Antiskid control method and device, vehicle, storage medium and product

Abstract

The invention relates to an anti-skid control method and device, a vehicle, a storage medium and a product, and the method comprises the steps: predicting the first road adhesion force of each wheel in a preset time domain based on a first road adhesion coefficient distribution map, the predicted driving track of each wheel and the vertical force of each wheel in the preset time domain; the predicted driving track of each wheel is predicted based on vehicle driving related information, and the vertical force of each wheel in a preset time domain is predicted based on pose sensing information; based on the first road surface adhesive force of each wheel in the preset time domain and the demand torque of each wheel, predicting first time when the target wheel slips; based on the first time when the target wheel slips, the output torque of an electric drive and brake module corresponding to the target wheel is controlled to smoothly approach the target torque from the current torque; wherein the first road surface adhesive force corresponding to the first time is used as the target torque.

Description

Technical Field

[0001] The present invention relates to the field of automobile control technology, and in particular to an anti-skid control method, device, vehicle, storage medium and product. Background Art

[0002] The current wheel anti-skid systems are all wheel-centric, small system feedback control systems based on observing the wheel speed. They need to apply control only after skidding occurs. In addition, in the control process, in order to meet real-time requirements, a (ON / OFF) pulse adjustment duty cycle control method with poor driving experience is adopted. When the algorithm is working, the longitudinal force of the vehicle changes suddenly, resulting in a more obvious jerky driving experience. Summary of the invention

[0003] The present invention provides an anti-skid control method to solve the problem in the prior art that the longitudinal force of the vehicle changes suddenly, resulting in a more obvious jerking driving experience; secondly, an anti-skid control device is provided; thirdly, a car is provided; thirdly, a computer storage medium is provided; and finally, a computer program product is provided.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

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

[0006] Predicting the first road adhesion force of each wheel in a preset time domain based on the first road 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 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 posture perception information;

[0007] Predicting the first time when the target wheel slips based on the first road adhesion of each wheel in a preset time domain and the required torque of each wheel;

[0008] Based on the first time when the target wheel slips, the output torque of the electric drive and brake modules corresponding to the target wheel is controlled to smoothly approach the target torque from the current torque; wherein the first road adhesion corresponding to the first time is used as the target torque.

[0009] According to the above technical means, based on the first time when the target wheel is predicted to slip, the output torque of the electric drive and braking module corresponding to the target wheel is controlled in advance to smoothly approach the corresponding target torque from the current torque, thereby preventing wheel slip and vehicle instability, thereby ensuring the comfort and safety of vehicle driving.

[0010] Further, based on the first road adhesion of each wheel in a preset time domain and the required torque of each wheel, predicting the first time when the target wheel slips includes: determining that when the road adhesion of the target wheel in the first time domain range is less than the required torque, taking the start time of the first time domain range as the first time when the target wheel slips.

[0011] According to the above technical means, when it is determined that the road adhesion of the target wheel in the first time domain range is less than the required torque, the start time of the first time domain range is taken as the first time when the target wheel slips, so as to ensure the accuracy of the first time when the target wheel slips.

[0012] Further, based on the first time 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, including: determining a minimum change gradient based on the first road adhesion of the target wheel at the current time and the first road adhesion of the target wheel at the first time; based on the minimum change gradient, 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.

[0013] According to the above 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 controlled in advance to smoothly approach the target torque from the current torque, thereby preventing wheel slippage and vehicle instability, thereby ensuring the comfort and safety of vehicle driving.

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

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

[0016] According to the above technical means, the road adhesion coefficient of the wheel is predicted based on the first road adhesion coefficient distribution map and the predicted driving trajectory of each wheel in the vehicle coordinate system, which can improve the prediction accuracy of the road adhesion coefficient of each wheel in a preset time domain. On this basis, the road adhesion of the wheel is predicted based on the road adhesion coefficient and vertical force of each wheel in the preset time domain, further improving the prediction accuracy of the first road adhesion of each wheel in the preset time domain.

[0017] Furthermore, the anti-skid control method also 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; based on the correspondence between the perception information and the road adhesion coefficient distribution map, determining a second road adhesion coefficient distribution map corresponding to the first perception information; converting the second road adhesion coefficient distribution map in the world coordinate system into the first road adhesion coefficient distribution map in the vehicle coordinate system.

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

[0019] Further, after 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, the anti-skid control method also includes: determining the slip rate 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 rate of the target wheel and the target slip rate; and 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 rate of the target wheel so that the target wheel does not slip.

