The electronic device queries a preset weather type-control strategy database based on the target weather type to determine a target control strategy that matches the target weather type.

[0095] Exemplarily, for example, after determining the low-visibility area and the drivable area contained in the target image data, the electronic device first traverses the low-visibility area to determine the first pixel coordinates contained in the low-visibility area, and traverses the drivable area to determine the second pixel coordinates contained in the drivable area. The electronic device then traverses the first pixel coordinates and the second pixel coordinates to determine the overlapping ratio between the low-visibility area and the drivable area, and determines the target weather type corresponding to the vehicle as a heavy fog weather type or a normal weather type based on the overlapping ratio. Finally, the electronic device queries a preset weather type-control strategy database to determine a target control strategy that matches the target weather type.

[0096] In this way, the electronic device can determine the weather type based on the degree of overlap between the traversable area and the foggy area, and then select the target control strategy that matches the weather type.

[0097] In a feasible implementation manner, the above step S30 may specifically include steps S301 to S304:

[0098] Step S301: determining the coordinates of each first pixel included in the low visibility area, and determining the coordinates of each second pixel included in the drivable area;

[0099] Step S302: traversing each of the first pixel coordinates and each of the second pixel coordinates to determine each overlapping pixel coordinate, and determining an overlapping area ratio according to each of the overlapping pixel coordinates;

[0100] Step S303: when it is detected that the overlapping area ratio reaches a preset overlapping ratio threshold, determining that the target weather type corresponding to the vehicle is a heavy fog weather type;

[0101] Step S304: When it is detected that the overlapping area ratio does not reach the overlapping ratio threshold, it is determined that the target weather type corresponding to the vehicle is a normal weather type.

[0102] In this embodiment, after determining the low visibility area and the drivable area contained in the target image data, the electronic device first determines the first pixel coordinates in the low visibility area and the second pixel coordinates in the drivable area. Then, the electronic device compares the first pixel coordinates with the second pixel coordinates to determine the overlapping pixel coordinates and the number of overlapping coordinates of the overlapping pixel coordinates. The electronic device determines the overlap ratio between the drivable area and the low visibility area based on the number of overlapping coordinates. Finally, the electronic device obtains a preset overlap ratio threshold, and when it detects that the overlap ratio reaches the overlap ratio threshold, it determines that the target weather type of the vehicle is a heavy fog weather type. Similarly, when it detects that the overlap ratio does not reach the overlap ratio threshold, the electronic device determines that the target weather type of the vehicle is a normal weather type.

[0103] Exemplarily, for example, after determining the low visibility area and the drivable area contained in the target image data, the electronic device first traverses the low visibility area to determine the first pixel coordinates contained therein, and traverses the drivable area to determine the second pixel coordinates contained therein. Afterwards, the electronic device compares each first pixel coordinate with each second pixel coordinate respectively to determine the first pixel coordinate that overlaps with the second pixel coordinate as the overlapping pixel coordinate, and determines the number of overlapping coordinates of the overlapping pixel coordinates. At the same time, the electronic device detects the number of target coordinates of the second pixel coordinates, and calculates the coordinate overlap ratio based on the number of overlapping coordinates and the number of target coordinates. Finally, the electronic device reads the above-mentioned storage module to obtain a preset overlap ratio threshold, and when detecting that the coordinate overlap ratio reaches the overlap ratio threshold, determines that the target weather type is a foggy weather type. Similarly, when detecting that the coordinate overlap ratio does not reach the overlap ratio threshold, the electronic device determines that the target weather type is a normal weather type.

[0104] In this embodiment, when the vehicle is driving, the electronic device configured on the vehicle first controls the image acquisition device configured on the vehicle to capture target image data containing the road ahead through the image acquisition device. The electronic device inputs the target image data into the image processing module configured on itself, and the image processing module processes the target image data to calculate the transmittance parameter corresponding to each pixel point. After that, the electronic device calls a preset deep learning model to process the target image data to extract the image features contained in the target image data, thereby identifying the lane lines, road boundaries, obstacles contained in the target image data based on the image features. and other key elements, and then identify the drivable area contained in the target image data according to each key element. At the same time, the electronic device determines the above-mentioned abnormal pixel points contained in the target image data based on each transmittance parameter, and clusters the abnormal pixel points to determine the low visibility area contained in the target image data. Finally, the electronic device traverses the low visibility area and the drivable area to determine the overlapping ratio between the low visibility area and the drivable area, and then determines the target weather type corresponding to the vehicle according to the overlapping ratio. The electronic device queries the preset weather type-control strategy database according to the target weather type to determine the target control strategy that matches the target weather type.

