Method for controlling a vehicle based on road type and vehicle

Abstract

The application provides a method for controlling a vehicle based on a road surface type and the vehicle, and the method is applied to the field of vehicle driving, and the method comprises the following steps: acquiring a dynamic parameter in a vehicle driving process and an image of a driving road of the vehicle, the dynamic parameter being used for representing an operation state of a dynamic component of the vehicle; determining a road surface type in which the vehicle is located according to the image and the dynamic parameter, wherein the road surface type comprises a flat road surface and a bumpy road surface; and controlling an anti-lock braking system of the vehicle based on the road surface type in which the vehicle is located. According to the method, the road surface type in which the vehicle is currently located can be flexibly and accurately determined based on the acquired dynamic parameter and the image of the driving road in the vehicle driving process. Furthermore, the ABS of the vehicle is controlled according to the road surface type, so that the ABS is accurately controlled, and the driving safety of the vehicle is ensured.

Description

Technical Field

[0001] This application relates to the field of vehicle driving, and more specifically, to a method and vehicle for controlling a vehicle based on road surface type in the field of vehicle driving. Background Technology

[0002] During vehicle operation, the vehicle's driving behavior may differ depending on the type of road surface (e.g., bumpy road or smooth road).

[0003] In one possible implementation, if the vehicle requires braking during operation, the anti-lock braking system (ABS) plays a crucial role. ABS can automatically adjust the vehicle's braking force during braking to prevent dangerous accidents caused by wheel lock-up.

[0004] Due to differences in road surface types, the control process of ABS also varies, and the above-mentioned ABS working process cannot be well adapted to road surface types.

[0005] In summary, how to combine ABS control with road surface type has become an urgent problem to be solved. Summary of the Invention

[0006] This application provides a method and a vehicle for controlling a vehicle based on road surface type. This method can flexibly and accurately determine the current road surface type of the vehicle during operation based on acquired power parameters and images of the road. Furthermore, it controls the vehicle's ABS according to the road surface type, achieving accurate control of the ABS and ensuring vehicle driving safety.

[0007] In a first aspect, a method for controlling a vehicle based on road surface type is provided. The method includes: acquiring power parameters of the vehicle during driving and an image of the road on which the vehicle is driving, wherein the power parameters are used to represent the operating state of the vehicle's power components; determining the road surface type on which the vehicle is located based on the image and the power parameters, wherein the road surface type includes smooth road surface and bumpy road surface; and controlling the vehicle's anti-lock braking system based on the road surface type on which the vehicle is located.

[0008] The aforementioned technical solution proposes a method for controlling a vehicle based on road surface type during operation, specifically how to control the vehicle's ABS (Anti-lock Braking System) according to the road surface type. Firstly, this application determines the road surface type by acquiring images of the road the vehicle is traveling on and the vehicle's dynamic parameters during operation. This process of determining the road surface type using two different parameters makes the determination method flexible and diverse, avoiding the monotony and inaccuracy problems associated with using only one parameter. Furthermore, adjusting the vehicle's ABS based on the road surface type ensures that the ABS operating state more closely matches the determined road surface type, achieving accurate ABS adjustment when the vehicle is traveling on different types of road surfaces.

[0009] In conjunction with the first aspect, in some possible implementations, controlling the anti-lock braking system of the vehicle based on the road surface type includes any of the following: disabling the anti-lock braking system when the road surface type is bumpy; increasing the slip ratio threshold of the wheel controlled by the anti-lock braking system to a target slip ratio threshold when the road surface type is bumpy; and increasing the deceleration threshold of the wheel controlled by the anti-lock braking system to a target deceleration threshold when the road surface type is bumpy.

[0010] Under normal circumstances, when a vehicle is driving on a smooth road, the impact of ABS on the braking distance is negligible. ABS also adds to the vehicle's stability. Therefore, in this scenario, it is acceptable to either turn ABS off or keep it on.

[0011] When a vehicle is traveling on a bumpy road, ABS can significantly increase the braking distance. This situation poses a high risk to driving. Therefore, when driving on bumpy roads, it is necessary to promptly control the vehicle's ABS to avoid this problem of increased braking distance.

[0012] Specifically, when a vehicle is traveling on a bumpy road, this application proposes the following measures to control ABS. The first is to disable the vehicle's ABS. In this case, ABS does not operate, thus avoiding the problem of increased braking distance caused by ABS. The second is to increase the ABS slip rate threshold or the ABS deceleration threshold. These two measures, by raising the ABS operating threshold, can shorten the ABS operating time, thereby reducing the possibility of increased braking distance.

[0013] In combination with the first aspect and the above implementation methods, in some possible implementation methods, determining the road surface type where the vehicle is located based on the image and the power parameters includes: determining the value of the color information of the image, the value of the color information being used to represent the brightness of the image being acquired; determining the wheel speed change rate of the vehicle based on the power parameters; and determining the road surface type based on the value of the color information and the wheel speed change rate.

[0014] The above technical solution proposes a process for determining road surface type based on images and dynamic parameters. First, the color information values ​​of the image are obtained; then, the wheel speed change rate of the vehicle is determined based on the dynamic parameters. Finally, the road surface type is determined using both the color information values ​​and the wheel speed change rate.

[0015] In combination with the first aspect and the above implementation methods, in some possible implementation methods, the power parameter includes at least one of wheel speed pulse frequency, number of teeth of half-shaft gear, and wheel rolling radius. Determining the wheel speed change rate of the vehicle based on the power parameter includes: determining the wheel edge linear velocity of the wheel at the current moment based on the wheel speed pulse frequency, the number of teeth, and the wheel rolling radius; determining the wheel speed change rate based on the wheel edge linear velocity at the current moment and the wheel edge linear velocity at the previous moment, wherein the interval between the previous moment and the current moment is a preset time.

[0016] In the above technical solution, during the process of determining the wheel speed change rate based on the power parameters, the wheel edge linear velocity at the current moment can be obtained from the wheel speed pulse frequency, the number of teeth of the half-shaft gear, and the wheel rolling radius included in the power parameters. Based on the wheel edge linear velocity at the current moment and the wheel edge linear velocity at the previous moment, the difference between the wheel edge linear velocities is determined, which is the wheel speed change rate.

[0017] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, determining the road surface type based on the value of the color information and the wheel speed change rate includes: if the value of the color information is greater than or equal to a first threshold, or less than a second threshold, determining the road surface type based on the wheel speed change rate, wherein the first threshold is greater than the second threshold; if the value of the color information is greater than or equal to the second threshold and less than the first threshold, determining a target determination method for the road surface type based on the priority of the wheel speed change rate and the priority of the image, wherein the target determination method includes a wheel speed change rate method and an image method; and determining the road surface type based on the target determination method.

[0018] In the above technical solution, after obtaining the color information values ​​of the image and the wheel speed change rate, the relationship between the color information values ​​and thresholds can be determined first to ascertain the brightness of the captured image. The first threshold can be understood as the threshold for the color information values ​​under exposed lighting conditions, and the second threshold can be understood as the threshold for the color information values ​​under relatively dim lighting conditions.

[0019] In one scenario, if the color information value is greater than or equal to the first threshold, or less than the second threshold, it indicates that the captured light is either too strong and severely overexposed, or too dark. In this case, the road surface type determined from the image will have a large error, and it is necessary to determine the road surface type based on the wheel speed change rate.

[0020] In another scenario, if the color information value falls between the second and first thresholds, it indicates that the ambient light level is appropriate. In this case, the road surface type can be determined using either the image or the wheel speed change rate. The choice between these two methods for determining the road surface type can be made by prioritizing either the image or the wheel speed change rate.

[0021] The aforementioned methods for determining road surface types can still accurately identify road surface types even when image acquisition is less than ideal, thus improving the accuracy of road surface type recognition. Under ideal image acquisition conditions, the method can flexibly select either the wheel speed change rate or the image itself to determine the road surface type, making the road surface type determination process flexible and diverse.

[0022] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, the method for determining the target determination method of the road surface type based on the priority of the wheel speed change rate and the priority of the image includes: if the priority of the wheel speed change rate is higher than the priority of the image, determining the wheel speed change rate method as the target determination method; if the priority of the wheel speed change rate is lower than the priority of the image, determining the image method as the target determination method; if the priority of the wheel speed change rate is equal to the priority of the road surface image, determining either the wheel speed change rate method or the image method as the target determination method.