[0020] According to the above technical means, the target wheel is monitored for slippage at the first time. If it slips, it means that there is a deviation in the prediction of slippage at the first time. Based on the slip rate of the target wheel, the output torque of the electric drive and brake module corresponding to the target wheel is adjusted in time to ensure that the target wheel does not slip.

[0021] Furthermore, the anti-skid control method also 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 identified and tracked by a recognition and tracking system; determining the longitudinal resultant force of the vehicle based on the first road adhesion and a preset resistance of each wheel at the first time; determining the compensated vehicle speed based on the longitudinal resultant force of the vehicle, the vehicle mass and the delay time of the recognition and tracking system; and determining the second reference vehicle speed based on the historical vehicle speed and the compensated vehicle speed.

[0022] Further, when it is determined that the target wheel is slipping, based on the slip rate of the target wheel, the output torque of the electric drive and braking module corresponding to the target wheel is adjusted so that the target wheel does not slip, including: determining the slip rate deviation of the target wheel based on the slip rate deviation of the target wheel; determining the torque adjustment value of 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.

[0023] According to the above technical means, based on the slip rate of the target wheel and the target slip rate, the slip rate deviation of the target wheel is determined, and then based on the slip rate deviation of the target wheel, the torque adjustment value is determined, and then based on the required torque of the target wheel and the torque adjustment value, the output torque of the electric drive and braking module corresponding to the target wheel is adjusted in time to ensure that the target wheel does not slip.

[0024] Further, the torque adjustment value of the target wheel is determined based on the slip rate deviation of the target wheel, including: determining a proportional control term based on the slip rate deviation and a proportional coefficient of the target wheel; determining an integral control term based on the slip rate deviation and an integral coefficient of the target wheel; determining a differential control term based on the slip rate deviation and a differential coefficient of the target wheel; and determining the torque adjustment value of the target wheel based on the proportional control term, the integral control term and the differential control term.

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

[0026] According to the above technical means, the first road adhesion coefficient distribution map is updated in real time based on the slip rate deviation of the target wheel and the driving track coordinates of the target wheel. The road adhesion coefficient experience database can be uploaded to the cloud later, so that any connected vehicle will not slip when encountering the same road next time.

[0027] An anti-skid control device, comprising:

[0028] A prediction unit, configured to predict the first road adhesion force of each wheel in a preset time domain based on the first road 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 the vehicle driving related information, and the vertical force of each wheel in the preset time domain is predicted based on the posture perception information;

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

[0030] A 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 adhesion corresponding to the first time is used as the target torque.

[0031] A vehicle comprises: a processor and a memory configured to store a computer program that can be run on the processor, wherein the processor is configured to execute the steps of the above method when running the computer program.

[0032] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0033] A computer program product comprises a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the steps of the aforementioned method are implemented.

[0034] Beneficial effects of the present invention:

[0035] (1) The present invention controls the output torque of the electric drive and brake 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, thereby preventing the wheel from slipping and the vehicle from becoming unstable, thereby ensuring the comfort and safety of vehicle driving;

[0036] (2) When the present invention determines that the road adhesion of the target wheel in the first time domain range is less than the required torque, the start time of the first time domain range is used as the first time when the target wheel slips, so as to ensure the accuracy of the first time when the target wheel slips;

[0037] (3) The present invention controls the output torque of the electric drive and brake modules corresponding to the target wheel in advance to smoothly approach the target torque from the current torque by determining the minimum change gradient, thereby preventing wheel slip and vehicle instability, thereby ensuring the comfort and safety of vehicle driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 1 ;

[0039] Figure 2 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 2 ;

[0040] Figure 3 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 3 ;

[0041] Figure 4 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 4 ;

[0042] Figure 5 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 5 ;

[0043] Figure 6 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 6 ;

[0044] Figure 7 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 7 ;

[0045] Figure 8 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 8 ;

[0046] Fig. 9 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 9 ;

[0047] Fig.10 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 10 ;

[0048] Fig.11 This is an overall architecture diagram of a wheel slip system in an embodiment of the present invention;

[0049] Fig.12 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 10 one;

[0050] Fig.13A schematic diagram of a flow chart of calculating a reference vehicle speed in an embodiment of the present invention;

[0051] Fig.14 A schematic diagram of the composition structure of the anti-skid control device in an embodiment of the present invention;

[0052] Fig.15 Schematic diagram of the vehicle structure in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will describe the embodiments of the present invention 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 contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways 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, not for limiting the scope of protection of the present invention.

[0054] The embodiment of the present invention provides an anti-skid control method. Figure 1 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 1 ,like Figure 1 As shown, the anti-skid control method comprises the following steps:

[0055] S101: Based on the first road 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 adhesion of each wheel in a 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 posture perception information.