[0105] In this way, the present application solves the technical problem in the related art that vehicles easily identify the wrong weather type. That is, the present application calculates the transmittance parameters of each pixel point in the image data, so that the electronic device can fully consider the physical attenuation characteristics of fog to light, avoid determining the target weather type based on the overall brightness of the image data, and thus enable the electronic device to obtain the accurate weather type, thereby screening the appropriate control strategy to reduce the driving safety risk during vehicle driving.

[0106] Based on the first embodiment of the present application, a second embodiment of the present application is proposed. In the second embodiment of the present application, the same or similar contents as those of the above embodiments can be referred to above and will not be described in detail. On this basis, the vehicle further includes a vibration wave sensor. After the above step S10, the method for determining the driving control strategy of the present application can also include steps A10 to A40:

[0107] Step A10: When it is detected that the vehicle is in a low-visibility environment, obtaining an initial vibration wave signal on the vehicle through the vibration wave sensor;

[0108] Step A20: Processing the initial vibration wave signal to obtain an average impact energy density and a single impact energy density;

[0109] Step A30: When it is detected that the average impact energy density reaches a preset first energy density threshold, determining that the target weather type is a sandstorm weather type, and determining a matching target control strategy according to the sandstorm weather type;

[0110] Step A40: When it is detected that the energy density of the single impact reaches a preset second energy density threshold, the target weather type is determined to be a hail weather type, and a matching target control strategy is determined based on the hail weather type, wherein the second energy density threshold is greater than the first energy density threshold.

[0111] It should be noted that the average impact energy density is the total energy integrated over a unit time period in the vibration wave signal and is used to characterize the intensity of sustained impacts to the vehicle. Furthermore, the single impact energy density is the energy integrated over the duration of a single high-amplitude impact in the vibration wave signal and is used to characterize the intensity of instantaneous impacts to the vehicle.

[0112] In this embodiment, upon detecting that the vehicle is in a low-visibility environment, the electronic device further invokes a vibration wave sensor configured on the vehicle to collect an initial vibration wave signal generated on the vehicle body, and processes the initial vibration wave signal to obtain a time-domain vibration signal. The electronic device then intercepts the time-domain vibration signal within a fixed time window and calculates the energy integral corresponding to the signal within the fixed time window to obtain an average impact energy density. Simultaneously, the electronic device reads the peak value of the time-domain vibration signal and intercepts the time window before and after the peak value to calculate the peak value to obtain a single energy impact energy density. Finally, the electronic device reads the aforementioned storage module to obtain a preset first energy density threshold and a second energy density threshold greater than the first energy density threshold. Upon detecting that the average impact energy density reaches the first energy density threshold, the electronic device determines that the target weather type is sandstorm weather, and thereby queries a preset weather type-control strategy database to determine a target control strategy matching the sandstorm weather type. Similarly, upon detecting that the single energy impact energy density reaches the second energy density threshold, the electronic device determines that the target weather type is hail weather, and thereby queries a preset weather type-control strategy database to determine a target control strategy matching the hail weather type.

[0113] Exemplarily, for example, when the electronic device detects that the vehicle is in a low visibility environment, it further calls the vibration wave sensor configured on the vehicle to collect the vibration wave signal generated on the vehicle body. The electronic device then processes the signal spectrum of the vibration wave signal to extract the time domain waveform data, and performs baseline calibration and noise filtering on the time domain waveform data to obtain the time domain vibration signal. Afterwards, the electronic device extracts the signal within a fixed time window of the time domain vibration signal, and calculates the energy integral of the signal within the fixed time window to obtain the average impact energy density. At the same time, the electronic device reads the signal peak of the time domain vibration signal, and intercepts 0.1s before and after the signal peak as a reference time window, and determines the single impact energy density of the signal peak based on the reference time window. Afterwards, the electronic device first reads the above-mentioned storage module to obtain a preset first energy density threshold, and obtains a second energy density threshold that is greater than the first energy density threshold. The electronic device The device compares the average impact energy density with the first energy density threshold, and when it is detected that the average impact energy density reaches the first energy density threshold, it determines that the vehicle is continuously impacted within a fixed time window and the vehicle is in a low visibility environment. At this time, the electronic device determines that the target weather type of the vehicle is a sandstorm weather type, and queries the preset weather type-control strategy database to determine the target control strategy that matches the sandstorm weather type; similarly, the electronic device compares the single impact energy density with the second energy density threshold, and when it is detected that the single impact energy density reaches the second energy density threshold, it determines that the vehicle is subjected to a large impact in a shorter period of time and the vehicle is in a low visibility environment. At this time, the electronic device determines that the target weather type of the vehicle is a hail weather type, and queries the preset weather type-control strategy database to determine the target control strategy that matches the hail weather type.