[0023] The above technical solution specifically provides several scenarios for selecting the target determination method based on priority. If the image has a higher priority, this application can determine the target road surface type using the image; if the wheel speed change rate has a higher priority, this application can determine the road surface type using the wheel speed change rate; if both have equal priority, this application can choose either method to determine the road surface type. The above process ensures that the determination method with the higher priority is selected to determine the target road surface type.

[0024] In combination with the first aspect and the above implementation methods, in some possible implementation methods, when the target determination method is the wheel speed change rate method, the road surface type is determined according to the target determination method, including: if the wheel speed change rate is greater than or equal to the preset wheel speed change rate, the road surface type is determined to be the bumpy road surface; if the wheel speed change rate is less than the preset wheel speed change rate, the road surface type is determined to be the smooth road surface.

[0025] In the above technical solution, smooth road surfaces and bumpy road surfaces correspond to different wheel speed change rates. For the strategy of determining road surface type based on wheel speed change rate, a preset wheel speed change rate can be set. Using the preset wheel speed change rate as a boundary, if the current wheel speed change rate is greater than or equal to the preset wheel speed change rate, the road surface type can be determined to be a bumpy road surface; if the current wheel speed change rate is less than the preset wheel speed change rate, the road surface type can be determined to be a smooth road surface. This method of determining road surface type based on the preset wheel speed change rate and its magnitude is simple, efficient, and fast.

[0026] In combination with the first aspect and the above implementation methods, in some possible implementation methods, when the target determination method is the image method, determining the road surface type according to the target determination method includes: extracting features from the image to determine the image features; determining the road surface type as the bumpy road surface when the similarity between the image features and the first image features is greater than or equal to the first similarity; and determining the road surface type as the smooth road surface when the similarity between the image features and the second image features is greater than or equal to the second similarity.

[0027] In the above technical solution, for the strategy of determining road surface type through images, this application can pre-define a first image feature corresponding to a bumpy road surface and a first similarity corresponding to the first image feature, and a second image feature corresponding to a smooth road surface and a second similarity corresponding to the second image feature. Based on this, this application can extract image features from the image and calculate the similarity between the image feature and the first image feature, and the similarity between the image feature and the second image feature. If the similarity between the image feature and the first image feature is greater than or equal to the first similarity, it indicates that the road surface type is a bumpy road surface; conversely, if the similarity between the image feature and the second image feature is greater than or equal to the second similarity, it indicates that the road surface type is a smooth road surface.

[0028] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, before acquiring the image of the road on which the vehicle is traveling, the method further includes: acquiring the vehicle speed; determining the speed range to which the speed belongs; and acquiring the image of the road on which the vehicle is traveling, including: determining the acquisition range of the image based on the speed range; and acquiring the image corresponding to the acquisition range.

[0029] In the above technical solution, the image acquisition range varies depending on the vehicle speed during image acquisition. For example, at lower vehicle speeds, the image acquisition range can be controlled to be closer to the vehicle; at higher vehicle speeds, in order to ensure that the image recognition results can keep up with the vehicle's speed, the image acquisition range can be controlled to be farther away from the vehicle.

[0030] Therefore, during image acquisition, this application can first determine the vehicle speed range based on the vehicle speed, then determine the image acquisition range based on that speed range, and finally acquire appropriate images. The aforementioned image acquisition takes into account vehicle speed, ensuring the correlation between image recognition results and vehicle speed, indirectly improving the timeliness and accuracy of recognition.

[0031] In summary, this application proposes a method for controlling a vehicle based on road surface type during operation, specifically how to control the vehicle's ABS based on the road surface type. Firstly, this application determines the road surface type by acquiring images of the road the vehicle is traveling on and the vehicle's dynamic parameters during operation. The process of determining the road surface type using two different parameters provides flexibility and avoids the monotony and inaccuracy associated with using only one parameter. Furthermore, adjusting the vehicle's ABS based on the road surface type ensures that the ABS operating state more closely matches the determined road surface type, achieving accurate ABS adjustment when the vehicle is traveling on different road surfaces.

[0032] Under normal circumstances, when a vehicle is driving on a smooth road, the impact of ABS on the braking distance is negligible. ABS also adds to the vehicle's stability. Therefore, in this scenario, it is acceptable to either turn ABS off or keep it on.

[0033] When a vehicle is traveling on a bumpy road, ABS can significantly increase the braking distance. This situation poses a high risk to driving. Therefore, when driving on bumpy roads, it is necessary to promptly control the vehicle's ABS to avoid this problem of increased braking distance.

[0034] Specifically, when a vehicle is traveling on a bumpy road, this application proposes the following measures to control ABS. The first is to disable the vehicle's ABS. In this case, ABS does not operate, thus avoiding the problem of increased braking distance caused by ABS. The second is to increase the ABS slip rate threshold or the ABS deceleration threshold. These two measures, by raising the ABS operating threshold, can shorten the ABS operating time, thereby reducing the possibility of increased braking distance.

[0035] Specifically, a process for determining road surface type based on images and dynamic parameters is proposed. First, the color information values ​​of the image are obtained; then, the wheel speed change rate of the vehicle is determined based on the dynamic parameters. Finally, the road surface type is determined using both the color information values ​​and the wheel speed change rate.

[0036] In determining the wheel speed change rate based on power parameters, the wheel edge linear velocity at the current moment can be obtained from the wheel speed pulse frequency, the number of teeth on the half-shaft gear, and the wheel rolling radius included in the power parameters. Based on the wheel edge linear velocity at the current moment and the wheel edge linear velocity at the previous moment, the difference between the wheel edge linear velocities is determined, which is the wheel speed change rate.

[0037] After obtaining the color information values ​​of the image and the wheel speed change rate, the first step is to determine the relationship between the color information values ​​and the thresholds to ascertain the brightness of the captured image. The first threshold can be understood as the threshold for color information values ​​under normal lighting conditions, while the second threshold can be understood as the threshold for color information values ​​under relatively low lighting conditions.

[0038] In one scenario, if the color information value is greater than or equal to the first threshold, or less than the second threshold, it indicates that the captured light is either too strong and severely overexposed, or too dark. In this case, the road surface type determined from the image will have a large error, and it is necessary to determine the road surface type based on the wheel speed change rate.

[0039] In another scenario, if the color information value falls between the second and first thresholds, it indicates that the ambient light level is appropriate. In this case, the road surface type can be determined using either the image or the wheel speed change rate. The choice between these two methods for determining the road surface type can be made by prioritizing either the image or the wheel speed change rate.

[0040] The aforementioned methods for determining road surface types can still accurately identify road surface types even when image acquisition is less than ideal, thus improving the accuracy of road surface type recognition. Under ideal image acquisition conditions, the method can flexibly select either the wheel speed change rate or the image itself to determine the road surface type, making the road surface type determination process flexible and diverse.

[0041] Specifically, several scenarios are given for selecting the target determination method based on priority. If the image has a higher priority, this application can determine the target road surface type using the image; if the wheel speed change rate has a higher priority, this application can determine the road surface type using the wheel speed change rate; if both have equal priority, this application can choose either method to determine the road surface type. The above process ensures that the determination method with the higher priority is selected to determine the target road surface type.

[0042] Since smooth and bumpy road surfaces correspond to different wheel speed change rates, a strategy for determining road surface type based on wheel speed change rate can be implemented by pre-setting a preset wheel speed change rate. Using this preset rate as a boundary, if the current wheel speed change rate is greater than or equal to the preset rate, the road surface type can be determined as bumpy; if the current wheel speed change rate is less than the preset rate, the road surface type can be determined as smooth. This method of determining road surface type based on the preset wheel speed change rate and its magnitude is simple, efficient, and fast.

[0043] For the strategy of determining road surface type through images, this application can pre-define a first image feature corresponding to a bumpy road surface and a first similarity corresponding to the first image feature, and a second image feature corresponding to a smooth road surface and a second similarity corresponding to the second image feature. Based on this, this application can extract image features from the image and calculate the similarity between the image feature and the first image feature, and the similarity between the image feature and the second image feature. If the similarity between the image feature and the first image feature is greater than or equal to the first similarity, it indicates that the road surface type is a bumpy road surface; conversely, if the similarity between the image feature and the second image feature is greater than or equal to the second similarity, it indicates that the road surface type is a smooth road surface.