[0056] In the embodiment of the present invention, the first road adhesion coefficient distribution map includes the adhesion coefficients of various road positions within the drivable area of ​​the vehicle.

[0057] In the embodiment of the present invention, the vehicle driving related information refers to the related information that affects the vehicle driving. The driving trajectory of each wheel is predicted based on the related information that affects the vehicle driving to obtain the predicted driving trajectory of each wheel. The predicted driving trajectory of each wheel includes the trajectory coordinates of each wheel at each time in a preset time domain. The preset time domain refers to a time domain after the current time.

[0058] In an embodiment of the present invention, the vertical force of the wheel refers to the vertical force between the wheel and the ground. The vehicle includes a posture sensing module, and the posture sensing information corresponding to the posture sensing module includes but is not limited to vehicle position information, vehicle posture information, wheel speed of each wheel, reference vehicle speed, six-way acceleration, steering wheel angle and required torque. The reference vehicle speed can be understood as the vehicle speed at the current moment. The vertical force of each wheel is predicted based on the posture sensing information 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.

[0059] S102: Predicting the first time when the target wheel slips based on the first road adhesion of each wheel in a preset time domain and the required torque of each wheel.

[0060] In the embodiment of the present invention, the required torque refers to the torque required for driving or braking each wheel. When the vehicle is accelerator-operated, the required torque is the driving required torque. When the vehicle is braking, the required torque is the braking required torque.

[0061] In the embodiment of the present invention, the required torque of each wheel refers to the required torque at the current time. The first road adhesion of each wheel in the preset time domain includes the first road adhesion of each wheel at each time. Therefore, slip prediction can be performed based on the first road adhesion of each wheel in the time domain and the required torque of each wheel to predict the first time when the target wheel slips. The target wheel can be at least one wheel among all the wheels included in the vehicle.

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

[0063] In the embodiment of the present invention, different wheels correspond to different electric drive and brake modules. Based on the first time when the target wheel is predicted to slip, the output torque of the electric drive and brake module corresponding to the target wheel is controlled in advance to smoothly approach the corresponding target torque from the current torque, so as to prevent the wheel from slipping and the vehicle from becoming unstable, thereby ensuring the comfort and safety of vehicle driving.

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

[0065] S201: When it is determined that the road adhesion of the target wheels in the first time domain range is less than the required torque, the start time of the first time domain range is used as the first time when the target wheels slip.

[0066] In the embodiment of the present invention, it is determined whether the road adhesion of the target wheel at each time in the first time domain range is less than the required torque. If the road adhesion of the target wheel at all times in the first time domain range is less than the required torque, it means that the target wheel will slip at the start time of the first time domain range, that is, the start time of the first time domain range is the first time when the target wheel slips. If the road adhesion of the target wheel at each time in the first time domain range is not less than the required torque, it means that the target wheel will not slip at the start time of the first time domain range.

[0067] In the embodiment of the present invention, the preset time domain includes multiple first time domain ranges, and each first time domain range may overlap or may not overlap. The first time domain range here can be understood as any time domain range in the preset time domain.

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

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

[0070] S302: Based on the minimum change gradient, control the output torque of the electric drive and brake 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 the 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 an embodiment of the present invention, the absolute value of the difference between the first road adhesion of the target wheel at the current time and the first road adhesion of the target wheel at the first time is determined to obtain the road adhesion difference; and the absolute value of the difference between the current time and the first time is determined to obtain the time difference; and then the ratio of the road adhesion difference to the time difference is determined to obtain the minimum change gradient.

[0073] Furthermore, based on the minimum change gradient, according to the torque distribution strategy, the output torque of the electric drive and brake modules corresponding to the target wheel is controlled to smoothly approach the target torque from the current torque. Here, the torque distribution strategy can be that the torque change between two adjacent times is the minimum change gradient. Alternatively, the torque distribution strategy can be that the torque change between two adjacent times in the first time period is greater than the minimum change gradient, and the torque change between two adjacent times in the second time period is less than the minimum change gradient. The sum of the first time period and the second time period is the time difference between the current time and the first time. There is no specific restriction on the torque distribution strategy.

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

[0075] S401: Predicting a road adhesion coefficient of each wheel in a preset time domain based on a first road adhesion coefficient distribution map and a predicted driving trajectory of each wheel in a vehicle coordinate system.

[0076] S402: Predicting a first road adhesion force of each wheel within a preset time domain based on a road adhesion coefficient and a vertical force of each wheel within a preset time domain.