[0114] In this way, the electronic device can accurately distinguish the type of impact suffered by the vehicle when the vehicle is impacted, and then determine the target weather type of the vehicle as sandstorm weather or hail weather based on the type of impact suffered by the vehicle.

[0115] Based on the first embodiment and / or the second embodiment of the present application, a third embodiment of the present application is proposed. In the third embodiment of the present application, the same or similar contents as those of the above embodiments can be referred to above and will not be described in detail. On this basis, the vehicle further includes a vibration wave sensor. After the above step S10, the method for determining the driving control strategy of the present application can also include steps B10 to B30:

[0116] Step B10: When it is detected that the vehicle is in a low visibility environment, detecting chassis parameters of the vehicle;

[0117] Step B20: determining the driving state of the vehicle according to the vehicle chassis parameters, wherein the driving state is a slipping state or a normal state;

[0118] Step B30: When it is detected that the driving state is the slipping state, it is determined that the target weather type corresponding to the vehicle is a freezing rain weather type, and a matching target control strategy is determined according to the freezing rain weather type.

[0119] It should be noted that these vehicle chassis parameters characterize the contact characteristics between the tires and the road surface during vehicle operation. Furthermore, a slipping state occurs when insufficient friction between the tires and the road surface causes a significant discrepancy between the wheel speed and the actual vehicle speed. Understandably, a slipping state carries a high risk of vehicle loss of control.

[0120] In this embodiment, when the electronic device detects that the vehicle is in a low visibility environment, it can also first detect the vehicle chassis parameters of the vehicle. After that, the electronic device reads the multiple wheel end torque parameters and multiple wheel speed parameters contained in the vehicle chassis parameters, and determines whether the corresponding driving state of the vehicle is a slipping state or a normal state based on the multiple wheel end torque parameters and / or the multiple wheel speed parameters. Finally, when the electronic device detects that the driving state of the vehicle is a slipping state, it determines that the target weather type corresponding to the vehicle is a freezing rain weather type, and queries the preset weather type-control strategy database to determine the target control strategy that matches the freezing rain weather type.

[0121] Exemplarily, for example, after screening and obtaining the target weather type, the electronic device may first detect the vehicle chassis parameters of the vehicle. Afterwards, the electronic device reads the multiple wheel-end torque parameters and multiple wheel speed parameters contained in the vehicle chassis parameters, and judges the contact state between the vehicle's tires and the road surface based on the wheel-end torque parameters and / or wheel speed parameters, and then determines whether the vehicle's driving state is a slipping state or a normal state. Finally, when the electronic device detects that the vehicle's driving state is a slipping state, it determines that the weather type corresponding to the vehicle is freezing rain weather, and thereby queries the preset weather type-control strategy database to determine the target control strategy that matches the freezing rain weather type.

[0122] In this way, the electronic equipment can detect in real time whether the vehicle is in a slipping state through the vehicle's chassis parameters, thereby determining whether the vehicle is in freezing rain weather, in order to screen a more matching control strategy and reduce the incidence of vehicle slipping accidents in freezing rain weather.

[0123] In a feasible implementation manner, the vehicle chassis parameters include multiple wheel end torque parameters. The above step B20 may specifically include at least one of steps B201 to B203:

[0124] Step B201: determining a first target torque parameter among the plurality of wheel-end torque parameters, and determining that the driving state of the vehicle is the slipping state when it is detected that the first target torque parameter reaches a preset first torque threshold, wherein the first target torque parameter is the wheel-end torque parameter with the largest value;

[0125] Step B202: Dividing the plurality of wheel-end torque parameters into a plurality of torque parameter groups, and determining that the driving state is the slipping state when detecting that at least one torque parameter group reaches a preset second torque threshold, wherein the second torque threshold is greater than the first torque threshold;

[0126] Step B203: When a second target torque parameter is detected among the multiple wheel-end torque parameters, the driving state is determined to be the slipping state, wherein the torque direction of the second target torque parameter is opposite to the torque direction of the other wheel-end torque parameters.

[0127] In this embodiment, after detecting the vehicle chassis parameters, the electronic device may first read multiple wheel-end torque parameters included in the vehicle chassis parameters and determine a first target torque parameter having the largest value among the multiple wheel-end torque parameters. Thereafter, when the electronic device detects that the first target torque parameter reaches a preset first torque threshold, the electronic device determines that the vehicle's driving state is a slipping state.