[0044] During image acquisition, the image acquisition range varies depending on the vehicle speed. For example, at lower speeds, the image acquisition range can be controlled to be closer to the vehicle; at higher speeds, in order to ensure that the image recognition results can keep up with the vehicle's speed, the image acquisition range can be controlled to be farther away from the vehicle.

[0045] Therefore, during image acquisition, this application can first determine the vehicle speed range based on the vehicle speed, then determine the image acquisition range based on that speed range, and finally acquire appropriate images. The aforementioned image acquisition takes into account vehicle speed, ensuring the correlation between image recognition results and vehicle speed, indirectly improving the timeliness and accuracy of recognition.

[0046] Secondly, a device for controlling a vehicle based on road surface type is provided. The device includes: an acquisition module for acquiring power parameters of the vehicle during driving and an image of the road on which the vehicle is driving, wherein the power parameters are used to represent the operating state of the vehicle's power components; a determination module for determining the road surface type on which the vehicle is located based on the image and the power parameters, wherein the road surface type includes smooth road surface and bumpy road surface; and a control module for controlling the vehicle's anti-lock braking system based on the road surface type on which the vehicle is located.

[0047] In conjunction with the second aspect, in some possible implementations, the control module is specifically used to perform any of the following: when the road surface type is the bumpy road surface, deactivating the vehicle's anti-lock braking system; when the road surface type is the bumpy road surface, increasing the slip ratio threshold of the wheel controlled by the anti-lock braking system to a target slip ratio threshold; when the road surface type is the bumpy road surface, increasing the deceleration threshold of the wheel controlled by the anti-lock braking system to a target deceleration threshold.

[0048] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is specifically used to: determine the value of the color information of the image, the value of the color information being used to represent the brightness of the image being acquired; determine the wheel speed change rate of the vehicle based on the power parameter; and determine the road surface type based on the value of the color information and the wheel speed change rate.

[0049] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the power parameter includes at least one of wheel speed pulse frequency, number of teeth of half-shaft gear, and wheel rolling radius. The determining module is further configured to: determine the wheel edge linear velocity of the wheel at the current moment based on the wheel speed pulse frequency, the number of teeth, and the wheel rolling radius; determine the wheel speed change rate based on the wheel edge linear velocity at the current moment and the wheel edge linear velocity at the previous moment, wherein the interval between the previous moment and the current moment is a preset time.

[0050] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is further configured to: determine the road surface type based on the wheel speed change rate when the value of the color information is greater than or equal to a first threshold or less than a second threshold, wherein the first threshold is greater than the second threshold; determine the target determination method for the road surface type based on the priority of the wheel speed change rate and the priority of the image when the value of the color information is greater than or equal to the second threshold and less than the first threshold, wherein the target determination method includes a wheel speed change rate method and an image method; and determine the road surface type based on the target determination method.

[0051] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is further configured to: determine the wheel speed change rate method as the target determination method when the priority of the wheel speed change rate is higher than the priority of the image; determine the image method as the target determination method when the priority of the wheel speed change rate is lower than the priority of the image; and determine either the wheel speed change rate method or the image method as the target determination method when the priority of the wheel speed change rate is equal to the priority of the road surface image.

[0052] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, when the target determination method is the wheel speed change rate method, the determining module is further used to: if the wheel speed change rate is greater than or equal to the preset wheel speed change rate, determine the road surface type as the bumpy road surface; if the wheel speed change rate is less than the preset wheel speed change rate, determine the road surface type as the smooth road surface.

[0053] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, when the target determination method is the image method, the determining module is further configured to: extract features from the image to determine the image features; determine the road surface type as the bumpy road surface when the similarity between the image features and the first image features is greater than or equal to the first similarity; and determine the road surface type as the smooth road surface when the similarity between the image features and the second image features is greater than or equal to the second similarity.

[0054] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the device further includes: a processing module, configured to acquire the vehicle speed; determine the speed range to which the vehicle speed belongs; determine the acquisition range of the image based on the speed range; and the acquisition module is specifically configured to: acquire the image corresponding to the acquisition range.

[0055] Thirdly, a vehicle is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the vehicle to perform the methods described in the first aspect or any possible implementation thereof.

[0056] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0057] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of a vehicle ABS working scenario provided in an embodiment of this application;

[0059] Figure 2 This is a schematic flowchart illustrating a method for controlling vehicles based on road surface type, as provided in an embodiment of this application.

[0060] Figure 3 This is a schematic diagram of a scenario for acquiring an image of a vehicle's driving road, provided in an embodiment of this application;

[0061] Figure 4 This is a schematic diagram of a device for controlling vehicles based on road surface type, provided in an embodiment of this application.

[0062] Figure 5 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0063] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0064] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0065] Figure 1 This is a schematic diagram of a vehicle ABS working scenario provided in an embodiment of this application.

[0066] For example, such as Figure 1 As shown, while vehicle 101 is traveling on road 102, vehicle 101 can acquire various driving information, such as power parameters and images of the current road 102. Among these:

[0067] Power parameters represent the operating status of the power components during vehicle 101's operation. These power components can be parts used to transmit power, such as wheels and drive shafts (also called half-shafts). The operating status of the power components includes, for example, wheel speed, wheel rolling radius, wheel speed pulse frequency, and the number of teeth on the drive shaft gears.

[0068] In addition to the aforementioned power parameters and images, vehicle 101 can also acquire other driving information, such as vehicle speed and engine speed.

[0069] It should be understood that the vehicle 101 is equipped with various types of sensors and electronic control units (ECUs, also known as controllers). Sensors include vehicle speed sensors, wheel speed sensors, temperature sensors, door sensors, onboard cameras, accelerator pedal position sensors, etc. ECUs include engine control modules (ECMs or engine electronic control units), hybrid control units (HCUs), battery management systems (BMSs), automatic transmission control units (TCUs), ABS, etc.

[0070] The aforementioned different sensors and ECUs can be used to acquire various driving information of vehicle 101.

[0071] Specifically, the vehicle speed sensor can collect vehicle speed; the wheel speed sensor can collect wheel speed, and vehicle 101 obtains the wheel speed pulse frequency by acquiring the pulse signals emitted by the wheel speed sensor; the on-board camera can acquire images of road 102 during the vehicle 101's movement. The data collected by different sensors can also be further sent to the corresponding ECU for data analysis and processing.

[0072] The number of teeth on the half-shaft gear and the rolling radius of the wheel can be considered as inherent parameters of the vehicle 101, and are generally stored in the corresponding ECU.

[0073] Multiple ECUs in vehicle 101 can communicate with each other to facilitate data transmission. Optionally, the ECU communication connections can include Controller Area Network (CAN) bus connection, Local Interconnect Network (LIN) bus connection, FlexRay bus connection, Media Oriented Systems Transport (MOST) bus connection, and Ethernet connection. Each connection method corresponds to a communication method, namely CAN bus communication (or CAN communication), LIN bus communication, FlexRay bus communication, MOST bus communication, and Ethernet communication. This application embodiment does not limit this. The following embodiment uses CAN communication within vehicle 101 as an example for illustration.

[0074] Vehicle 101 may experience different driving conditions when traveling on different types of road surfaces. Based on these differences, the impact of ABS on the braking distance of Vehicle 101 will vary. To ensure the smoothness and safety of Vehicle 101, Vehicle 101 can adjust its ABS settings according to the specific road surface.

[0075] Optional road surface types include smooth road surfaces and bumpy road surfaces.

[0076] It should be understood that, under normal circumstances, on smooth roads, ABS has little impact on the vehicle's braking distance and can be considered negligible. Furthermore, ABS can additionally increase the vehicle's stability. Therefore, on smooth roads, it is acceptable to either turn ABS off or keep it on.

[0077] On bumpy roads, ABS can significantly increase the braking distance of vehicle 101. This increases the risk of accidents. Therefore, when vehicle 101 is traveling on bumpy roads, it is necessary to promptly control the ABS to prevent prolonged braking and potential accidents.

[0078] Specifically, when controlling the ABS of vehicle 101 based on road surface type, the ECU can determine the current road surface type of vehicle 101 by acquiring power parameters and images. Then, different ABS control strategies are selected according to the road surface type.

[0079] Figure 2 This is a schematic flowchart illustrating a method for controlling vehicles based on road surface type, provided in an embodiment of this application. It should be understood that this method can be applied to... Figure 1 The scenario shown can be applied to any ECU in vehicle 101. This application embodiment uses the Vehicle Control Unit (VCU, also known as the vehicle controller) as an example to provide a detailed description of a method for controlling a vehicle based on road type.