[0077] In the embodiment of the present invention, the first road adhesion coefficient distribution map includes adhesion coefficients of various road positions in the drivable area of ​​the current vehicle in the vehicle coordinate system, wherein the vehicle coordinate system refers to a coordinate system established with the center of the vehicle as the origin.

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

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

[0080] In some embodiments, the following steps are also included: obtaining 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; based on the correspondence between the perception information and the road adhesion coefficient distribution map, determining the first road adhesion coefficient distribution map corresponding to the first perception information; converting the second road adhesion coefficient distribution map in the world coordinate system into the first road adhesion coefficient distribution map in the vehicle coordinate system.

[0081] In the embodiment of the present invention, the perception module in the vehicle includes but is not limited to a road type perception module, an environment perception module, a key target perception and positioning module, and a posture perception module. Among them, the road type perception module recognizes and perceives the driving environment of the vehicle through the on-board intelligent sensor, such as urban roads, highways, rural roads, cement roads, gravel roads, water roads, ice and snow roads, and unpaved roads, which can be called road type perception information. The environment perception module perceives the surrounding environment of the vehicle through networked weather forecasts, on-board temperature sensors, rainfall ambient light sensors, etc., such as temperature, altitude, wind speed, weather, and slope, which can be called environmental perception information. The key target perception and positioning module identifies and tracks key targets that affect driving performance in the driving environment through radar and cameras, such as speed bumps, potholes, water accumulation, and foreign body belts on the ice surface, which can be called key target perception information. The posture perception module is used to perceive the location information of the vehicle. Then the first perception information includes but is not limited to road type perception information, environment perception information, key target perception information, and vehicle location information. The first perception information can be understood as perception information that affects the road adhesion coefficient.

[0082] In the embodiment of the present invention, the perception information can be understood as relevant information that affects the road adhesion coefficient. The road adhesion coefficient distribution map includes the adhesion coefficients of each road position in the drivable area of ​​the vehicle in the world coordinate system. Therefore, based on the correspondence between the perception information and the road adhesion coefficient distribution map, a second road adhesion coefficient distribution map in the world coordinate system corresponding to the first perception information is determined, and the second road adhesion coefficient distribution map includes the adhesion coefficients of each road position in the drivable area of ​​the current vehicle in the world coordinate system.

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

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

[0085] S501: Predicting a vehicle driving trajectory based on first driving related information.

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

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

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

[0089] In the embodiment of the present invention, the second driving related information includes but is not limited to the position of each wheel in the vehicle coordinate system, the first reference vehicle speed, the steering wheel angle, the navigation road information, and the vehicle parameters. Here, the navigation road information refers to the information of the road on which the current vehicle is located obtained through the navigation map. The vehicle parameters include but are not limited to the wheelbase, track width, and suspension steering system parameters such as the steering speed ratio.

[0090] In some embodiments, after the output torque of the electric drive and brake module corresponding to the target wheel is controlled to smoothly approach the required torque from the current torque, the following steps S601 to S604 are also included:

[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 the embodiment of the present 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 rate 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 the embodiment of the present invention, when the slip rate of the target wheel is greater than the target slip rate, it is determined that the target wheel will slip at the first time. Correspondingly, when the slip rate of the target wheel is less than or equal to the target slip rate, it is determined that the target wheel will not slip at the first time.

[0095] S603: When it is determined that the target wheel is slipping, based on the slip rate of the target wheel, the output torque of the electric drive and brake modules corresponding to the target wheel is adjusted so that the target wheel does not slip.

[0096] In the embodiment of the present invention, based on the slip rate of the target wheel, the output torque of the electric drive and brake module corresponding to the target wheel is adjusted, 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 a slip ratio deviation of the target wheel based on the slip ratio of the target wheel and the target slip ratio.

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

[0100] S702: Determine a 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 the torque adjustment value of the target wheel, adjust the output torque of the electric drive and brake modules corresponding to the target wheel so that the target wheel does not slip.

[0102] In the embodiment of the present invention, the torque adjustment value is subtracted from the required torque of the target wheel to obtain the adjusted torque corresponding to the target wheel, and the torque is transmitted to the electric drive and brake module corresponding to the target wheel, so that the electric drive and brake module corresponding to the target wheel outputs the adjusted torque. In this way, the target wheel stops slipping, so that the vehicle returns to a steady state and ensures driving safety.

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

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

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

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

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

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

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

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

[0111] In the embodiment of the present invention, the torque adjustment value of the target wheel is obtained by the sum of the proportional control term, the integral control term and the differential control term.

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

[0113] S901: Determine the historical vehicle speed based on the vehicle coordinates at the current time and the vehicle coordinates at the first time in the 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 recognition and tracking system.

[0114] In an embodiment of the present invention, at the current time, a static target is identified and tracked by an identification and tracking system (for example, including a camera and a radar), and a static target coordinate system is established with the static target as the coordinate origin, and the vehicle coordinates of the vehicle in the static target coordinate system at the current time are determined, as well as the vehicle coordinates of the vehicle in the static target coordinate system at the first time. 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 the number of static targets is one, the distance between the vehicle coordinates at the current time and the vehicle coordinates at the first time is divided by the time difference between the current time and the first time to obtain the historical vehicle speed. When the number of static targets is multiple, the average of the historical vehicle speeds corresponding to the multiple static targets is determined to obtain the final historical vehicle speed.

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

[0117] In the embodiment of the present invention, the preset resistance includes but is not limited to slope resistance, rolling resistance, acceleration resistance and wind resistance. It should be noted that the slope resistance refers to the resistance generated by the slope of the road on which the vehicle is traveling. Rolling resistance refers to the resistance in the opposite direction of the rolling direction that the wheel is subjected to during the rolling process. Acceleration resistance refers to the inertial force generated by acceleration during the driving of the vehicle. Wind resistance refers to the resistance caused by the wind when the driving direction of the vehicle is opposite to the wind direction.

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

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

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

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

[0122] In the embodiment of the present 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 following steps S1001 are also included:

[0124] S1001: Based on the slip ratio deviation of the target wheel and the driving trajectory coordinates of the target wheel, updating a first road adhesion coefficient distribution map.

[0125] In the embodiment of the present invention, based on the driving track coordinates of the target wheel in the world coordinate system, the road adhesion coefficient of the target wheel is retrieved from the first road adhesion coefficient distribution map, and then, based on the slip rate deviation of the target wheel, the road adhesion coefficient of the target wheel in the first road adhesion coefficient distribution map is updated to obtain an updated first road adhesion coefficient distribution map. The updated first road adhesion coefficient distribution map can be subsequently uploaded to the cloud to update the road adhesion coefficient data database in the cloud, so that any connected vehicle will not slip when encountering the same road next time.

[0126] Based on the above embodiments, the present invention provides an overall architecture diagram of a wheel slip system. Fig.11 FIG. 1 is an overall architecture diagram of the wheel slip system in an embodiment of the present invention, as shown in FIG. Fig.11 As shown, the wheel slip system includes three modules: first, a perception layer 10; second, a prediction layer 20; and third, a 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 posture perception module.

[0128] The perception layer 10 mainly implements the following functions:

[0129] 1) The road type perception module uses the on-board intelligent sensor to identify and perceive the vehicle's driving environment and obtain road type perception information, such as urban roads, highways, rural roads, cement roads, gravel roads, water crossings, icy and snowy roads, unpaved roads, etc.

[0130] 2) Environmental perception module, which senses the vehicle's surrounding environment through networked weather forecasts, on-board temperature sensors, rainfall and ambient light sensors, etc., and obtains environmental perception information such as temperature, altitude, wind speed, weather, slope, etc.

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

[0132] 4) The posture perception module senses the position of the vehicle in the geodetic coordinate system through the GPS positioning system and the inertial navigation system, inputs the vehicle's environmental information into the map system, and inputs it into the road adhesion coefficient experience database for retrieval to improve the retrieval accuracy. It is also used to sense the wheel speed, reference vehicle speed, six-way acceleration, steering wheel angle, and driving braking torque, i.e., required torque.

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

[0134] Here, the perception information 14 corresponding to the vehicle posture perception module includes at least: wheel speed, reference vehicle speed, six-way 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 a preset time domain, i.e. the wheel vertical dynamic load prediction.

[0135] The local database and the cloud online database 18 are connected via the network and updated bidirectionally periodically. If other connected vehicles travel over a new road surface or a new key target that is not recorded in the cloud database and learn to identify the adhesion coefficient of the road surface or the new key target, the connected vehicle will synchronize the information to the cloud database. After the cloud performs calculations, integration and confirmation, the relevant information will be updated and published to all connected vehicles for retrieval by the connected vehicles.

[0136] Through radar and camera, multiple static targets are identified and tracked 17, and a static target coordinate system is established. The historical speed of the vehicle is calculated in the static target coordinate system 16, and then the compensation speed is estimated based on the delay time of the radar and camera system, the vehicle mass, and the road adhesion of each wheel, that is, delay compensation is performed 15, and then the reference speed is calculated based on the historical speed and the compensation speed. