An anti-skid control method and device, vehicle, storage medium and product

Abstract

This invention relates to an anti-skid control method, device, vehicle, storage medium, and product. The method includes: predicting the first road surface adhesion force of each wheel within a preset time domain based on a first road surface adhesion coefficient distribution map, the predicted driving trajectory of each wheel, and the vertical force of each wheel within a preset time domain; the predicted driving trajectory of each wheel is predicted based on vehicle driving-related information, and the vertical force of each wheel within the preset time domain is predicted based on pose perception information; predicting the first time when a target wheel will slip based on the first road surface adhesion force of each wheel within the preset time domain and the required torque of each wheel; and controlling the output torque of the electric drive and braking modules corresponding to the target wheel to smoothly approach the target torque from the current torque based on the first time when the target wheel slips; wherein the first road surface adhesion force corresponding to the first time is used as the target torque.

Description

Technical Field

[0001] This invention relates to the field of automotive control technology, specifically to an anti-skid control method, device, vehicle, storage medium, and product. Background Technology

[0002] Current wheel anti-skid systems are all wheel-centric, small-system feedback control systems that rely on observing wheel speed. They can only apply control after slippage occurs. Moreover, in order to meet real-time requirements, they use an ON / OFF pulse adjustment duty cycle control method, which results in a poor driving experience. When the algorithm is working, the longitudinal force on the vehicle changes abruptly, resulting in a noticeable jerky driving experience. Summary of the Invention

[0003] The present invention provides an anti-skid control method to solve the problem of abrupt changes in longitudinal force on a vehicle, resulting in a noticeable jerky driving experience; secondly, it provides an anti-skid control device; secondly, it provides a vehicle; thirdly, it provides a computer storage medium; and finally, it provides a computer program product.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] An anti-slip control method, the anti-slip control method comprising:

[0006] Based on the first road surface adhesion coefficient distribution map, the predicted driving trajectory of each wheel, and the vertical force of each wheel in the preset time domain, the first road surface adhesion force of each wheel in the preset time domain is predicted; wherein, the predicted driving trajectory of each wheel is predicted based on vehicle driving related information, and the vertical force of each wheel in the preset time domain is predicted based on pose perception information.

[0007] Based on the first road surface adhesion of each wheel in a preset time domain and the required torque of each wheel, the first time when the target wheel slips is predicted.

[0008] Based on the first moment when the target wheel slips, the output torque of the electric drive and braking module corresponding to the target wheel is controlled to smoothly approach the target torque from the current torque; wherein, the first road surface adhesion force corresponding to the first moment is taken as the target torque.

[0009] Based on the aforementioned technical means, by controlling the output torque of the electric drive and braking modules corresponding to the target wheel to smoothly approach the corresponding target torque from the current torque at the first moment of predicted target wheel slippage, the purpose of preventing wheel slippage and vehicle instability can be achieved, thereby ensuring the comfort and safety of vehicle driving.

[0010] Furthermore, predicting the first time when the target wheel slips based on the first road surface adhesion of each wheel within a preset time domain and the required torque of each wheel includes: determining that if the road surface adhesion of the target wheel is less than the required torque within the first time domain, the start time of the first time domain is taken as the first time when the target wheel slips.

[0011] Based on the above technical means, when it is determined that the road surface adhesion of the target wheel in the first time domain is less than the required torque, the start time of the first time domain is taken as the first time when the target wheel slips. This can ensure the accuracy of the first time when the target wheel slips.

[0012] Furthermore, the step of controlling the output torque of the electric drive and braking module corresponding to the target wheel to smoothly approach the target torque from the current torque based on the first time the target wheel slips includes: determining the minimum change gradient based on the first road surface adhesion of the target wheel at the current time and the first road surface adhesion of the target wheel at the first time; and controlling the output torque of the electric drive and braking module corresponding to the target wheel to smoothly approach the target torque from the current torque based on the minimum change gradient.

[0013] Based on the aforementioned technical means, by determining the minimum change gradient, the output torque of the electric drive and braking modules corresponding to the target wheel can be smoothly controlled from the current torque to the target torque in advance. This can prevent wheel slippage and vehicle instability, thereby ensuring the comfort and safety of vehicle driving.

[0014] Furthermore, determining the minimum change gradient based on the first road surface adhesion of the target wheel at the current time and the first road surface adhesion of the target wheel at the first time includes: determining a road surface adhesion difference based on the first road surface adhesion of the target wheel at the current time and the first road surface adhesion of the target wheel at the first time; and determining the minimum change gradient based on the road surface adhesion difference and the time difference between the current time and the first time.

[0015] Furthermore, the first road surface adhesion coefficient distribution map, the predicted driving trajectory of each wheel, and the vertical force of each wheel in the preset time domain are used to predict the first road surface adhesion force of each wheel in the preset time domain, including: predicting the road surface adhesion coefficient of each wheel in the preset time domain based on the first road surface adhesion coefficient distribution map and the predicted driving trajectory of each wheel in the vehicle coordinate system; and predicting the first road surface adhesion force of each wheel in the preset time domain based on the road surface adhesion coefficient and vertical force of each wheel in the preset time domain.

[0016] Based on the aforementioned technical means, and using the distribution map of the first road surface adhesion coefficient and the predicted driving trajectory of each wheel in the vehicle coordinate system, the road surface adhesion coefficient of the wheels can be predicted, which can improve the prediction accuracy of the road surface adhesion coefficient of each wheel within a preset time domain. Furthermore, based on the road surface adhesion coefficient and vertical force of each wheel within the preset time domain, the road surface adhesion force of the wheels can be predicted, further improving the prediction accuracy of the first road surface adhesion force of each wheel within the preset time domain.

[0017] Furthermore, the anti-skid control method further includes: acquiring first perception information; wherein the first perception information includes, but is not limited to, road type perception information, environmental perception information, key target perception information, and vehicle position information; determining a second road surface adhesion coefficient distribution map corresponding to the first perception information based on the correspondence between the perception information and the road surface adhesion coefficient distribution map; and converting the second road surface adhesion coefficient distribution map in the world coordinate system into a first road surface adhesion coefficient distribution map in the vehicle coordinate system.

[0018] Furthermore, the anti-skid control method also includes: predicting the vehicle's trajectory based on first driving-related information; and predicting the trajectory of each wheel in the vehicle's coordinate system based on the vehicle's trajectory and second driving-related information. The vehicle's driving-related information includes the first driving-related information and the second driving-related information. The first driving-related information includes, but is not limited to, vehicle slippage state, first reference vehicle speed, steering wheel angle, second road surface adhesion of each wheel, required torque of each wheel, and yaw moment. The second driving-related information includes, but is not limited to, the position of each wheel in the vehicle's coordinate system, the first reference vehicle speed, the steering wheel angle, navigation road information, and vehicle parameters.

[0019] Furthermore, after the output torque of the electric drive and braking module corresponding to the target wheel smoothly approaches the target torque from the current torque, the anti-slip control method further includes: determining the slip ratio of the target wheel based on the second reference vehicle speed and the wheel speed of the target wheel; determining whether the target wheel is slipping based on the slip ratio of the target wheel and the target slip ratio; and if the target wheel is determined to be slipping, adjusting the output torque of the electric drive and braking module corresponding to the target wheel based on the slip ratio of the target wheel so that the target wheel does not slip.

[0020] Based on the above technical means, the system monitors whether the target wheel slips in the first instance. If slippage occurs, it indicates that the prediction of slippage in the first instance is inaccurate. Based on the slip ratio of the target wheel, the system adjusts the output torque of the electric drive and braking modules corresponding to the target wheel in a timely manner to ensure that the target wheel does not slip.