[0128] or,

[0129] The electronic device may also read multiple wheel-end torque parameters included in the vehicle chassis parameters and divide the multiple wheel-end torque parameters according to the wheel position information, so as to divide the wheel-end torque parameters corresponding to the wheels on the same side / level into a torque parameter group. Thereafter, the electronic device obtains a second torque threshold value greater than the first torque threshold value, and compares the multiple torque parameter groups with the second torque threshold value respectively. When it is detected that at least one torque parameter group reaches the second torque threshold value, it is determined that the driving state of the vehicle is a slipping state.

[0130] or,

[0131] The electronic device can also read multiple wheel-end torque parameters included in the vehicle chassis parameters, and determine that the vehicle's driving state is a slipping state when it detects that there is a second target torque parameter among the multiple wheel-end torque parameters, the torque direction of which is opposite to the torque direction of other wheel-end torque parameters.

[0132] For example, after detecting the vehicle chassis parameters, the electronic device may first read the four wheel-end torque parameters included in the vehicle chassis parameters, and determine a first target torque parameter having the largest value among the four wheel-end torque parameters. Thereafter, the electronic device reads the above-mentioned storage module to obtain a preset first torque threshold, and compares the first target torque parameter with the first torque threshold. If the electronic device detects that the first target torque parameter is greater than or equal to the first torque threshold, the electronic device determines a duration of the first target torque parameter, and if the electronic device detects that the duration reaches a preset time threshold, determines that the vehicle's driving state is a slipping state.

[0133] Similarly,

[0134] After reading the four wheel-end torque parameters contained in the vehicle chassis parameters, the electronic device may further divide the four wheel-end torque parameters according to the wheel position information to divide the wheel-end torque parameters corresponding to the wheels on the same side into a torque parameter group. Thereafter, the electronic device reads the storage module to obtain a preset second torque threshold value that is greater than the first torque threshold value. The electronic device then compares the two torque parameter groups with the second torque threshold value respectively, thereby determining that the vehicle's driving state is a slipping state when it detects that the two wheel-end torque parameters contained in at least one torque parameter group are simultaneously higher than the second torque threshold value D; alternatively, the electronic device may further divide the wheel-end torque parameters corresponding to the wheels in a horizontal position into a torque parameter group. Thereafter, the electronic device compares the two torque parameter groups with the second torque threshold value respectively, thereby determining that the vehicle's driving state is a slipping state when it detects that the two wheel-end torque parameters contained in at least one wheel-end torque parameter group are simultaneously higher than the second torque threshold value;

[0135] Similarly,

[0136] After reading the four wheel-end torque parameters contained in the vehicle chassis parameters, the electronic device can also first detect the torque direction corresponding to each of the four wheel-end torque parameters, so as to determine that the vehicle's driving state is a slipping state when it detects that there is at least one second target torque parameter with an opposite direction among the four wheel-end torque parameters.

[0137] In this way, the electronic equipment can detect in real time whether the vehicle is in a slipping state through the vehicle's chassis parameters, thereby determining whether the vehicle is in freezing rain weather, in order to screen a more matching control strategy and reduce the incidence of vehicle slipping accidents in freezing rain weather.

[0138] In a feasible implementation manner, the vehicle chassis parameters further include a plurality of wheel speed parameters, and the above step B20 may further include steps B204 to B206:

[0139] Step B204: determining a target wheel speed parameter, and calculating wheel speed differences corresponding to the target wheel speed parameter, wherein the target wheel speed parameter is the wheel speed parameter with the largest value;

[0140] Step B205: determining a target wheel speed difference and obtaining a preset first wheel speed difference threshold, wherein the target wheel speed difference is a wheel speed difference with the largest value;

[0141] Step B206: When it is detected that the target wheel speed difference reaches the first wheel speed difference threshold, determining that the driving state is the slipping state.

[0142] In this embodiment, after detecting the vehicle chassis parameters, the electronic device can also read multiple wheel speed parameters included in the vehicle chassis parameters, and determine the target wheel speed parameter with the largest value among the multiple wheel speed parameters. The electronic device then calculates the wheel speed differences generated between the target wheel speed parameter and other wheel speed parameters. Afterwards, the electronic device determines the target wheel speed difference with the largest value among the wheel speed differences. At the same time, the electronic device reads the storage module to obtain a preset first wheel speed difference threshold. Finally, when the electronic device detects that the target wheel speed difference reaches the first wheel speed difference threshold, it determines that the vehicle's driving state is a slipping state.