[0080] For example, such as Figure 2 As shown, the method 200 includes:

[0081] 201. Acquire the power parameters and images of the road the vehicle is traveling on during the driving process. The power parameters are used to represent the operating status of the vehicle's power components.

[0082] It should be understood that ABS plays a crucial role in vehicle operation. As an anti-lock braking system, ABS automatically controls the amount of braking force when braking is required, ensuring sufficient traction between the wheels and the ground to prevent wheel lock-up.

[0083] It should also be understood that on smooth roads, ABS has little impact on the vehicle's braking distance, does not affect normal driving, and can even increase vehicle stability, thus contributing to safe driving. However, on bumpy roads, ABS can significantly increase the braking distance, which poses a higher risk to driving.

[0084] Based on the impact of ABS on vehicle braking distance when the vehicle is traveling on different types of road surfaces, the embodiments of this application can intelligently control ABS according to the different types of road surfaces the vehicle is currently traveling on.

[0085] In one possible implementation, when controlling ABS, the road surface type of the road where the vehicle is located needs to be determined first. Specifically, in determining the road surface type, the VCU needs to acquire the vehicle's dynamic parameters and images of the road surface during its driving process. Among these:

[0086] Power parameters are used to indicate the driving status of the power components in a vehicle. Optionally, power components include drive shafts (half-shafts), wheels, and other components related to power transmission. Correspondingly, power parameters may include wheel speed, wheel rolling radius, number of teeth on the half-shaft gear, wheel speed pulse frequency, etc.

[0087] The rolling radius of a wheel refers to the actual radius of its contact with the ground when it rolls. The commonly used wheel radius, however, refers to the distance from the inner circumference of the wheel to its axle. The relationship between the two is as follows: when the vehicle's trajectory is straight, the rolling radius equals the wheel radius; when the vehicle's trajectory is turning, the rolling radius is smaller than the wheel radius because the outer path of the wheel is longer than the inner path, requiring it to roll a greater distance to complete the turn.

[0088] Generally, the rolling radius of a wheel is related to the tire specifications and model, which can be considered an inherent parameter that can be obtained directly; or it can be approximated by calculation using relevant parameters.

[0089] For example, the rolling radius of a wheel can be approximately calculated using the following formula (1):

[0090] r = S / 2πn Formula (1)

[0091] In formula (1):

[0092] n: the number of revolutions of the wheel;

[0093] S: The distance the wheel rolls when the wheel rotates n times, in mm;

[0094] r: Wheel rolling radius, unit: mm.

[0095] Optionally, the ABS can calculate the wheel rolling radius r by obtaining the aforementioned parameters S and n. The wheel rolling radius r is then transmitted to the VCU via the CAN bus in the form of a CAN signal. Alternatively, the ABS can transmit the parameters S and n to the VCU via the CAN bus in the form of a CAN signal. The VCU then calculates the wheel rolling radius r. This embodiment does not limit the specific ECU used to calculate the wheel rolling radius r.

[0096] Optionally, the VCU can acquire data either actively or passively. Active acquisition refers to other ECUs proactively sending relevant data to the VCU after acquiring it; passive acquisition refers to the VCU sending a data acquisition request to other ECUs when it has processing needs. Other ECUs respond to this data acquisition request by sending the corresponding data to the VCU. This application embodiment does not specifically limit the method by which the VCU acquires data.

[0097] The number of teeth on the half-shaft gear, also known as the drive shaft, is a solid shaft connecting the differential to the drive wheels. The function of the half-shaft is to transmit power from the differential to the left and right drive wheels. As an important component of the vehicle's powertrain, the number of teeth on the half-shaft gear is an inherent property of gears and is usually stored in the powertrain-related ECU (such as ECM and TCU). The ECM and TCU can send the number of teeth on the half-shaft gear to the VCU via the CAN bus using CAN signals.

[0098] Regarding the wheel speed pulse frequency, the ABS can obtain the number of pulse signals sent by the four wheel speed sensors within the cycle, calculate the wheel speed pulse frequency of the four wheels, and send it to the VCU via the CAN bus in the form of a CAN signal.

[0099] Image data can be captured using onboard cameras in the vehicle. These cameras can be directly connected to the VCU (Vehicle Control Unit) and send the captured images to the VCU.

[0100] It should be understood that the image acquisition range for driving roads may vary depending on vehicle speed. For example, at higher speeds, the acquisition range can be controlled to cover areas farther away from the vehicle compared to slower speeds. This process avoids vehicle safety issues that may arise when the vehicle has already reached the road ahead at high speeds, but the road surface type is still unknown.

[0101] In one possible implementation, before acquiring the image, the vehicle speed needs to be obtained first, and then the acquisition range is determined based on the vehicle speed, specifically including:

[0102] Get vehicle speed;

[0103] Determine the speed range to which the vehicle speed belongs;

[0104] Determine the image acquisition range corresponding to the vehicle speed range;

[0105] In addition, acquiring the image corresponding to the acquisition range, including:

[0106] Acquire the image corresponding to the acquisition range.

[0107] Figure 3 This is a schematic diagram of a scenario for acquiring an image of a vehicle's driving road, provided in an embodiment of this application.

[0108] For example, such as Figure 3 As shown in the figure, the shaded area represents the image acquisition range determined by the vehicle speed. When vehicle 101 is traveling straight, the acquisition range is rectangular. When vehicle 101 is turning, the acquisition range is trapezoidal. The following embodiment of this application uses the scenario of vehicle 101 traveling straight as an example to provide a detailed description of the acquisition range.

[0109] The data acquisition area has a start boundary and an end boundary. The start boundary includes the left and top boundaries of the rectangle. The end boundary includes the right and bottom boundaries of the rectangle. The area formed by these four boundaries is the data acquisition area.

[0110] like Figure 3 As shown, the position of the driver in vehicle 101 is used as a reference. The boundary of the rectangle closer to vehicle 101 is called the "left boundary", and the boundary farther from vehicle 101 is called the "right boundary". The center line of the front or rear axle of vehicle 101 is used as the dividing line, and the boundary to the left of the center line is called the "upper boundary", and the boundary to the right of the center line is called the "lower boundary".

[0111] For the left boundary, the distance between the vehicle-mounted camera 103 and the left boundary of the acquisition range is denoted as "X". min ".

[0112] The position of the left boundary will vary depending on the vehicle speed. Specifically, when the vehicle speed is 0-120 km / h, X min The range is 5-20m.

[0113] In one possible implementation, embodiments of this application can adjust X according to different vehicle speed ranges. min Further division is needed.

[0114] Table 1 shows a vehicle speed range and X provided in the embodiments of this application. min A table illustrating the correspondence.

[0115] Table 1

[0116]

[0117]

[0118] For example, as shown in Table 1, when vehicle 101 is in motion, if the vehicle speed is within different speed ranges, X min The value of will also change accordingly.

[0119] For the right boundary, the distance between the vehicle-mounted camera 103 and the right boundary is denoted as "X". max The left and right boundaries have the following relationship: X max =X min +10.

[0120] For the upper boundary, such as Figure 3 As shown, the length of the shorter side of the rectangle is 3.5m. Divide the shorter side into two equal parts. The vertical distance between the geometric center of the rectangle and its upper boundary is denoted as "Y". max With the geometric center of the rectangle as the origin, the direction of the upper boundary is denoted as the positive direction, and the direction of the lower boundary is denoted as the negative direction, then Y... max =1.75.

[0121] Correspondingly, the vertical distance between the geometric center of the rectangle and its lower boundary is denoted as "Y". min Y min = -1.75.

[0122] Based on the above four boundaries, the collection range can be determined.

[0123] In another scenario, if vehicle 101 is turning, the left and right boundaries remain unchanged. However, due to the change in the wheel angle, the upper and lower boundaries will change.

[0124] Specifically, the upper boundary can be represented as: Y max =1.75+50δ / L; the lower boundary can be represented as: Y min = -1.75 + 50δ / L. Where δ is the front wheel steering angle (in this embodiment, it is assumed that the steering angles of the left and right front wheels of vehicle 101 are the same); L is the wheelbase of vehicle 101.

[0125] Based on the above process, the embodiments of this application can provide a solution for flexibly determining the collection range according to vehicle speed.