[0021] Furthermore, the anti-skid control method further includes: determining the historical vehicle speed based on the vehicle coordinates at the current time and the vehicle coordinates at the first time in a static target coordinate system; wherein the static target coordinate system is a coordinate system established based on a static target, and the static target is a static target identified and tracked by the identification and tracking system; determining the longitudinal resultant force of the vehicle based on the first road surface adhesion and preset resistance of each wheel at the first time; determining the compensation speed based on the longitudinal resultant force of the vehicle, the vehicle mass, and the delay time of the identification and tracking system; and determining the second reference speed based on the historical vehicle speed and the compensation speed.

[0022] Furthermore, when it is determined that the target wheel is slipping, adjusting the output torque of the electric drive and braking module corresponding to the target wheel based on the slip ratio of the target wheel to prevent the target wheel from slipping includes: determining the slip ratio deviation of the target wheel based on the slip ratio of the target wheel and the target slip ratio; determining the torque adjustment value of the target wheel based on the slip ratio deviation of the target wheel; and adjusting the output torque of the electric drive and braking module corresponding to the target wheel based on the required torque of the target wheel and the torque adjustment value to prevent the target wheel from slipping.

[0023] Based on the above technical means, the slip ratio deviation of the target wheel is determined based on the slip ratio of the target wheel and the target slip ratio. Then, based on the slip ratio deviation of the target wheel, the torque adjustment value is determined. Finally, based on the required torque of the target wheel and the torque adjustment value, the output torque of the electric drive and braking modules corresponding to the target wheel is adjusted in a timely manner to ensure that the target wheel does not slip.

[0024] Furthermore, determining the torque adjustment value of the target wheel based on the slip ratio deviation of the target wheel includes: determining a proportional control term based on the slip ratio deviation of the target wheel and the proportional coefficient; determining an integral control term based on the slip ratio deviation of the target wheel and the integral coefficient; determining a derivative control term based on the slip ratio deviation of the target wheel and the derivative coefficient; and determining the torque adjustment value of the target wheel based on the proportional control term, the integral control term, and the derivative control term.

[0025] Furthermore, when it is determined that the target wheel has slipped, the anti-skid control method further includes: updating the first road surface adhesion coefficient distribution map based on the slip ratio deviation of the target wheel and the driving trajectory coordinates of the target wheel.

[0026] Based on the aforementioned technical methods, the first road surface adhesion coefficient distribution map is updated in real time based on the slip ratio deviation of the target wheel and the driving trajectory coordinates of the target wheel. This map can then be uploaded to a cloud-based road surface adhesion coefficient experience database, ensuring that any connected vehicle will not slip when encountering the same road surface again.

[0027] An anti-slip control device, the anti-slip control device comprising:

[0028] The prediction unit is used to predict the first road surface adhesion force of each wheel in a preset time domain based on the first road surface adhesion coefficient distribution map, the predicted driving trajectory of each wheel, and the vertical force of each wheel in a preset time domain; wherein, the driving trajectory of each wheel is predicted based on vehicle driving related information, and the vertical force of each wheel in the preset time domain is predicted based on pose perception information.

[0029] The prediction unit is also used to predict the first time when the target wheel slips, based on the first road surface adhesion of each wheel in a preset time domain and the required torque of each wheel.

[0030] The control unit is used to control the output torque of the electric drive and braking module corresponding to the target wheel to smoothly approach the target torque from the current torque based on the first time when the target wheel slips; wherein the first road surface adhesion force corresponding to the first time is used as the target torque.

[0031] A vehicle includes a processor and a memory configured to store a computer program capable of running on the processor, wherein the processor is configured to perform the steps of the aforementioned method when running the computer program.

[0032] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned method.

[0033] A computer program product includes a computer program or instructions that, when executed by a processor, implement the steps of the aforementioned method.

[0034] The beneficial effects of this invention are:

[0035] (1) Based on the first moment when the target wheel slips, the present invention controls the output torque of the electric drive and braking module corresponding to the target wheel to smoothly approach the target torque from the current torque, thereby preventing wheel slip and vehicle instability, and thus ensuring the comfort and safety of vehicle driving.

[0036] (2) When the road surface adhesion of the target wheel in the first time domain range is less than the required torque, the present invention takes the start time of the first time domain range as the first time when the target wheel slips, which can ensure the accuracy of the first time when the target wheel slips.

[0037] (3) By determining the minimum change gradient, the present invention controls the output torque of the electric drive and braking modules corresponding to the target wheel to smoothly approach the target torque from the current torque, thereby preventing wheel slippage and vehicle instability, and thus ensuring the comfort and safety of vehicle driving. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 1 ;

[0039] Figure 2 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 2 ;

[0040] Figure 3 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 3 ;

[0041] Figure 4 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 4 ;

[0042] Figure 5 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 5 ;

[0043] Figure 6 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 6 ;

[0044] Figure 7 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 7 ;

[0045] Figure 8 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 8 ;

[0046] Figure 9 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 9 ;

[0047] Figure 10 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 10 ;

[0048] Figure 11 This is a diagram showing the overall architecture of the wheel slippage system in an embodiment of the present invention;

[0049] Figure 12 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 10 one;

[0050] Figure 13This is a schematic diagram of the process for calculating the reference vehicle speed in an embodiment of the present invention;

[0051] Figure 14 This is a schematic diagram of the anti-slip control device structure in an embodiment of the present invention;

[0052] Figure 15 This is a schematic diagram of the vehicle composition structure in an embodiment of the present invention. Detailed Implementation

[0053] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0054] This invention provides an anti-slip control method. Figure 1 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 1 ,like Figure 1 As shown, the anti-slip control method includes the following steps:

[0055] S101: Based on the first road surface adhesion coefficient distribution map, the predicted driving trajectory of each wheel, and the first vertical force of each wheel in the time domain, predict the first road surface adhesion force of each wheel in the preset time domain; wherein, the predicted driving trajectory of each wheel is predicted based on vehicle driving-related information, and the first vertical force of each wheel in the preset time domain is predicted based on pose perception information.

[0056] In this embodiment of the invention, the first road surface adhesion coefficient distribution map includes the adhesion coefficients of each road surface location within the drivable area of ​​the vehicle.

[0057] In this embodiment of the invention, vehicle driving-related information refers to information affecting vehicle driving. Based on this information, the driving trajectory of each wheel is predicted to obtain the predicted driving trajectory of each wheel. The predicted driving trajectory of each wheel includes the trajectory coordinates of each wheel at various times within a preset time domain. The preset time domain refers to a time period following the current time.

[0058] In this embodiment of the invention, the vertical force of the wheel refers to the vertical force between the wheel and the ground. The vehicle includes a pose perception module, and the pose perception information corresponding to the pose perception module includes, but is not limited to, vehicle position information, vehicle posture information, wheel speed of each wheel, reference vehicle speed, six-axis acceleration, steering wheel angle, and required torque. The reference vehicle speed can be understood as the vehicle speed at the current moment. Based on the pose perception information, the vertical force of each wheel is predicted to obtain the first vertical force of each wheel in a preset time domain. The first vertical force of each wheel in the preset time domain includes the first vertical force of each wheel at each time point.

[0059] S102: Based on the first road surface adhesion of each wheel in the preset time domain and the required torque of each wheel, predict the first time when the target wheel will slip.

[0060] In this embodiment of the invention, the required torque refers to the torque required for driving or braking of each wheel. When the vehicle is accelerating, the required torque is the driving torque. When the vehicle is braking, the required torque is the braking torque.

[0061] In this embodiment of the invention, the required torque for each wheel refers to the required torque at the current time. The first road surface adhesion force of each wheel within a preset time domain includes the first road surface adhesion force of each wheel at various times. Therefore, slippage prediction can be performed based on the first road surface adhesion force of each wheel within the time domain and the required torque of each wheel, predicting the first time when the target wheel will slip. The target wheel can be at least one wheel among all the wheels of the vehicle.