[0143] Exemplarily, for example, after detecting the vehicle chassis parameters, the electronic device can also read the wheel speed parameters corresponding to each of the four tires included in the vehicle chassis parameters, and screen out the target wheel speed parameter with the largest value from the four wheel speed parameters. The electronic device then calculates the wheel speed difference generated between the target wheel speed parameter and the other wheel speed parameters. Afterwards, the electronic device determines the target wheel speed parameter with the largest value from each wheel speed difference, and reads the above-mentioned storage module to obtain a preset first wheel speed difference threshold. Finally, the electronic device compares the target wheel speed difference with the first wheel speed difference threshold, and thereby determines the duration corresponding to the target wheel speed difference when it is detected that the target wheel speed difference is greater than the first wheel speed difference threshold. When the electronic device determines that the duration reaches the preset time threshold, it determines that the vehicle's driving state is a slipping state.

[0144] It should be noted that, in this embodiment and another embodiment, after detecting the wheel speed parameters corresponding to each of the four tires, the electronic device can also calculate the four wheel speed parameters to obtain the wheel speed difference between the tires on the same side / at the same level. At the same time, the electronic device reads the above-mentioned storage module to obtain a second wheel speed difference threshold that is greater than the first wheel speed difference threshold. Afterwards, the electronic device compares each wheel speed difference with the second wheel speed difference threshold respectively, and thus determines that the vehicle's driving state is a slipping state when it is detected that at least one wheel speed difference reaches the second wheel speed difference threshold.

[0145] In this way, the electronic equipment can detect in real time whether the vehicle is in a slipping state through the vehicle's chassis parameters, thereby determining whether the vehicle is in freezing rain weather, in order to screen a more matching control strategy and reduce the incidence of vehicle slipping accidents in freezing rain weather.

[0146] For example, in order to help understand the implementation process of the method for determining the driving control strategy obtained by combining this embodiment with the above embodiments, please refer to Figure 3 , Figure 3 This is a brief flowchart of the method for determining the driving control strategy of this application, specifically:

[0147] In this embodiment, while a vehicle is traveling, an electronic device configured on the vehicle first controls an image acquisition device configured on the vehicle to capture target image data including a road ahead through the image acquisition device. The electronic device processes the target image data to determine a transmittance parameter corresponding to each pixel in the target image data. The electronic device then reads a memory module configured therein to obtain a preset transmittance threshold value and compares each transmittance parameter with the transmittance threshold value. Based on multiple comparison results, the electronic device identifies abnormal pixels whose transmittance parameters do not meet the transmittance threshold value. The electronic device then determines whether the vehicle is in a low-visibility environment based on the number of abnormal pixels. If the electronic device determines that the vehicle is in a low-visibility environment, the electronic device further processes the target image data to identify low-visibility areas and drivable areas within the target image data. The electronic device detects an overlap ratio between the low-visibility area and the drivable area. If the overlap ratio reaches a preset overlap ratio threshold value, the electronic device determines that the weather type corresponding to the vehicle's environment is heavy fog. Finally, the electronic device determines a first control strategy that matches the heavy fog weather type based on a preset weather type-control strategy database.

[0148] In addition, when the electronic device determines that the vehicle is in a low visibility environment, it can also call the vibration wave sensor configured on the vehicle to collect the initial vibration wave signal generated by the outside of the vehicle body. The electronic device then processes the initial vibration wave signal to extract the corresponding average impact energy density and single impact energy density. Thereafter, the electronic device obtains a preset first energy density threshold and a second energy density threshold that is greater than the first energy density threshold. When the electronic device detects that the average impact energy density reaches the first energy density threshold, it determines that the weather type corresponding to the vehicle environment is a sandstorm weather type. Finally, the electronic device determines the second control strategy that matches the sandstorm weather type based on the preset weather type-control strategy database. When the electronic device detects that the single impact energy density reaches the second energy density threshold, it determines that the weather type corresponding to the vehicle environment is a hail weather type. The electronic device determines the third control strategy that matches the hail weather type based on the preset weather type-control strategy database.

[0149] In addition, when the electronic device determines that the vehicle is in a low visibility environment, it can also call the chassis speed sensor configured on the vehicle to detect the vehicle chassis, thereby determining the vehicle chassis parameters. The electronic device then reads multiple wheel-end torque parameters / wheel speed parameters contained in the vehicle chassis parameters, and detects whether the vehicle's driving state is in a slipping state or a normal state based on the multiple wheel-end torque parameters / wheel speed parameters. Finally, when the electronic device detects that the vehicle's driving state is in a slipping state, it determines that the weather type corresponding to the vehicle's environment is a freezing rain weather type. The electronic device determines a fourth control strategy that matches the freezing rain weather type based on a preset weather type-control strategy database.