[0126] In the above technical solution, the image acquisition range varies depending on the vehicle speed during image acquisition. For example, at lower vehicle speeds, the image acquisition range can be controlled to be closer to the vehicle; at higher vehicle speeds, in order to ensure that the image recognition results can keep up with the vehicle's speed, the image acquisition range can be controlled to be farther away from the vehicle.

[0127] Therefore, during image acquisition, this application can first determine the vehicle speed range based on the vehicle speed, then determine the image acquisition range based on that speed range, and finally acquire appropriate images. The aforementioned image acquisition takes into account vehicle speed, ensuring the correlation between image recognition results and vehicle speed, indirectly improving the timeliness and accuracy of recognition.

[0128] 202. Based on the image and dynamic parameters, determine the road surface type where the vehicle is located. The road surface type includes smooth road surface and bumpy road surface.

[0129] After acquiring images and dynamic parameters, the VCU can determine the type of road surface the vehicle is currently on using these two parameters.

[0130] One possible implementation involves determining the road surface type the vehicle is on based on images and dynamic parameters, specifically including:

[0131] Determine the numerical values ​​of the color information of the image, which are used to represent the brightness of the image being captured;

[0132] Determine the rate of change of the vehicle's wheel speed based on the power parameters;

[0133] The road surface type is determined based on the numerical value of the color information and the rate of change of wheel speed.

[0134] Optionally, the image's color information includes the image's three primary colors (Red, Green, Blue, RGB), YUV information, and (hue, saturation, value, HSV) information. Correspondingly, the numerical values ​​of the color information include any one of the image's RGB values, YUV values, and HSV values.

[0135] Among them, the numerical value of color information can be used to represent the brightness of the image being captured, that is, the brightness of the light source used to capture the image, such as whether the image is overexposed or whether the light is too dark.

[0136] In one possible implementation, when determining the wheel speed change rate of a vehicle using power parameters, the power parameters may include at least one of the wheel speed pulse frequency, the number of teeth on the half-shaft gear, and the wheel rolling radius obtained in step 201. The process of determining the wheel speed change rate includes:

[0137] The wheel edge linear velocity at the current moment is determined based on the wheel speed pulse frequency, the number of teeth, and the wheel rolling radius.

[0138] The wheel speed change rate is determined based on the wheel edge linear velocity at the current moment and the wheel edge linear velocity at the previous moment, with a preset time interval between the previous moment and the current moment.

[0139] For example, after obtaining the power parameters, the wheel speed change rate can be calculated using the following formulas (2)-(7) in the embodiments of this application.

[0140]

[0141] e lf =V lf (k)-V lf (kT d ) Formula (3)

[0142] e rf =V rf (k)-V rf (kT d ) Formula (4)

[0143] e lr =Vlr(k)-V lr (kT d ) Formula (5)

[0144] e rr =V rr (k)-V rr (kT d ) Formula (6)

[0145]

[0146] In formulas (2)-(7):

[0147] V w The linear velocity of the wheel rim at the current moment, in m / s, specifically including the linear velocity V of the left front wheel (lfw, abbreviated as "lf"). lf The linear velocity V of the left rear wheel (lrw, abbreviated as "lr") at its wheel edge. lr The linear velocity V of the right front wheel (RFW, abbreviated as "RF") at its wheel edge. rf The linear velocity V of the right rear wheel (rrw, abbreviated as "rr") rr ;

[0148] f: Wheel speed pulse frequency, unit: Hertz (Hz). Specifically includes the wheel speed pulse frequency of the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel;

[0149] n: Number of teeth on the half-shaft gear;

[0150] r: Wheel rolling radius, unit: mm;

[0151] e lf : Rate of change of wheel speed of the left front wheel, unit: m / s;

[0152] e rf : Rate of change of wheel speed of the right front wheel, unit: m / s;

[0153] e lr : Rate of change of wheel speed of the left rear wheel, unit: m / s;

[0154] e rr : Rate of change of wheel speed of the right rear wheel, unit: m / s;

[0155] k: Current time, in seconds;

[0156] T d Sampling period, also known as preset duration, in seconds;

[0157] E: Total wheel speed change rate, unit: m / s.

[0158] The wheel speed change rate (i.e., the total wheel speed change rate) of the wheel in the embodiment of this application can be calculated using the above formulas (2)-(7).

[0159] Furthermore, the road surface type can be determined based on the numerical value of color information and the rate of change of wheel speed.

[0160] The above technical solution proposes a process for determining road surface type based on images and dynamic parameters. First, the color information values ​​of the image are obtained; then, the wheel speed change rate of the vehicle is determined based on the dynamic parameters. Finally, the road surface type is determined using both the color information values ​​and the wheel speed change rate.

[0161] Specifically, in determining the wheel speed change rate based on power parameters, the wheel edge linear velocity at the current moment can be obtained from the wheel speed pulse frequency, the number of teeth on the half-shaft gear, and the wheel rolling radius included in the power parameters. Based on the wheel edge linear velocity at the current moment and the wheel edge linear velocity at the previous moment, the difference between the wheel edge linear velocities is determined, which is the wheel speed change rate.

[0162] After determining the color information and wheel speed change rate, the VCU can combine the color information and wheel speed change rate to obtain the current road surface type.

[0163] One possible implementation method, when determining the road surface type based on color information and wheel speed change rate, specifically includes:

[0164] If the value of the color information is greater than or equal to the first threshold, or less than the second threshold, the road surface type is determined based on the wheel speed change rate, where the first threshold is greater than the second threshold.

[0165] When the value of color information is greater than or equal to the second threshold and less than the first threshold, the target determination method for road surface type is determined according to the priority of wheel speed change rate and image priority. The target determination method includes wheel speed change rate method and image method.

[0166] The road surface type is determined based on the target determination method.

[0167] It should be understood that the lighting conditions for images captured by vehicle-mounted cameras may vary due to weather factors. For example, the lighting is stronger in sunny weather and dimmer in overcast weather. Image recognition errors are larger in both excessively bright and excessively dark conditions. Therefore, in this embodiment, when determining the road surface type based on the image and wheel speed change rate, the brightness of the image can be assessed first based on the numerical values ​​of its color information.

[0168] For example, taking RGB color information as an example, the numerical value of color information is the RGB value, denoted as (a, b, c). Here, a represents the R value, b represents the G value, and c represents the B value. The values ​​of a, b, and c are between 0 and 255.

[0169] This application embodiment can preset threshold values ​​for color information, including a first threshold and a second threshold. The first threshold can be understood as an exposure threshold, i.e., the critical value at which the collected light is exposed; the second threshold can be understood as an insufficient light threshold, i.e., the critical value at which the collected light is too dim.

[0170] Optionally, the first threshold and the second threshold can be thresholds for three values ​​a, b, and c; or they can be thresholds for the average of the three values ​​a, b, and c. This application does not limit this.

[0171] In one scenario, if the RGB values ​​of an image are greater than or equal to a first threshold, it indicates that the image was overexposed; conversely, if the RGB values ​​are less than a second threshold, it indicates that the image was underexposed. In both of these cases, identifying the road surface type based solely on the image has a significant error, necessitating the determination of the road surface type based on the wheel speed change rate.

[0172] In another scenario, when the RGB values ​​of the image are between the second and first thresholds, it indicates that the image acquisition lighting is normal. In this case, both the image and the wheel speed change rate can be used to determine the road surface type. In this embodiment, the target determination method for the road surface type can be selected based on the priority of the wheel speed change rate and the priority of the image. Then, the road surface type is determined based on the target determination method.

[0173] In the above technical solution, after obtaining the color information values ​​of the image and the wheel speed change rate, the relationship between the color information values ​​and thresholds can be determined first to ascertain the brightness of the captured image. The first threshold can be understood as the threshold for the color information values ​​under exposed lighting conditions, and the second threshold can be understood as the threshold for the color information values ​​under relatively dim lighting conditions.

[0174] In one scenario, if the color information value is greater than or equal to the first threshold, or less than the second threshold, it indicates that the captured light is either too strong and severely overexposed, or too dark. In this case, the road surface type determined from the image will have a large error, and it is necessary to determine the road surface type based on the wheel speed change rate.

[0175] In another scenario, if the color information value falls between the second and first thresholds, it indicates that the ambient light level is appropriate. In this case, the road surface type can be determined using either the image or the wheel speed change rate. The choice between these two methods for determining the road surface type can be made by prioritizing either the image or the wheel speed change rate.

[0176] The aforementioned methods for determining road surface types can still accurately identify road surface types even when image acquisition is less than ideal, thus improving the accuracy of road surface type recognition. Under ideal image acquisition conditions, the method can flexibly select either the wheel speed change rate or the image itself to determine the road surface type, making the road surface type determination process flexible and diverse.

[0177] Specifically, when the lighting conditions for image acquisition are normal, the target determination method is determined based on the priority of the image and the priority of the wheel speed change rate, including:

[0178] If the priority of wheel speed change rate is higher than that of image, the wheel speed change rate method will be determined as the target determination method.

[0179] If the priority of the wheel speed change rate is lower than that of the image, the image method will be determined as the target determination method.

[0180] When the priority of the wheel speed change rate is equal to that of the road surface image, either the wheel speed change rate method or the image method is determined as the target determination method.

[0181] For example, the image priority can be pre-stored in the vehicle-mounted camera, and the wheel speed change rate priority can be pre-stored in the ABS. The VCU can obtain the image priority from the vehicle-mounted camera and the wheel speed change rate priority from the ABS via the CAN bus. Alternatively, both the image priority and the wheel speed change rate priority can be pre-stored in the VCU, which is not limited in this embodiment.

[0182] In this embodiment of the application, it is assumed that the highest priority is 10.

[0183] In one scenario, if the image priority is 5 and the wheel speed change rate priority is 7, it can be seen that the wheel speed change rate has a higher priority than the image priority. In this case, the VCU can determine the wheel speed change rate method as the target determination method.

[0184] In another scenario, if the image priority is 7 and the wheel speed change rate priority is 5, it can be seen that the image priority is higher than the wheel speed change rate priority. In this case, the VCU can determine the image method as the target determination method.

[0185] In another scenario, if both the image priority and the wheel speed change rate priority are 5, the two priorities are identical. The VCU can arbitrarily select either determination method as the target determination method. Alternatively, the VCU, through the multimedia controller, controls the multimedia host to display both determination methods on the display area for the driver to choose from. Or, the VCU, through the multimedia controller, controls the multimedia host's audio playback device to announce the two determination methods in the form of voice information for the driver to choose from.

[0186] The above technical solution specifically provides several scenarios for selecting the target determination method based on priority. If the image has a higher priority, this application can determine the target road surface type using the image; if the wheel speed change rate has a higher priority, this application can determine the road surface type using the wheel speed change rate; if both have equal priority, this application can choose either method to determine the road surface type. The above process ensures that the determination method with the higher priority is selected to determine the target road surface type.

[0187] After determining the target determination method through any of the above methods, the road surface type can be further determined based on the target determination method.

[0188] In one possible implementation, when the target determination method is based on the wheel speed change rate, the road surface type is determined according to the target determination method, specifically including:

[0189] If the wheel speed change rate is greater than or equal to the preset wheel speed change rate, the road surface type is determined to be a bumpy road surface.

[0190] If the wheel speed change rate is less than the preset wheel speed change rate, the road surface type is determined to be a smooth road surface.

[0191] It should be understood that the rate of change of wheel speed reflects the fluctuation of wheel speed. On a smooth road surface, the wheel speed is relatively stable and does not change much. Therefore, the rate of change of wheel speed is relatively small on a smooth road surface. Conversely, on a bumpy road surface, due to the unevenness of the road, the wheel speed is prone to change. Therefore, the rate of change of wheel speed is relatively large on a bumpy road surface.

[0192] Based on the difference in wheel speed change rate when a vehicle is driving on a bumpy road and a smooth road, this embodiment of the application can pre-set a preset wheel speed change rate (denoted as Ea) and store it in the VCU. Using Ea as a dividing line, if E is greater than or equal to Ea, the road type is a bumpy road; if E is less than Ea, the road type is a smooth road. Optionally, the preset wheel speed change rate Ea = 1, or it can be adjusted according to the actual driving scenario.

[0193] For example, when the total wheel speed change rate E = 3 > Ea = 1 calculated by formulas (2)-(7), the road surface type can be determined to be a bumpy road surface.

[0194] Another example is that when the total wheel speed change rate E = 0.5 < Ea = 1 calculated by formulas (2)-(7), the road surface type can be determined to be a smooth road surface.

[0195] In the above technical solution, smooth road surfaces and bumpy road surfaces correspond to different wheel speed change rates. For the strategy of determining road surface type based on wheel speed change rate, a preset wheel speed change rate can be set. Using the preset wheel speed change rate as a boundary, if the current wheel speed change rate is greater than or equal to the preset wheel speed change rate, the road surface type can be determined to be a bumpy road surface; if the current wheel speed change rate is less than the preset wheel speed change rate, the road surface type can be determined to be a smooth road surface. This method of determining road surface type based on the preset wheel speed change rate and its magnitude is simple, efficient, and fast.

[0196] In another possible implementation, when the target determination method is image-based, the road surface type is determined according to the target determination method, specifically including:

[0197] Feature extraction is performed on the image to determine its image features;

[0198] If the similarity between the image features and the first image features is greater than or equal to the first similarity, the road surface type is determined to be a bumpy road surface.

[0199] If the similarity between the image feature and the second image feature is greater than or equal to the second similarity, the road surface type is determined to be a smooth road surface.

[0200] Optionally, image features include at least one of color features, texture features, shape features, and spatial relationship features. Color features may specifically include color histograms, color sets, color moments, color aggregation vectors, etc.

[0201] Optionally, image feature extraction algorithms include Scale Invariant Feature Transform (SIFT), Histograms of Oriented Gradients (HOG), Oriented Fast and Rotated BRIEF (ORB), Haar algorithm, and deep learning algorithms, such as the Visual Geometry Group (VGG) algorithm and Deep Residual Network (ResNet) algorithm. This application does not limit these algorithms.

[0202] It should be understood that the first image features corresponding to a bumpy road surface and the second image features corresponding to a smooth road surface are different. For example, the color features in the first image features can be colored (e.g., cobblestone roads are mostly colored), and the texture features can include the texture features of gravel roads, Belgian roads, and washboard roads, such as uneven stones. The color features in the second image features can be black (the color of asphalt roads), gray (the color of cement roads), etc., and the texture features can include smooth, fine, and uniform granules, etc.

[0203] In this embodiment, the aforementioned first image features and second image features can be used, along with a first similarity corresponding to the first image features and a second similarity corresponding to the second image features, and stored in the VCU. Optionally, the first similarity and the second similarity can be the same or different, and can be adjusted according to actual needs.

[0204] Based on the differences between the first image feature and the second image feature, after obtaining the image features, the VCU can calculate the similarity between the image features and the first image feature, as well as the similarity between the image features and the second image feature.

[0205] Optionally, image feature similarity calculation methods include the following categories: cosine similarity, hash algorithms, histograms, structural similarity index measure (SSIM), mutual information, Euclidean distance, Manhattan distance, Chebyshev distance, Hamming distance, cosine distance, Mahalanobis distance, etc. This application does not limit the method used to calculate image feature similarity.

[0206] For example, suppose the first similarity and the second similarity are both 90%. If the VCU calculates that the similarity between the image feature and the first image feature is 94% and the similarity between the image feature and the second image feature is 20%, then the VCU determines that the current road surface type is a bumpy road surface.

[0207] If the similarity between the image features and the first image features is 20%, and the similarity between the image features and the second image features is 96%, then the VCU determines that the current road surface type is a smooth road surface.

[0208] In the above technical solution, for the strategy of determining road surface type through images, this application can pre-define a first image feature corresponding to a bumpy road surface and a first similarity corresponding to the first image feature, and a second image feature corresponding to a smooth road surface and a second similarity corresponding to the second image feature. Based on this, this application can extract image features from the image and calculate the similarity between the image feature and the first image feature, and the similarity between the image feature and the second image feature. If the similarity between the image feature and the first image feature is greater than or equal to the first similarity, it indicates that the road surface type is a bumpy road surface; conversely, if the similarity between the image feature and the second image feature is greater than or equal to the second similarity, it indicates that the road surface type is a smooth road surface.

[0209] 203. Based on the road surface type where the vehicle is located, control the vehicle's anti-lock braking system.

[0210] Furthermore, after determining the road surface type in step 202, the VCU can select an appropriate ABS control strategy based on the road surface type.

[0211] In one possible implementation, when the road surface type is smooth, the vehicle's ABS is controlled based on the road surface type, including:

[0212] Keep the ABS on.

[0213] On smooth roads, ABS has almost no impact on the vehicle's braking distance. Therefore, on smooth roads, it is fine to keep ABS on or off.

[0214] In another possible implementation, when the road surface type is bumpy, the vehicle's ABS is controlled based on the road surface type, including any of the following:

[0215] Turn off the vehicle's anti-lock braking system;

[0216] Increase the slip ratio threshold of the wheels controlled by the anti-lock braking system to the target slip ratio threshold;

[0217] Increase the deceleration threshold of the wheels controlled by the anti-lock braking system to the target deceleration threshold.

[0218] On bumpy roads, ABS can significantly increase the vehicle's braking distance. Therefore, when driving on bumpy roads, it is necessary to promptly activate the vehicle's ABS.

[0219] ABS primarily determines whether a vehicle is instable (e.g., skidding) by monitoring wheel slip ratio or wheel deceleration. For example, if the wheel slip ratio exceeds a slip ratio threshold or the wheel deceleration exceeds a deceleration threshold, ABS determines that the wheels have locked up, and at this point, ABS begins to control the vehicle's braking force.

[0220] Therefore, to avoid the increased braking distance caused by ABS activation when driving on bumpy roads, ABS can be controlled in the following two ways: 1. Turn off ABS; 2. Increase the slip ratio threshold in ABS (e.g., increase it to a preset target slip ratio threshold) or increase the deceleration threshold (e.g., increase it to a preset target deceleration threshold). Increasing the slip ratio threshold and deceleration threshold raises the threshold for wheel instability. Correspondingly, the threshold for ABS activation is raised, thus shortening the ABS activation time during vehicle braking.

[0221] In the aforementioned technical solution, when a vehicle is traveling on a bumpy road, ABS can significantly increase the braking distance. This situation poses a high risk to driving. Therefore, when driving on bumpy roads, it is necessary to promptly control the vehicle's ABS to avoid the problem of increased braking distance during braking.

[0222] Specifically, when a vehicle is traveling on a bumpy road, this application proposes the following measures to control ABS. The first is to disable the vehicle's ABS. In this case, ABS does not operate, thus avoiding the problem of increased braking distance caused by ABS. The second is to increase the ABS slip rate threshold or the ABS deceleration threshold. These two measures, by raising the ABS operating threshold, can shorten the ABS operating time, thereby reducing the possibility of increased braking distance.

[0223] In summary, this application proposes a method for controlling a vehicle based on road surface type during operation, specifically how to control the vehicle's ABS based on the road surface type. Firstly, this application determines the road surface type by acquiring images of the road the vehicle is traveling on and the vehicle's dynamic parameters during operation. The process of determining the road surface type using two different parameters provides flexibility and avoids the monotony and inaccuracy associated with using only one parameter. Furthermore, adjusting the vehicle's ABS based on the road surface type ensures that the ABS operating state more closely matches the determined road surface type, achieving accurate ABS adjustment when the vehicle is traveling on different types of road surfaces.

[0224] Under normal circumstances, when a vehicle is driving on a smooth road, the impact of ABS on the braking distance is negligible. ABS also adds to the vehicle's stability. Therefore, in this scenario, it is acceptable to either turn ABS off or keep it on.

[0225] When a vehicle is traveling on a bumpy road, ABS can significantly increase the braking distance. This situation poses a high risk to driving. Therefore, when driving on bumpy roads, it is necessary to promptly control the vehicle's ABS to avoid this problem of increased braking distance.

[0226] Specifically, when a vehicle is traveling on a bumpy road, this application proposes the following measures to control ABS. The first is to disable the vehicle's ABS. In this case, ABS does not operate, thus avoiding the problem of increased braking distance caused by ABS. The second is to increase the ABS slip rate threshold or the ABS deceleration threshold. These two measures, by raising the ABS operating threshold, can shorten the ABS operating time, thereby reducing the possibility of increased braking distance.

[0227] Specifically, a process for determining road surface type based on images and dynamic parameters is proposed. First, the color information values ​​of the image are obtained; then, the wheel speed change rate of the vehicle is determined based on the dynamic parameters. Finally, the road surface type is determined using both the color information values ​​and the wheel speed change rate.

[0228] In determining the wheel speed change rate based on power parameters, the wheel edge linear velocity at the current moment can be obtained from the wheel speed pulse frequency, the number of teeth on the half-shaft gear, and the wheel rolling radius included in the power parameters. Based on the wheel edge linear velocity at the current moment and the wheel edge linear velocity at the previous moment, the difference between the wheel edge linear velocities is determined, which is the wheel speed change rate.

[0229] After obtaining the color information values ​​of the image and the wheel speed change rate, the first step is to determine the relationship between the color information values ​​and the thresholds to ascertain the brightness of the captured image. The first threshold can be understood as the threshold for color information values ​​under normal lighting conditions, while the second threshold can be understood as the threshold for color information values ​​under relatively low lighting conditions.

[0230] In one scenario, if the color information value is greater than or equal to the first threshold, or less than the second threshold, it indicates that the captured light is either too strong and severely overexposed, or too dark. In this case, the road surface type determined from the image will have a large error, and it is necessary to determine the road surface type based on the wheel speed change rate.

[0231] In another scenario, if the color information value falls between the second and first thresholds, it indicates that the ambient light level is appropriate. In this case, the road surface type can be determined using either the image or the wheel speed change rate. The choice between these two methods for determining the road surface type can be made by prioritizing either the image or the wheel speed change rate.

[0232] The aforementioned methods for determining road surface types can still accurately identify road surface types even when image acquisition is less than ideal, thus improving the accuracy of road surface type recognition. Under ideal image acquisition conditions, the method can flexibly select either the wheel speed change rate or the image itself to determine the road surface type, making the road surface type determination process flexible and diverse.

[0233] Specifically, several scenarios are given for selecting the target determination method based on priority. If the image has a higher priority, this application can determine the target road surface type using the image; if the wheel speed change rate has a higher priority, this application can determine the road surface type using the wheel speed change rate; if both have equal priority, this application can choose either method to determine the road surface type. The above process ensures that the determination method with the higher priority is selected to determine the target road surface type.

[0234] Since smooth and bumpy road surfaces correspond to different wheel speed change rates, a strategy for determining road surface type based on wheel speed change rate can be implemented by pre-setting a preset wheel speed change rate. Using this preset rate as a boundary, if the current wheel speed change rate is greater than or equal to the preset rate, the road surface type can be determined as bumpy; if the current wheel speed change rate is less than the preset rate, the road surface type can be determined as smooth. This method of determining road surface type based on the preset wheel speed change rate and its magnitude is simple, efficient, and fast.

[0235] For the strategy of determining road surface type through images, this application can pre-define a first image feature corresponding to a bumpy road surface and a first similarity corresponding to the first image feature, and a second image feature corresponding to a smooth road surface and a second similarity corresponding to the second image feature. Based on this, this application can extract image features from the image and calculate the similarity between the image feature and the first image feature, and the similarity between the image feature and the second image feature. If the similarity between the image feature and the first image feature is greater than or equal to the first similarity, it indicates that the road surface type is a bumpy road surface; conversely, if the similarity between the image feature and the second image feature is greater than or equal to the second similarity, it indicates that the road surface type is a smooth road surface.

[0236] During image acquisition, the image acquisition range varies depending on the vehicle speed. For example, at lower speeds, the image acquisition range can be controlled to be closer to the vehicle; at higher speeds, in order to ensure that the image recognition results can keep up with the vehicle's speed, the image acquisition range can be controlled to be farther away from the vehicle.

[0237] Therefore, during image acquisition, this application can first determine the vehicle speed range based on the vehicle speed, then determine the image acquisition range based on that speed range, and finally acquire appropriate images. The aforementioned image acquisition takes into account vehicle speed, ensuring the correlation between image recognition results and vehicle speed, indirectly improving the timeliness and accuracy of recognition.

[0238] Figure 4 This is a schematic diagram of a device for controlling vehicles based on road surface type, provided in an embodiment of this application.

[0239] For example, such as Figure 4 As shown, the device 400 includes:

[0240] The acquisition module 401 is used to acquire the power parameters of the vehicle during driving and the image of the road on which the vehicle is driving. The power parameters are used to represent the operating status of the power components of the vehicle.

[0241] The determination module 402 is used to determine the road surface type where the vehicle is located based on the image and the power parameters. The road surface type includes smooth road surface and bumpy road surface.

[0242] The control module 403 is used to control the anti-lock braking system of the vehicle based on the road surface type on which the vehicle is located.

[0243] In one possible implementation, the control module 403 is specifically configured to perform any of the following: when the road surface type is the bumpy road surface, deactivate the anti-lock braking system of the vehicle; when the road surface type is the bumpy road surface, increase the slip ratio threshold of the wheel controlled by the anti-lock braking system to a target slip ratio threshold; when the road surface type is the bumpy road surface, increase the deceleration threshold of the wheel controlled by the anti-lock braking system to a target deceleration threshold.

[0244] In one possible implementation, the determining module 402 is specifically used to: determine the value of the color information of the image, the value of the color information being used to represent the brightness of the image being acquired; determine the wheel speed change rate of the vehicle based on the power parameters; and determine the road surface type based on the value of the color information and the wheel speed change rate.

[0245] In one possible implementation, the power parameters include at least one of wheel speed pulse frequency, number of teeth of half-shaft gear, and wheel rolling radius. The determining module 402 is further configured to: determine the wheel edge linear velocity of the wheel at the current moment based on the wheel speed pulse frequency, the number of teeth, and the wheel rolling radius; and determine the wheel speed change rate based on the wheel edge linear velocity at the current moment and the wheel edge linear velocity at the previous moment, wherein the interval between the previous moment and the current moment is a preset time.

[0246] In one possible implementation, the determining module 402 is further configured to: determine the road surface type based on the wheel speed change rate when the value of the color information is greater than or equal to a first threshold or less than a second threshold, wherein the first threshold is greater than the second threshold; determine a target determination method for the road surface type based on the priority of the wheel speed change rate and the priority of the image when the value of the color information is greater than or equal to the second threshold and less than the first threshold, wherein the target determination method includes a wheel speed change rate method and an image method; and determine the road surface type based on the target determination method.

[0247] In one possible implementation, the determining module 402 is further configured to: determine the wheel speed change rate method as the target determination method when the priority of the wheel speed change rate is higher than the priority of the image; determine the image method as the target determination method when the priority of the wheel speed change rate is lower than the priority of the image; and determine either the wheel speed change rate method or the image method as the target determination method when the priority of the wheel speed change rate is equal to the priority of the road surface image.

[0248] In one possible implementation, when the target determination method is the wheel speed change rate method, the determination module 402 is further configured to: if the wheel speed change rate is greater than or equal to the preset wheel speed change rate, determine the road surface type as the bumpy road surface; if the wheel speed change rate is less than the preset wheel speed change rate, determine the road surface type as the smooth road surface.

[0249] In one possible implementation, when the target determination method is the image method, the determination module 402 is further configured to: extract features from the image to determine the image features; determine the road surface type as the bumpy road surface when the similarity between the image features and the first image features is greater than or equal to the first similarity; and determine the road surface type as the smooth road surface when the similarity between the image features and the second image features is greater than or equal to the second similarity.

[0250] Optionally, the device further includes: a processing module, configured to acquire the vehicle speed; determine the speed range to which the vehicle speed belongs; determine the acquisition range of the image based on the speed range; and the acquisition module 401 is specifically configured to: acquire the image corresponding to the acquisition range.

[0251] Figure 5 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0252] For example, such as Figure 5 As shown, the vehicle 101 includes a memory 501 and a processor 502. The memory 501 stores executable program code 5011, and the processor 502 is used to call and execute the executable program code 5011 to perform a method for controlling the vehicle based on road type.

[0253] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform a method for controlling vehicles based on road type provided in embodiments of this application.

[0254] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0255] When the functional modules are divided according to their respective functions, the device may also include an acquisition module, a determination module, and a control module. It should be noted that all relevant content regarding the steps involved in the above method embodiments can be referenced to the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0256] It should be understood that the device provided in this embodiment is used to execute the above-described method for controlling vehicles based on road surface type, and therefore can achieve the same effect as the above-described implementation method.

[0257] When using an integrated unit, the device may include a processing module and a storage module. When the device is applied to a vehicle, the processing module can be used to control and manage the vehicle's movements. The storage module can be used to support the vehicle in executing program code, etc.

[0258] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits as disclosed in this application. The processor may also be a combination of computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and microprocessors, etc., and the storage module may be a memory.

[0259] In addition, the device provided in the embodiments of this application may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute the method for controlling a vehicle based on road type provided in the above embodiments.

[0260] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement the method for controlling vehicles based on road type provided in the above embodiment.

[0261] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the method for controlling vehicles based on road type provided in the above embodiment.

[0262] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0263] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0264] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0265] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling vehicles based on road surface type, characterized in that, The method includes: The system acquires power parameters and images of the road the vehicle travels on during its driving process. The power parameters are used to represent the operating status of the vehicle's power components. Based on the image and the power parameters, the road surface type where the vehicle is located is determined, including smooth road surface and bumpy road surface; Based on the road surface type where the vehicle is located, control the vehicle's anti-lock braking system; Determining the road surface type of the vehicle based on the image and the power parameters includes: Determine the numerical value of the color information of the image, wherein the numerical value of the color information is used to represent the brightness of the image being captured; Based on the power parameters, determine the wheel speed change rate of the vehicle; If the value of the color information is greater than or equal to a first threshold, or less than a second threshold, the road surface type is determined based on the wheel speed change rate, wherein the first threshold is greater than the second threshold. When the value of the color information is greater than or equal to the second threshold and less than the first threshold, the target determination method of the road surface type is determined according to the priority of the wheel speed change rate and the priority of the image. The target determination method includes the wheel speed change rate method and the image method. The road surface type is determined according to the target determination method.

2. The method according to claim 1, characterized in that, The anti-lock braking system that controls the vehicle based on the road surface type on which the vehicle is located includes any one of the following: When the road surface type is the bumpy road surface, deactivate the vehicle's anti-lock braking system; When the road surface type is the bumpy road surface, the slip ratio threshold of the wheel controlled by the anti-lock braking system is increased to the target slip ratio threshold. When the road surface type is the bumpy road surface, the deceleration threshold of the wheel controlled by the anti-lock braking system is increased to the target deceleration threshold.

3. The method according to claim 1, characterized in that, The power parameters include at least one of wheel speed pulse frequency, number of teeth on the half-shaft gear, and wheel rolling radius. Determining the wheel speed change rate of the vehicle based on the power parameters includes: The wheel edge linear velocity at the current moment is determined based on the wheel speed pulse frequency, the number of teeth, and the wheel rolling radius; The wheel speed change rate is determined based on the wheel edge linear velocity at the current moment and the wheel edge linear velocity at the previous moment, wherein the interval between the previous moment and the current moment is a preset time.

4. The method according to claim 1, characterized in that, The method for determining the target type of the road surface based on the priority of the wheel speed change rate and the priority of the image includes: If the priority of the wheel speed change rate is higher than the priority of the image, the wheel speed change rate method is determined as the target determination method; If the priority of the wheel speed change rate is lower than the priority of the image, the image method is determined as the target determination method; If the priority of the wheel speed change rate is equal to the priority of the road surface image, then the wheel speed change rate method or the image method is determined as the target determination method.

5. The method according to claim 4, characterized in that, When the target determination method is the wheel speed change rate method, determining the road surface type according to the target determination method includes: If the wheel speed change rate is greater than or equal to the preset wheel speed change rate, the road surface type is determined to be the bumpy road surface; If the wheel speed change rate is less than the preset wheel speed change rate, the road surface type is determined to be the smooth road surface.

6. The method according to claim 4, characterized in that, When the target determination method is the image method, determining the road surface type according to the target determination method includes: Feature extraction is performed on the image to determine its image features; If the similarity between the image feature and the first image feature is greater than or equal to the first similarity, the road surface type is determined to be the bumpy road surface; If the similarity between the image feature and the second image feature is greater than or equal to the second similarity, the road surface type is determined to be the smooth road surface.

7. The method according to claim 1, characterized in that, Before acquiring the image of the road the vehicle is traveling on, the method further includes: Obtain the vehicle speed; Determine the speed range to which the vehicle speed belongs; The image acquisition range is determined based on the vehicle speed range. And, acquiring images of the road on which the vehicle is traveling, including: Obtain the image corresponding to the acquisition range.

8. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 7.

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