[0062] S103: Based on the first moment when the target wheel slips, control the output torque of the electric drive and braking module corresponding to the target wheel to smoothly approach the target torque from the current torque; wherein, the first road surface adhesion force corresponding to the first moment is used as the target torque.

[0063] In this embodiment of the invention, different wheels correspond to different electric drive and braking modules. Based on the predicted moment when the target wheel slips, the output torque of the electric drive and braking modules corresponding to the target wheel is controlled in advance to smoothly approach the corresponding target torque from the current torque. This can prevent wheel slippage and vehicle instability, thereby ensuring the comfort and safety of vehicle driving.

[0064] In some embodiments, S103 includes the following step S201:

[0065] S201: If the road surface adhesion of the target wheel within the first time domain is determined to be less than the required torque, the start time of the first time domain shall be taken as the first time when the target wheel slips.

[0066] In this embodiment of the invention, it is determined whether the road surface adhesion of the target wheel at each time within the first time domain is less than the required torque. If the road surface adhesion of the target wheel at all times within the first time domain is less than the required torque, it indicates that the target wheel will slip at the beginning time of the first time domain, i.e., the beginning time of the first time domain is the first time when the target wheel will slip. If the road surface adhesion of the target wheel at each time within the first time domain is not less than the required torque, it indicates that the target wheel will not slip at the beginning time of the first time domain.

[0067] In this embodiment of the invention, the preset time domain includes multiple segments of the first time domain, and each segment of the first time domain may overlap or not overlap. Here, the first time domain range can be understood as any segment of the time domain within the preset time domain.

[0068] In some embodiments, S104 includes the following steps S301 to S302:

[0069] S301: Determine the minimum change gradient based on the first road surface adhesion of the target wheel at the current time and the first road surface adhesion of the target wheel at the first time.

[0070] S302: Based on the minimum change gradient, control the output torque of the electric drive and braking modules corresponding to the target wheel to smoothly approach the target torque from the current torque.

[0071] In some embodiments, S301 includes: determining a road adhesion difference based on the road adhesion of the target wheel at the current time and the road adhesion of the target wheel at a first time; and determining a minimum change gradient based on the road adhesion difference and the time difference between the current time and the first time.

[0072] In this embodiment of the invention, the absolute value of the difference between the first road surface adhesion of the target wheel at the current time and the first road surface adhesion of the target wheel at the first time is determined to obtain the road surface adhesion difference; and the absolute value of the difference between the current time and the first time is determined to obtain the time difference; then the ratio of the road surface adhesion difference to the time difference is determined to obtain the minimum change gradient.

[0073] Furthermore, based on the minimum change gradient, and according to the torque distribution strategy, the output torque of the electric drive and braking modules corresponding to the target wheel is smoothly controlled to approach the target torque from the current torque. Here, the torque distribution strategy can be that the torque change between two adjacent time intervals is the minimum change gradient. Alternatively, the torque distribution strategy can be that the torque change between two adjacent time intervals in the first time interval is greater than the minimum change gradient, and the torque change between two adjacent time intervals in the second time interval is less than the minimum change gradient. The sum of the first and second time intervals is the time difference between the current time and the first time interval. No specific restrictions are placed on the torque distribution strategy here.

[0074] In some embodiments, S101 includes the following steps S401 to S402:

[0075] S401: Based on the distribution map of the first road surface adhesion coefficient and the predicted driving trajectory of each wheel in the vehicle coordinate system, predict the road surface adhesion coefficient of each wheel in the preset time domain.

[0076] S402: Based on the road adhesion coefficient and vertical force of each wheel in the preset time domain, predict the first road adhesion force of each wheel in the preset time domain.

[0077] In this embodiment of the invention, the first road surface adhesion coefficient distribution map includes the adhesion coefficients at various road surface locations within the drivable area of ​​the current vehicle, in the vehicle coordinate system. The vehicle coordinate system refers to a coordinate system established with the vehicle center as the origin.

[0078] In this embodiment of the invention, the predicted driving trajectory of each wheel can be understood as the driving trajectory formed by connecting the trajectory coordinates of each wheel at various times within a preset time domain in the vehicle coordinate system. Therefore, based on the trajectory coordinates of each wheel at various times within the preset time domain, the road adhesion coefficient corresponding to the trajectory coordinates of each wheel at various times within the preset time domain is retrieved from the first road adhesion coefficient distribution map.

[0079] In this embodiment of the invention, vertical force refers to the vertical force between the wheel and the ground. Here, the road surface adhesion coefficient of each wheel at each time within a preset time domain is multiplied by the vertical force at the corresponding time to obtain the first road surface adhesion force of each wheel at each time within the preset time domain.

[0080] In some embodiments, the method further includes the following steps: acquiring first perception information; wherein the first perception information includes, but is not limited to, road type perception information, environmental perception information, key target perception information, and vehicle location information; determining the first road surface adhesion coefficient distribution map corresponding to the first perception information based on the correspondence between the perception information and the road surface adhesion coefficient distribution map; and converting the second road surface adhesion coefficient distribution map in the world coordinate system into the first road surface adhesion coefficient distribution map in the vehicle coordinate system.

[0081] In this embodiment of the invention, the perception modules in the vehicle include, but are not limited to, a road type perception module, an environment perception module, a key target perception and positioning module, and a pose perception module. The road type perception module identifies and perceives the vehicle's driving environment through onboard intelligent sensors, such as urban roads, highways, rural roads, cement roads, gravel roads, flooded roads, icy roads, and unpaved roads; this can be referred to as road type perception information. The environment perception module perceives the vehicle's surrounding environment through connected weather forecasts, onboard temperature sensors, and rainfall and ambient light sensors, such as temperature, altitude, wind speed, weather, and slope; this can be referred to as environmental perception information. The key target perception and positioning module identifies and tracks key targets in the driving environment that affect driving performance through radar and cameras, such as speed bumps, potholes, puddles, and ice or debris fields; this can be referred to as key target perception information. The pose perception module is used to perceive the vehicle's position information. Therefore, the first perception information includes, but is not limited to, road type perception information, environment perception information, key target perception information, and vehicle position information. The first perception information can be understood as perception information affecting the road surface adhesion coefficient.

[0082] In this embodiment of the invention, the perceived information can be understood as relevant information affecting the road surface adhesion coefficient. The road surface adhesion coefficient distribution map includes the adhesion coefficient at various road surface locations in the world coordinate system within the drivable area of ​​the vehicle. Therefore, based on the correspondence between the perceived information and the road surface adhesion coefficient distribution map, a second road surface adhesion coefficient distribution map in the world coordinate system corresponding to the first perceived information is determined. The second road surface adhesion coefficient distribution map includes the adhesion coefficient at various road surface locations in the world coordinate system within the drivable area of ​​the current vehicle.

[0083] Furthermore, based on the current vehicle location information, the second road surface adhesion coefficient distribution map in the world coordinate system is converted into the first road surface adhesion coefficient distribution map in the vehicle coordinate system.

[0084] In some embodiments, the steps S501 to S502 are also included:

[0085] S501: Predict the vehicle's trajectory based on first-stage driving-related information.

[0086] S502: Based on the vehicle's driving trajectory and second driving-related information, predict the driving trajectory of each wheel in the vehicle coordinate system.

[0087] In this embodiment of the invention, vehicle driving-related information includes first driving-related information and second driving-related information.

[0088] In this embodiment of the invention, the first driving-related information includes, but is not limited to, vehicle slippage state, first reference vehicle speed, steering wheel angle, second road surface adhesion of each wheel, required torque of each wheel, and yaw moment. The vehicle slippage state includes both slippage and non-slippage states. The first reference vehicle speed refers to the vehicle speed at the current time. The second road surface adhesion of each wheel refers to the road surface adhesion of each wheel at the current time.