[0150] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the method for determining the driving control strategy of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0151] This application also provides a device for determining a driving control strategy, please refer to Figure 4 The driving control strategy determination device is applied to a vehicle including an image acquisition device, and the device includes:

[0152] a transmittance detection module 10 for capturing target image data including the road ahead of the vehicle through the image acquisition device and determining a transmittance parameter corresponding to each pixel point in the target image data;

[0153] an image processing module 20 for determining a low visibility area and a drivable area included in the target image data when detecting that the vehicle is in a low visibility environment based on the transmittance parameter;

[0154] The strategy screening module 30 is configured to determine a target weather type corresponding to the vehicle based on the low visibility area and the drivable area, and determine a matching target control strategy according to the target weather type.

[0155] In a feasible implementation manner, the transmittance detection module 10 is further used to:

[0156] Obtaining a preset transmittance threshold, and comparing each of the transmittance parameters with the transmittance threshold to obtain a plurality of comparison results;

[0157] Determining a plurality of target pixel points according to the plurality of comparison results, wherein the target pixel points are pixel points whose transmittance parameters do not reach the transmittance threshold;

[0158] Determine the number of low-transmittance pixel points corresponding to the plurality of target pixel points, and when it is detected that the number of low-transmittance pixel points reaches a preset threshold, determine that the vehicle is in a low-visibility environment, and execute the step of determining the low-visibility area and the drivable area contained in the target image data.

[0159] In a feasible implementation manner, the transmittance detection module 10 is further used to:

[0160] Determining each cluster area in the target image data, and determining a transmission change parameter between adjacent cluster areas based on each of the transmittance parameters;

[0161] When it is detected that at least one transmission change parameter reaches a preset change parameter threshold, it is determined that the vehicle is in the low visibility environment, and the step of determining the low visibility area and the drivable area included in the target image data is performed.

[0162] In a feasible implementation manner, the policy screening module 30 is further configured to:

[0163] Determining the coordinates of each first pixel included in the low-visibility area, and determining the coordinates of each second pixel included in the drivable area;

[0164] traversing each of the first pixel coordinates and each of the second pixel coordinates to determine each overlapping pixel coordinate, and determining an overlapping area ratio according to each of the overlapping pixel coordinates;

[0165] When it is detected that the overlapping area ratio reaches a preset overlapping ratio threshold, determining that the target weather type corresponding to the vehicle is a heavy fog weather type;

[0166] When it is detected that the overlapping area ratio does not reach the overlapping ratio threshold, it is determined that the target weather type corresponding to the vehicle is a normal weather type.

[0167] In a feasible embodiment, the vehicle further includes a vibration wave sensor, and the strategy screening module 30 is further configured to:

[0168] When detecting that the vehicle is in a low visibility environment, acquiring an initial vibration wave signal on the vehicle through the vibration wave sensor;

[0169] Processing the initial vibration wave signal to obtain an average impact energy density and a single impact energy density;

[0170] When it is detected that the average impact energy density reaches a preset first energy density threshold, determining that the target weather type is a sandstorm weather type, and determining a matching target control strategy according to the sandstorm weather type;

[0171] When it is detected that the single impact energy density reaches a preset second energy density threshold, the target weather type is determined to be a hail weather type, and a matching target control strategy is determined based on the hail weather type, wherein the second energy density threshold is greater than the first energy density threshold.

[0172] In a feasible implementation manner, the policy screening module 30 is further configured to:

[0173] In the case where it is detected that the vehicle is in a low visibility environment, detecting vehicle chassis parameters of the vehicle;

[0174] determining a driving state of the vehicle according to the vehicle chassis parameters, wherein the driving state is a slipping state or a normal state;

[0175] When it is detected that the driving state is the slipping state, it is determined that the target weather type corresponding to the vehicle is a freezing rain weather type, and a matching target control strategy is determined according to the freezing rain weather type.

[0176] In a feasible implementation manner, the vehicle chassis parameters include multiple wheel end torque parameters, and the strategy screening module 30 is further configured to:

[0177] determining a first target torque parameter among the plurality of wheel end torque parameters, and determining that the driving state of the vehicle is the slipping state when it is detected that the first target torque parameter reaches a preset first torque threshold, wherein the first target torque parameter is the wheel end torque parameter with the largest value;

[0178] dividing the plurality of wheel-end torque parameters into a plurality of torque parameter groups, and determining that the driving state is the slipping state when detecting that at least one torque parameter group reaches a preset second torque threshold, wherein the second torque threshold is greater than the first torque threshold;

[0179] When a second target torque parameter is detected among the plurality of wheel end torque parameters, the driving state is determined to be the slip state, wherein a torque direction of the second target torque parameter is opposite to a torque direction of the other wheel end torque parameters.