[0089] In this embodiment of the invention, the second driving-related information includes, but is not limited to, the positions of each wheel in the vehicle coordinate system, the first reference vehicle speed, the steering wheel angle, navigation road information, and vehicle parameters. Here, navigation road information refers to the information about the road where the vehicle is currently located, obtained through a navigation map. Vehicle parameters include, but are not limited to, wheelbase, track width, and suspension and steering system parameters such as steering ratio.

[0090] In some embodiments, after controlling the output torque of the electric drive and braking module corresponding to the target wheel to smoothly approach the required torque from the current torque, the method further includes the following steps S601 to S604:

[0091] S601: Determine the slip ratio of the target wheel based on the second reference vehicle speed and the wheel speed of the target wheel.

[0092] In this embodiment of the invention, the second reference vehicle speed refers to the vehicle speed at the first time. The wheel speed of the target wheel refers to the wheel speed of the target wheel at the first time. Based on the second reference vehicle speed and the wheel speed of the target wheel, the slip ratio of the target wheel at the first time is determined.

[0093] S602: Determine whether the target wheel is slipping based on the slip ratio of the target wheel and the target slip ratio.

[0094] In this embodiment of the invention, when the slip ratio of the target wheel is greater than the target slip ratio, it is determined that the target wheel will slip at the first moment. Conversely, when the slip ratio of the target wheel is less than or equal to the target slip ratio, it is determined that the target wheel will not slip at the first moment.

[0095] S603: If it is determined that the target wheel is slipping, adjust the output torque of the electric drive and braking modules corresponding to the target wheel based on the slip ratio of the target wheel so that the target wheel does not slip.

[0096] In this embodiment of the invention, the output torque of the electric drive and braking modules corresponding to the target wheel is adjusted based on the slip ratio of the target wheel, so that the target wheel stops slipping, the vehicle returns to a steady state, and driving safety is ensured.

[0097] In some embodiments, S603 includes the following steps S701 to S703:

[0098] S701: Determine the slip ratio deviation of the target wheel based on the slip ratio of the target wheel and the target slip ratio.

[0099] In this embodiment of the invention, the difference between the slip ratio of the target wheel and the target slip ratio is used to obtain the slip ratio deviation of the target wheel.

[0100] S702: Determine the torque adjustment value of the target wheel based on the slip ratio deviation of the target wheel.

[0101] S703: Based on the required torque and torque adjustment value of the target wheel, adjust the output torque of the electric drive and braking modules corresponding to the target wheel to prevent the target wheel from slipping.

[0102] In this embodiment of the invention, the required torque of the target wheel is subtracted from the torque adjustment value to obtain the adjusted torque corresponding to the target wheel, which is then transmitted to the electric drive and braking module corresponding to the target wheel, causing the electric drive and braking module corresponding to the target wheel to output the adjusted torque. This stops the target wheel from slipping, allowing the vehicle to return to a steady state and ensuring driving safety.

[0103] In some embodiments, S702 includes the following steps S801 to S804:

[0104] S801: Determine the proportional control term based on the slip ratio deviation and proportional coefficient of the target wheel.

[0105] In this embodiment of the invention, the slip ratio deviation of the target wheel is multiplied by the proportional coefficient to obtain the proportional control term.

[0106] S802: Determine the integral control term based on the slip ratio deviation and integral coefficient of the target wheel.

[0107] In this embodiment of the invention, the slip ratio deviation of the target wheel is multiplied by the integral coefficient to obtain the integral control term.

[0108] S803: Determine the differential control term based on the slip ratio deviation and differential coefficient of the target wheel.

[0109] In this embodiment of the invention, the slip ratio deviation of the target wheel is multiplied by the differential coefficient to obtain the differential control term.

[0110] S804: Determines the torque adjustment value of the target wheel based on the proportional control term, integral control term, and derivative control term.

[0111] In this embodiment of the invention, the sum of the proportional control term, the integral control term, and the derivative control term is used to obtain the torque adjustment value of the target wheel.

[0112] In some embodiments, the calculation of the second reference vehicle speed includes the following steps S901 to S904:

[0113] S901: Based on the vehicle coordinates at the current time and the vehicle coordinates at the first time in the static target coordinate system, determine the historical vehicle speed; where the static target coordinate system is a coordinate system established based on static targets, and static targets are static targets identified and tracked by the identification and tracking system.

[0114] In this embodiment of the invention, at the current time, a static target is identified and tracked by an identification and tracking system (e.g., including a camera and radar), and a static target coordinate system is established with the static target as the origin. The vehicle coordinates in the static target coordinate system at the current time are determined, and the vehicle coordinates in the static target coordinate system at the first time are also determined. Finally, based on the vehicle coordinates at the current time and the vehicle coordinates at the first time, the historical vehicle speed is determined.

[0115] It should be noted that the number of static targets can be one or more. When there is only one static target, the historical vehicle speed is obtained by dividing the distance between the vehicle's coordinates at the current time and at the vehicle's coordinates at the previous time by the time difference between the current time and the previous time. When there are multiple static targets, the average of the historical vehicle speeds corresponding to each static target is determined to obtain the final historical vehicle speed.

[0116] S902: Determine the longitudinal resultant force of the vehicle based on the first road surface adhesion and preset resistance of each wheel at the first moment.

[0117] In this embodiment of the invention, the preset resistance includes, but is not limited to, slope resistance, rolling resistance, acceleration resistance, and wind resistance. It should be noted that slope resistance refers to the resistance generated by the slope of the road on which the vehicle travels. Rolling resistance refers to the resistance experienced by the wheels during rolling, which is opposite to the direction of rolling. Acceleration resistance refers to the inertial force generated during vehicle acceleration. Wind resistance refers to the resistance caused by wind when the vehicle's direction of travel is opposite to the wind direction.

[0118] In this embodiment of the invention, after the first road surface adhesion forces of each wheel are added together at the first moment, the preset resistance is subtracted to obtain the longitudinal resultant force of the vehicle.

[0119] S903: Determine the compensation speed based on the vehicle's longitudinal force, overall vehicle mass, and the delay time of the identification and tracking system.

[0120] In this embodiment of the invention, the ratio of the longitudinal resultant force of the vehicle to the total vehicle mass and the delay time is used to obtain the compensated vehicle speed.

[0121] S904: Determine the second reference speed based on historical vehicle speed and compensated vehicle speed.

[0122] In this embodiment of the invention, the historical vehicle speed is added to the compensated vehicle speed to obtain the second reference vehicle speed at the first time.

[0123] In some embodiments, the method further includes the following step S1001:

[0124] S1001: Update the first road surface adhesion coefficient distribution map based on the slip ratio deviation of the target wheel and the driving trajectory coordinates of the target wheel.

[0125] In this embodiment of the invention, based on the trajectory coordinates of the target wheel in the world coordinate system, the road surface adhesion coefficient of the target wheel is retrieved from the first road surface adhesion coefficient distribution map. Then, based on the slip ratio deviation of the target wheel, the road surface adhesion coefficient of the target wheel in the first road surface adhesion coefficient distribution map is updated, resulting in an updated first road surface adhesion coefficient distribution map. Subsequently, the updated first road surface adhesion coefficient distribution map can be uploaded to the cloud to update the road surface adhesion coefficient in the cloud for database verification. This ensures that any connected vehicle will not slip when encountering the same road surface again.

[0126] Based on the above embodiments, the present invention provides an overall architecture diagram of a wheel slippage system. Figure 11 This is a diagram illustrating the overall architecture of the wheel slippage system in an embodiment of the present invention. Figure 11 As shown, the wheel slippage system includes three modules: first, the perception layer 10; second, the prediction layer 20; and third, the decision and control layer 30.

[0127] The perception layer 10 mainly includes a road type perception module, an environment perception module, a key target perception and positioning module, and a pose perception module.

[0128] Perception layer 10 mainly implements the following functions:

[0129] 1) The road type perception module uses onboard intelligent sensors to identify and perceive the driving environment of the vehicle and obtain road type perception information, such as urban roads, highways, rural roads, cement roads, gravel roads, water crossing roads, icy and snowy roads, unpaved roads, etc.

[0130] 2) Environmental perception module: It senses the environment around the vehicle through networked weather forecasts, vehicle temperature sensors, rainfall and ambient light sensors, etc., and obtains environmental perception information such as temperature, altitude, wind speed, weather, and slope.

[0131] 3) The key target perception and positioning module uses radar and cameras to identify and track key targets in the driving environment that affect driving performance, and obtain key target perception information, such as speed bumps, potholes, water accumulation, ice, and foreign objects.

[0132] 4) The pose perception module uses GPS and inertial navigation systems to perceive the vehicle's position in the geodetic coordinate system. This information is then input into a map system to obtain environmental information about the vehicle's location, and further input into a road surface adhesion coefficient empirical database for retrieval, improving retrieval accuracy. It is also used to perceive wheel speed, reference vehicle speed, six-axis acceleration, steering wheel angle, and drive / braking torque (i.e., required torque).

[0133] Here, the perception layer 10 inputs all perceived information, i.e., the first perceived information 11, into the local road surface adhesion coefficient empirical database 12 (including the correspondence between perceived information and road surface adhesion coefficient distribution maps in the world coordinate system) for retrieval, and obtains the first road surface adhesion coefficient distribution map corresponding to the first perceived information 11 in the world coordinate system. Then, the first road surface adhesion coefficient distribution map corresponding to the first perceived information 11 in the world coordinate system is converted into the second road surface adhesion coefficient distribution map 13 corresponding to the first perceived information 11 in the vehicle coordinate system.

[0134] Here, the perception information 14 corresponding to the vehicle position and posture perception module includes at least: wheel speed, reference vehicle speed, six-axis acceleration, steering wheel angle and driving braking torque, i.e. required torque. Based on this information, the vertical force of each wheel is predicted to obtain the vertical force of each wheel in the preset time domain, i.e. wheel vertical dynamic load prediction.

[0135] The local database and the cloud-based online database 18 are connected via the network and updated bidirectionally on a periodic basis. If other connected vehicles drive on new road surfaces or new key targets that are not recorded in the cloud database and learn to identify the adhesion coefficient of the road surface or new key target, the connected vehicle will synchronize the information to the cloud database. After the cloud performs calculations, integration, and confirmation, it will update and publish the relevant information to all connected vehicles for retrieval.

[0136] Multiple static targets are identified and tracked using radar and cameras 17, and a static target coordinate system is established. The vehicle's historical speed 16 is calculated within this coordinate system. Then, based on the radar and camera system delay time, vehicle mass, and the road surface adhesion of each wheel, a compensation speed is estimated, i.e., delay compensation is applied 15. A reference speed is then calculated based on the historical speed and the compensation speed. Subsequently, the slip ratio of each wheel is calculated based on the reference speed and the wheel speed of each wheel, and the slippage status of all four wheels is monitored in real time based on the slip ratio.

[0137] Prediction layer 20 mainly implements the following functions:

[0138] 1) Vehicle trajectory prediction. Based on initial driving information such as vehicle slippage state, initial reference speed, steering wheel angle, road adhesion of each wheel, wheel-end driving force and braking torque, and yaw moment, the vehicle trajectory is predicted in human-driven scenarios. Similarly, based on initial driving information such as vehicle slippage state and autonomous driving planning and control outputs, the vehicle trajectory is predicted in autonomous driving scenarios. The parameters of the autonomous driving planning and control outputs include, but are not limited to, lateral deviation, heading angle, curvature, longitudinal distance, speed, and acceleration.

[0139] 2) Wheel trajectory prediction. Based on the vehicle's coordinates, reference speed, steering wheel angle, navigation road information, driving plan information, vehicle trajectory prediction, and basic vehicle parameters (such as wheelbase, track width, and suspension and steering system parameters such as steering ratio), i.e., the second driving information, the trajectory of each wheel is predicted, and the trajectory of all wheels is predicted22, including the trajectory prediction of the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel.

[0140] 3) Input the predicted driving trajectories 22 of all wheels into the second road surface adhesion coefficient distribution map 13 under the vehicle coordinate system, predict the road surface adhesion coefficient change curve of each wheel in the preset time domain, and then predict the vertical force change curve of each wheel in the time domain based on the vehicle pose perception information. Combine the road surface adhesion coefficient change curve and the wheel vertical force change curve in the time domain 23 to finally predict the first road surface adhesion curve TrcX(Fx,t)24 of each wheel in the preset time domain, including the first road surface adhesion of the left front wheel in the preset time domain, the first road surface adhesion of the right front wheel in the preset time domain, the first road surface adhesion of the left rear wheel in the preset time domain, and the first road surface adhesion of the right rear wheel in the preset time domain.

[0141] The decision-making and control layer 30 mainly has the following functions:

[0142] 1) Wheel slippage time prediction 31. Based on the road adhesion curve TrcX(Fx,t)24 of each wheel output by prediction layer 20 and the driver's required torque at the wheel end 32, the first time when the target wheel may slip is predicted. The number of target wheels can be one or more.

[0143] 2) Minimum change gradient 32. Based on the first road surface adhesion of the target wheel at the current time and the first road surface adhesion of the target wheel at the first time, the minimum change gradient 32 is determined.

[0144] 3) Wheel-end torque distribution of the electric drive and braking system 34. The wheel-end torque distribution is determined based on the powertrain configuration (front-wheel drive, rear-wheel drive, four-wheel drive, distributed drive, etc.) to coordinate the electric drive and braking systems to meet the target wheel-end torque. Specifically, this can be achieved by controlling the output torque of the target wheel based on the minimum change gradient, executing 35 for the electric drive torque or 36 for the braking torque, smoothly approaching the target torque from the current torque. The target torque is the initial road surface adhesion of the target wheel at the first moment.

[0145] 4) Feedback control based on slip ratio 37, specifically refers to determining the slip ratio of the target wheel based on the second reference vehicle speed and the wheel speed of the target wheel at the first time; and determining whether the target wheel has slipped based on the slip ratio of the target wheel and the target slip ratio.

[0146] 5) Monitoring Feedback Control Execution Details 38. This involves real-time monitoring of the slip ratio changes of each wheel. If the front-end predictive control algorithm is accurate, the slip ratio remains stable, and the wheels will not slip. If a deviation occurs in the front-end predictive control, causing wheel slippage, the closed-loop control algorithm is quickly activated. This algorithm rapidly suppresses wheel slippage by reducing torque through a slip ratio closed-loop control, restoring the vehicle to a steady state. Specifically, this rapid suppression of wheel slippage through slip ratio closed-loop torque reduction means adjusting the output torque of the electric drive and braking modules corresponding to the target wheel based on its slip ratio, preventing the target wheel from slipping.

[0147] 6) Correct the road surface adhesion coefficient empirical database 39. This involves feeding back the deviation of the predictive control algorithm and the actual road surface adhesion coefficient at the deviation points to the front-end perception and prediction module. The perception and prediction module then corrects the local and cloud-based road surface adhesion coefficient empirical database in real time based on the back-end feedback.

[0148] based on Figure 11 The diagram shown illustrates the overall architecture of the wheel slippage system. This embodiment of the invention provides a wheel slippage control process. Figure 12 This is a flowchart illustrating the anti-slip control method in an embodiment of the present invention. Figure 10 First, such as Figure 12 As shown, it includes the following steps:

[0149] S40: Obtain the required torque for each wheel.

[0150] The driver or the autonomous driving system starts driving the vehicle. The driver operates the accelerator or brake, while the autonomous driving system sends the required driving torque or braking torque at the wheel ends to the braking and drive system of the base vehicle in real time through the planning and control module of the system.

[0151] S41: Obtain first perception information.

[0152] The vehicle perception module senses road type, environmental information, key targets, and vehicle location in real time to obtain initial perception information.

[0153] S42: Import the first perception information into the road surface adhesion coefficient empirical database.

[0154] The first sensing information is imported into the road surface adhesion coefficient empirical database (including the correspondence between sensing information and road surface adhesion coefficient distribution map) to obtain the first road surface adhesion coefficient distribution map corresponding to the first sensing information in the world coordinate system.

[0155] S43: Convert the first road surface adhesion coefficient distribution map corresponding to the first perception information in the world coordinate system into the second road surface adhesion coefficient distribution map corresponding to the first perception information in the vehicle coordinate system.

[0156] S44: Vehicle trajectory prediction.

[0157] Based on the initial driving information, the vehicle's trajectory is predicted. Specifically, based on the vehicle's slippage state, and taking into account vehicle speed, steering angle, road adhesion of each wheel, wheel-end driving braking torque, yaw moment, etc., the vehicle's trajectory is predicted in human-driven scenarios; based on the vehicle's slippage state, the autonomous driving planning and control outputs are used to predict the vehicle's trajectory in autonomous driving scenarios.

[0158] S45: Prediction of the trajectory of each wheel.

[0159] Based on the vehicle's trajectory and second driving information, the predicted trajectory of each wheel is calculated. The second driving information includes, but is not limited to, the position of each wheel in the vehicle's coordinate system, the first reference vehicle speed, the steering wheel angle, navigation road information, and vehicle parameters. Here, navigation road information refers to information about the road the vehicle is currently on, obtained through a navigation map. Vehicle parameters include, but are not limited to, wheelbase, track width, and suspension and steering system parameters such as steering ratio.

[0160] S46: Coefficient of road surface adhesion under the trajectory of each wheel.

[0161] Based on the distribution map of the second road surface adhesion coefficient of each wheel and the predicted driving trajectory of each wheel, the road surface adhesion coefficient under the driving trajectory of each wheel is predicted.

[0162] S47: Prediction of vertical dynamic load on each wheel.

[0163] Based on pose perception information, the vertical force of each wheel in a preset time domain is predicted, i.e., the vertical dynamic load.

[0164] S48: Prediction of road surface adhesion under the driving trajectory of each wheel.

[0165] Based on the road surface adhesion coefficient under the driving trajectory of each wheel and the vertical force of each wheel in the preset time domain, the first road surface adhesion force of each wheel in the preset time domain, namely TrcX(Fx,t), is predicted.

[0166] S49: Slippage time prediction.

[0167] Based on the first road surface adhesion of each wheel within the preset time domain and the required torque of each wheel, if it is predicted that the target wheel will slip at time t, then S50 is executed; otherwise, if no slippage occurs, S51 is executed.

[0168] S50: Controls the target wheel to smoothly approach the target torque before time t.

[0169] The first road surface adhesion force of the target wheel after time t is the target torque.

[0170] S51: Torque control according to the torque requirements of each wheel.

[0171] After executing S50, the following steps are also included:

[0172] S52: Calculate the slip ratio of the target wheel in real time based on the wheel speed of the target wheel and the reference vehicle speed.

[0173] S53: Whether the target wheel slips.

[0174] S54: If slippage occurs, use closed-loop control based on slip ratio.

[0175] The system monitors the target wheel's slip ratio in real time. If the front-end predictive control algorithm is accurate, the slip ratio remains stable, and the target wheel will not slip. If a deviation occurs in the front-end predictive control, causing the target wheel to slip, the closed-loop control algorithm is quickly activated. This algorithm rapidly suppresses the target wheel's slippage by reducing torque through a slip ratio closed-loop control, allowing the vehicle to return to a steady state. Specifically, based on the slip ratio deviation between the target wheel's slip ratio and the target slip ratio, the output torque of the electric drive and braking modules corresponding to the target wheel is adjusted.

[0176] S55: Cloud-based online database.

[0177] The local road surface adhesion coefficient experience database and the cloud-based online database form an end-to-end road surface adhesion coefficient experience data verification database. The cloud will periodically synchronize the road surface adhesion coefficient information learned and certified by the connected vehicles to the vehicle end, and the vehicle end will also synchronize the road surface adhesion coefficient information learned to the cloud.

[0178] The deviation of the predictive control algorithm and the actual road adhesion coefficient at the deviation point are fed back to the front-end perception and prediction module. The perception and prediction module then corrects the local and cloud road adhesion coefficients in real time based on the back-end feedback and verifies the data.

[0179] Thus, by predicting the moment when a wheel will slip and controlling the corresponding wheel's drive and braking torque in advance, the wheel torque is smoothly brought close to the predicted adhesion boundary before the predicted slippage occurs. This fully utilizes ground adhesion, prevents vehicle instability due to wheel slippage, and ensures driving comfort. If the prediction of the wheel slippage moment is inaccurate and the wheel slips when it reaches the corresponding road surface, the wheel-end closed-loop torque control algorithm based on slip ratio is automatically activated to quickly suppress wheel slippage. Simultaneously, the road surface information at the slippage point is fed back to the perception and prediction layers. After learning and correction by the perception and prediction layers, this vehicle and other connected vehicles will not slip on the same road surface in the future. As the number of vehicles using this algorithm and the mileage increase, the accuracy of predictive control will continue to improve, and eventually the activation frequency of the feedback algorithm in this system will approach zero.

[0180] This invention proposes a method for calculating reference vehicle speed based on static target recognition and a static target coordinate system. This method is based on static target recognition using radar and cameras. A static target coordinate system is constructed based on multiple static targets. The "historical vehicle speed" of the vehicle at the previous moment is calculated within this static target coordinate system. Simultaneously, considering the system delays of the camera and radar, the change in vehicle speed ΔV within the system delay Δt is predicted. Finally, the historical vehicle speed is superimposed with the system delay-compensated speed to obtain the reference vehicle speed at the current moment. Specific implementation details are as follows: Figure 13 Refer to the vehicle speed calculation process as follows:

[0181] S60: Multi-static target recognition.

[0182] Multiple static targets are identified and tracked using radar cameras, and a static target coordinate system is established with each static target as the origin.

[0183] S61: Multiple static targets and vehicle ranging.

[0184] This means determining the distance between each static target and the vehicle at the current time, and determining the distance between each static target and the vehicle at the time of slippage, i.e., the first instant.

[0185] S62: Transform the vehicle coordinate system to the static target coordinate system.

[0186] S63: Calculate the historical vehicle speed in the static target coordinate system.

[0187] S64: Obtain the first road surface adhesion of each wheel during the slippage time.

[0188] S65: Based on the vehicle's position and pose information, calculate the vehicle speed at which the system delay needs to be compensated.

[0189] Specifically, the sum of the surface adhesion forces of each wheel during the initial slippage time is determined, and then a preset resistance is subtracted to obtain the vehicle's longitudinal resultant force. The ratio of the vehicle's longitudinal resultant force to the total vehicle mass and the coefficient delay time yields the compensated vehicle speed. The preset resistance includes, but is not limited to, gradient resistance, rolling resistance, acceleration resistance, and wind resistance. The specific resistances included can be determined based on the vehicle's position and attitude information.

[0190] S66: Calculate the reference speed by adding the historical vehicle speed to the compensated vehicle speed.

[0191] S67: Display the reference vehicle speed on the vehicle's display unit.

[0192] Furthermore, the slip ratio of each wheel is calculated based on the reference vehicle speed and the wheel speed of each wheel, and the slipping status of the four wheels is monitored in real time based on the slip ratio.

[0193] This invention provides an anti-slip control device. Figure 14 This is a schematic diagram of the anti-slip control device structure in an embodiment of the present invention, as shown below. Figure 14 As shown, the anti-slip control device 140 includes:

[0194] The prediction unit 1401 is used to predict the first road surface adhesion force of each wheel in a preset time domain based on the first road surface adhesion coefficient distribution map, the predicted driving trajectory of each wheel, and the vertical force of each wheel in a preset time domain; wherein, the driving trajectory of each wheel is predicted based on vehicle driving related information, and the vertical force of each wheel in the preset time domain is predicted based on pose perception information.

[0195] The prediction unit 1401 is also used to predict the first time when the target wheel slips, based on the first road surface adhesion of each wheel in a preset time domain and the required torque of each wheel.

[0196] The control unit 1402 is used to control the output torque of the electric drive and braking module corresponding to the target wheel to smoothly approach the target torque from the current torque based on the first time when the target wheel slips; wherein the first road surface adhesion force corresponding to the first time is used as the target torque.

[0197] In this embodiment of the invention, based on the predicted first moment when the target wheel slips, the output torque of the electric drive and braking modules corresponding to the target wheel is controlled in advance to smoothly approach the corresponding target torque from the current torque. This can achieve the purpose of preventing wheel slippage and vehicle instability, thereby ensuring the comfort and safety of vehicle driving.

[0198] In some embodiments, the prediction unit 1401 is further configured to determine, when the road surface adhesion of the target wheel within a first time domain range is less than the required torque, to take the start time of the first time domain range as the first time when the target wheel slips.

[0199] In some embodiments, the control unit 1402 is further configured to determine a minimum change gradient based on the first road surface adhesion of the target wheel at the current time and the first road surface adhesion of the target wheel at the first time; and based on the minimum change gradient, control the output torque of the electric drive and braking module corresponding to the target wheel to smoothly approach the target torque from the current torque.

[0200] In some embodiments, the control unit 1402 is further configured to determine a road surface adhesion difference based on the first road surface adhesion of the target wheel at the current time and the first road surface adhesion of the target wheel at the first time; and to determine the minimum change gradient based on the road surface adhesion difference and the time difference between the current time and the first time.

[0201] In some embodiments, the prediction unit 1401 is further configured to predict the road surface adhesion coefficient of each wheel in a preset time domain based on the first road surface adhesion coefficient distribution map and the predicted driving trajectory of each wheel in the vehicle coordinate system; and to predict the first road surface adhesion force of each wheel in the preset time domain based on the road surface adhesion coefficient and vertical force of each wheel in the preset time domain.

[0202] In some embodiments, the prediction unit 1401 is further configured to acquire first perception information; wherein the first perception information includes, but is not limited to, road type perception information, environmental perception information, key target perception information, and vehicle location information; based on the correspondence between the perception information and the road surface adhesion coefficient distribution map, determine the first road surface adhesion coefficient distribution map corresponding to the first perception information; and convert the second road surface adhesion coefficient distribution map in the world coordinate system into the first road surface adhesion coefficient distribution map in the vehicle coordinate system.

[0203] In some embodiments, the prediction unit 1401 is further configured to predict the vehicle's driving trajectory based on the first driving-related information; and to predict the predicted driving trajectory of each wheel in the vehicle coordinate system based on the vehicle's driving trajectory and the second driving-related information; wherein the vehicle driving-related information includes the first driving-related information and the second driving-related information, the first driving-related information including but not limited to vehicle slippage state, first reference vehicle speed, steering wheel angle, second road surface adhesion of each wheel, required torque of each wheel, and yaw moment, and the second driving-related information including but not limited to the position of each wheel in the vehicle coordinate system, the first reference vehicle speed, the steering wheel angle, navigation road information, and vehicle parameters.

[0204] In some embodiments, a monitoring unit is further included, configured to determine the slip ratio of the target wheel based on a second reference vehicle speed and the wheel speed of the target wheel; determine whether the target wheel is slipping based on the slip ratio of the target wheel and a target slip ratio; and, if the target wheel is determined to be slipping, adjust the output torque of the electric drive and braking module corresponding to the target wheel based on the slip ratio of the target wheel so that the target wheel does not slip.

[0205] In some embodiments, the system further includes a determining unit, configured to determine a historical vehicle speed based on the vehicle coordinates at the current time and the vehicle coordinates at the first time in a static target coordinate system; wherein the static target coordinate system is a coordinate system established based on a static target, and the static target is a static target identified and tracked by the identification and tracking system; the system determines the longitudinal resultant force of the vehicle based on the first road surface adhesion and preset resistance of each wheel at the first time; the system determines a compensated vehicle speed based on the longitudinal resultant force of the vehicle, the vehicle mass, and the delay time of the identification and tracking system; and the system determines a second reference vehicle speed based on the historical vehicle speed and the compensated vehicle speed.

[0206] In some embodiments, the monitoring unit is further configured to determine a slip ratio deviation of the target wheel based on the slip ratio of the target wheel and the target slip ratio; determine a torque adjustment value of the target wheel based on the slip ratio deviation of the target wheel; and adjust the output torque of the electric drive and braking module corresponding to the target wheel based on the required torque of the target wheel and the torque adjustment value, so that the target wheel does not slip.

[0207] In some embodiments, the monitoring unit is further configured to determine a proportional control term based on the slip ratio deviation and proportional coefficient of the target wheel; determine an integral control term based on the slip ratio deviation and integral coefficient of the target wheel; determine a derivative control term based on the slip ratio deviation and derivative coefficient of the target wheel; and determine a torque adjustment value of the target wheel based on the proportional control term, the integral control term, and the derivative control term.

[0208] In some embodiments, an updating unit is further included, which updates the first road surface adhesion coefficient distribution map based on the slip ratio deviation of the target wheel and the travel trajectory coordinates of the target wheel.

[0209] This invention also provides another vehicle. Figure 15 This is a schematic diagram of the vehicle composition structure in an embodiment of the present invention, such as... Figure 15 As shown, the vehicle 150 includes: a processor 1501 and a memory 1502 configured to store computer programs capable of running on the processor;

[0210] When the processor 1501 is configured to run a computer program, it executes the method steps described in the foregoing embodiments.

[0211] Of course, in practical applications, such as Figure 15 As shown, the various components in the vehicle 150 are coupled together via a bus system 1503. It is understood that the bus system 1503 is used to enable communication between these components. In addition to a data bus, the bus system 1503 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 15 The general labeled all buses as Bus System 1503.

[0212] In practical applications, the aforementioned processor can be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field-Programmable Gate Array (FPGA), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of the aforementioned processor can also be other types, and this embodiment of the invention does not impose specific limitations.

[0213] The aforementioned memory can be volatile memory, such as random-access memory (RAM); or non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); or a combination of the above types of memory, and provides instructions and data to the processor.

[0214] In an exemplary embodiment, the present invention also provides a computer-readable storage medium for storing a computer program.

[0215] Optionally, the computer-readable storage medium can be applied to any of the methods in the embodiments of the present invention, and the computer program causes the computer to execute the corresponding processes implemented by the processor in the various methods of the embodiments of the present invention. For the sake of brevity, these will not be described in detail here.