[0180] In a feasible implementation, the vehicle chassis parameters further include a plurality of wheel speed parameters, and the strategy screening module 30 is further configured to:

[0181] Determining a target wheel speed parameter and calculating wheel speed differences corresponding to the target wheel speed parameter, wherein the target wheel speed parameter is the wheel speed parameter with the largest value;

[0182] Determining a target wheel speed difference and obtaining a preset first wheel speed difference threshold, wherein the target wheel speed difference is a wheel speed difference with a maximum value;

[0183] When it is detected that the target wheel speed difference reaches the first wheel speed difference threshold, the driving state is determined to be the slipping state.

[0184] The driving control strategy determination device provided in this application, utilizing the driving control strategy determination method described in the aforementioned embodiments, can address the technical issue in related art whereby vehicles are prone to incorrectly identifying weather conditions. Compared to the prior art, the driving control strategy determination device provided in this application offers the same beneficial effects as the driving control strategy determination method described in the aforementioned embodiments. Other technical features of the driving control strategy determination device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0185] The present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for determining the driving control strategy in the above-mentioned embodiment one.

[0186] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include, but is not limited to, an electronic device configured in a vehicle and equipped with an image processing module, or a mobile terminal, data storage control terminal, PC, or other terminal connected to an electronic control unit of the electronic device. Figure 5 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0187] like Figure 5 As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the electronic device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems may be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape or a hard disk; and a communication device 1009. The communication device 1009 may allow the electronic device to communicate with other devices wirelessly or wired to exchange data. Although the figures show electronic devices with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0188] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0189] The electronic device provided in this application, utilizing the method for determining the driving control strategy described in the aforementioned embodiment, can resolve the technical issue in related art whereby vehicles are prone to incorrectly identifying the weather type. Compared to the prior art, the electronic device provided in this application has the same beneficial effects as the method for determining the driving control strategy described in the aforementioned embodiment. Other technical features of this electronic device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0190] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0191] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0192] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, and the computer-readable program instructions are used to execute the method for determining the driving control strategy in the above-mentioned embodiment.

[0193] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0194] The computer-readable storage medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0195] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device: captures target image data containing the road in front of the vehicle through the image acquisition device, and determines the transmittance parameters corresponding to each pixel point in the target image data; when it is detected that the vehicle is in a low-visibility environment in combination with the transmittance parameters, determines the low-visibility area and the drivable area contained in the target image data; determines the target weather type corresponding to the vehicle in combination with the low-visibility area and the drivable area, and determines a matching target control strategy based on the target weather type.

[0196] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0197] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0198] The modules described in the embodiments of the present application can be implemented in the form of software or in the form of hardware. In some cases, the names of the modules do not constitute a limitation on the modules themselves.

[0199] The readable storage medium provided in the present application is a computer readable storage medium, which stores computer readable program instructions (i.e., a computer program) for executing the determination method of the driving control strategy, and can solve the technical problem that the vehicle easily identifies an incorrect weather type in the related art. Compared with the prior art, the computer readable storage medium provided in the present application has the same beneficial effects as the determination method of the driving control strategy provided in the above embodiments, and will not be described here.

[0200] The present application also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the determination method of the driving control strategy as described above.

[0201] The computer program product provided in the present application can solve the technical problem that the vehicle easily identifies an incorrect weather type in the related art. Compared with the prior art, the computer program product provided in the present application has the same beneficial effects as the determination method of the driving control strategy provided in the above embodiments, and will not be described here.

[0202] The above only describes some embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation, direct / indirect application in other related technical fields, or the like made by using the content of the present application specification and drawings is included in the patent protection scope of the present application.

Claims

1. A method for determining a driving control strategy, characterized in that: The method for determining a driving control strategy is applied to a vehicle including an image acquisition device, and the method includes: capturing target image data containing the road ahead of the vehicle through the image acquisition device, and determining the transmittance parameter corresponding to each pixel point in the target image data; In a case where it is detected in combination with the transmittance parameter that the vehicle is in a low-visibility environment, determining a low-visibility area and a drivable area included in the target image data; The target weather type corresponding to the vehicle is determined in combination with the low visibility area and the drivable area, and a matching target control strategy is determined according to the target weather type.

2. The method for determining a driving control strategy according to claim 1, wherein: After the step of determining the transmittance parameter corresponding to each pixel point in the target image data, the method includes: Obtaining a preset transmittance threshold, and comparing each of the transmittance parameters with the transmittance threshold to obtain a plurality of comparison results; Determining a plurality of target pixel points according to the plurality of comparison results, wherein the target pixel points are pixel points whose transmittance parameters do not reach the transmittance threshold; Determine the number of low-transmittance pixel points corresponding to the plurality of target pixel points, and when it is detected that the number of low-transmittance pixel points reaches a preset threshold, determine that the vehicle is in a low-visibility environment, and execute the step of determining the low-visibility area and the drivable area contained in the target image data.

3. The method for determining a driving control strategy according to claim 2, wherein: After the step of determining the transmittance parameter corresponding to each pixel point in the target image data, the method further includes: Determining each cluster area in the target image data, and determining a transmission change parameter between adjacent cluster areas based on each of the transmittance parameters; When it is detected that at least one transmission change parameter reaches a preset change parameter threshold, it is determined that the vehicle is in the low visibility environment, and the step of determining the low visibility area and the drivable area included in the target image data is performed.

4. The method for determining a driving control strategy according to claim 1, wherein: The step of determining the target weather type corresponding to the vehicle by combining the low visibility area and the drivable area includes: Determining the coordinates of each first pixel included in the low-visibility area, and determining the coordinates of each second pixel included in the drivable area; traversing each of the first pixel coordinates and each of the second pixel coordinates to determine each overlapping pixel coordinate, and determining an overlapping area ratio according to each of the overlapping pixel coordinates; When it is detected that the overlapping area ratio reaches a preset overlapping ratio threshold, determining that the target weather type corresponding to the vehicle is a heavy fog weather type; When it is detected that the overlapping area ratio does not reach the overlapping ratio threshold, it is determined that the target weather type corresponding to the vehicle is a normal weather type.

5. The method for determining a driving control strategy according to claim 1, wherein: The vehicle further includes a vibration wave sensor. After the step of determining the transmittance parameter corresponding to each pixel point in the target image data, the method further includes: When detecting that the vehicle is in a low visibility environment, acquiring an initial vibration wave signal on the vehicle through the vibration wave sensor; Processing the initial vibration wave signal to obtain an average impact energy density and a single impact energy density; When it is detected that the average impact energy density reaches a preset first energy density threshold, determining that the target weather type is a sandstorm weather type, and determining a matching target control strategy according to the sandstorm weather type; When it is detected that the single impact energy density reaches a preset second energy density threshold, the target weather type is determined to be a hail weather type, and a matching target control strategy is determined based on the hail weather type, wherein the second energy density threshold is greater than the first energy density threshold.

6. The method for determining a driving control strategy according to claim 1, wherein: After the step of determining the transmittance parameter corresponding to each pixel point in the target image data, the method further includes: In the case where it is detected that the vehicle is in a low visibility environment, detecting vehicle chassis parameters of the vehicle; determining a driving state of the vehicle according to the vehicle chassis parameters, wherein the driving state is a slipping state or a normal state; When it is detected that the driving state is the slipping state, it is determined that the target weather type corresponding to the vehicle is a freezing rain weather type, and a matching target control strategy is determined according to the freezing rain weather type.

7. The method for determining a driving control strategy according to claim 6, wherein: The vehicle chassis parameters include a plurality of wheel end torque parameters, and the step of determining the driving state of the vehicle based on the vehicle chassis parameters includes at least one of the following: determining a first target torque parameter among the plurality of wheel end torque parameters, and determining that the driving state of the vehicle is the slipping state when it is detected that the first target torque parameter reaches a preset first torque threshold, wherein the first target torque parameter is the wheel end torque parameter with the largest value; dividing the plurality of wheel-end torque parameters into a plurality of torque parameter groups, and determining that the driving state is the slipping state when detecting that at least one torque parameter group reaches a preset second torque threshold, wherein the second torque threshold is greater than the first torque threshold; When a second target torque parameter is detected among the plurality of wheel end torque parameters, the driving state is determined to be the slip state, wherein a torque direction of the second target torque parameter is opposite to a torque direction of the other wheel end torque parameters.

8. The method for determining a driving control strategy according to claim 6, wherein: The vehicle chassis parameters further include a plurality of wheel speed parameters, and the step of determining the driving state of the vehicle based on the vehicle chassis parameters further includes: Determining a target wheel speed parameter and calculating wheel speed differences corresponding to the target wheel speed parameter, wherein the target wheel speed parameter is the wheel speed parameter with the largest value; Determining a target wheel speed difference and obtaining a preset first wheel speed difference threshold, wherein the target wheel speed difference is a wheel speed difference with a maximum value; When it is detected that the target wheel speed difference reaches the first wheel speed difference threshold, the driving state is determined to be the slipping state.

9. An electronic device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for determining a driving control strategy according to any one of claims 1 to 8.

10. A vehicle, characterized in that: The vehicle includes the electronic device according to claim 9.

11. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for determining the driving control strategy according to any one of claims 1 to 8 are implemented.

12. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the method for determining the driving control strategy according to any one of claims 1 to 8.

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