[0216] In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0217] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0218] Furthermore, in the various embodiments of the present invention, all functional units can be integrated into one processing module, or each unit can be a separate unit, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units. Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0219] The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0220] The features disclosed in the several product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0221] The features disclosed in the several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0222] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for preventing slippage, characterized in that, The anti-slip control method includes: Based on the first road surface adhesion coefficient distribution map, the predicted driving trajectory of each wheel, and the vertical force of each wheel in the preset time domain, the first road surface adhesion force of each wheel in the preset time domain is predicted; wherein, the predicted driving trajectory of each wheel is predicted based on vehicle driving related information, and the vertical force of each wheel in the preset time domain is predicted based on pose perception information. Based on the first road surface adhesion of each wheel in a preset time domain and the required torque of each wheel, the first time when the target wheel slips is predicted. Based on the first moment when the target wheel slips, the output torque of the electric drive and braking module corresponding to the target wheel is controlled to smoothly approach the target torque from the current torque; wherein, the first road surface adhesion force corresponding to the first moment is taken as the target torque.

2. The anti-slip control method according to claim 1, characterized in that, The method of predicting the first time when the target wheel will slip, based on the first road surface adhesion of each wheel within a preset time domain and the required torque of each wheel, includes: If the road surface adhesion of the target wheel is less than the required torque within the first time domain range, the start time of the first time domain range is taken as the first time when the target wheel slips.

3. The anti-slip control method according to claim 1 or 2, characterized in that, The step of controlling the output torque of the electric drive and braking module corresponding to the target wheel to smoothly approach the target torque from the current torque based on the first moment when the target wheel slips includes: Based on the first road surface adhesion of the target wheel at the current time and the first road surface adhesion of the target wheel at the first time, determine the minimum change gradient; Based on the minimum change gradient, the output torque of the electric drive and braking module corresponding to the target wheel is controlled to smoothly approach the target torque from the current torque.

4. The anti-slip control method according to claim 3, characterized in that, The determination of the minimum change gradient based on the first road surface adhesion of the target wheel at the current time and the first road surface adhesion of the target wheel at the first time includes: Based on the first road surface adhesion force of the target wheel at the current time and the first road surface adhesion force of the target wheel at the first time, determine the difference in road surface adhesion force. The minimum gradient of change is determined based on the difference in road surface adhesion and the time difference between the current time and the first time.

5. The anti-slip control method according to claim 1 or 2, characterized in that, The method of predicting the first road surface adhesion force of each wheel within a preset time domain based on the first road surface adhesion coefficient distribution map, the predicted driving trajectory of each wheel, and the vertical force of each wheel within a preset time domain includes: Based on the first road surface adhesion coefficient distribution map and the predicted driving trajectory of each wheel in the vehicle coordinate system, the road surface adhesion coefficient of each wheel in the preset time domain is predicted. Based on the road surface adhesion coefficient and vertical force of each wheel in a preset time domain, the first road surface adhesion force of each wheel in the preset time domain is predicted.

6. The anti-slip control method according to claim 1 or 2, characterized in that, The anti-slip control method further includes: Acquire first perception information; wherein, the first perception information includes road type perception information, environmental perception information, key target perception information, and vehicle location information; the environmental perception information refers to information such as temperature, altitude, wind speed, weather, and slope; Based on the correspondence between the perceived information and the road surface adhesion coefficient distribution map, the second road surface adhesion coefficient distribution map corresponding to the first perceived information is determined. The second road surface adhesion coefficient distribution map in the world coordinate system is converted into the first road surface adhesion coefficient distribution map in the vehicle coordinate system.

7. The anti-slip control method according to claim 1 or 2, characterized in that, The anti-slip control method further includes: Based on first-stage driving-related information, predict the vehicle's driving trajectory; Based on the vehicle's driving trajectory and the second driving-related information, predict the driving trajectory of each wheel in the vehicle coordinate system. The vehicle driving-related information includes the first driving-related information and the second driving-related information. The first driving-related information includes the vehicle slippage state, the first reference vehicle speed, the steering wheel angle, the second road surface adhesion of each wheel, the required torque of each wheel, and the yaw moment. The second driving-related information includes the position of each wheel in the vehicle coordinate system, the first reference vehicle speed, the steering wheel angle, navigation road information, and vehicle structural parameters.

8. The anti-slip control method according to claim 1 or 2, characterized in that, After the output torque of the electric drive and braking module corresponding to the target wheel is smoothly controlled to approach the target torque from the current torque, the anti-slip control method further includes: The slip ratio of the target wheel is determined based on the second reference vehicle speed and the wheel speed of the target wheel; Based on the slip ratio of the target wheel and the target slip ratio, determine whether the target wheel has slipped; If the target wheel is found to be slipping, the output torque of the electric drive and braking modules corresponding to the target wheel is adjusted based on the slip ratio of the target wheel to prevent the target wheel from slipping.

9. The anti-slip control method according to claim 8, characterized in that, The anti-slip control method further includes: Based on the vehicle coordinates at the current time and the vehicle coordinates at the first time in a static target coordinate system, the historical vehicle speed is determined; wherein, the static target coordinate system is a coordinate system established based on a static target, and the static target is a static target identified and tracked by the identification and tracking system; Based on the first road surface adhesion and preset resistance of each wheel at the first time, the longitudinal resultant force of the vehicle is determined. The compensation speed is determined based on the longitudinal force of the vehicle, the overall vehicle mass, and the delay time of the identification and tracking system. The second reference speed is determined based on the historical vehicle speed and the compensated vehicle speed.

10. The anti-slip control method according to claim 8, characterized in that, When it is determined that the target wheel is slipping, based on the slip ratio of the target wheel, the output torque of the electric drive and braking module corresponding to the target wheel is adjusted to prevent the target wheel from slipping, including: Based on the slip ratio of the target wheel and the target slip ratio, the slip ratio deviation of the target wheel is determined; Based on the slip ratio deviation of the target wheel, the torque adjustment value of the target wheel is determined; Based on the required torque of the target wheel and the torque adjustment value, the output torque of the electric drive and braking modules corresponding to the target wheel is adjusted so that the target wheel does not slip.

11. The anti-slip control method according to claim 10, characterized in that, Determining the torque adjustment value of the target wheel based on the slip ratio deviation of the target wheel includes: Based on the slip ratio deviation and proportional coefficient of the target wheel, the proportional control term is determined; Based on the slip ratio deviation and integral coefficient of the target wheel, the integral control term is determined; Based on the slip ratio deviation and differential coefficient of the target wheel, the differential control term is determined; The torque adjustment value of the target wheel is determined based on the proportional control term, the integral control term, and the derivative control term.

12. The anti-slip control method according to claim 8, characterized in that, When it is determined that the target wheel has slipped, the anti-skid control method further includes: The first road surface adhesion coefficient distribution map is updated based on the slip ratio deviation of the target wheel and the travel trajectory coordinates of the target wheel.

13. An anti-slip control device, characterized in that, The anti-slip control device includes: The prediction unit is used to predict the first road surface adhesion force of each wheel in a preset time domain based on the first road surface adhesion coefficient distribution map, the predicted driving trajectory of each wheel, and the vertical force of each wheel in a preset time domain; wherein, the driving trajectory of each wheel is predicted based on vehicle driving related information, and the vertical force of each wheel in the preset time domain is predicted based on pose perception information. The prediction unit is also used to predict the first time when the target wheel slips, based on the first road surface adhesion of each wheel in a preset time domain and the required torque of each wheel. The control unit is used to control the output torque of the electric drive and braking module corresponding to the target wheel to smoothly approach the target torque from the current torque based on the first time when the target wheel slips; wherein the first road surface adhesion force corresponding to the first time is used as the target torque.

14. A vehicle, characterized in that, The vehicle includes: a processor and a memory configured to store computer programs capable of running on the processor. Wherein, when the processor is configured to run the computer program, it executes the steps of the anti-slip control method according to any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the anti-slip control method according to any one of claims 1 to 12.

16. A computer program product comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the anti-slip control method according to any one of claims 1 to 12.

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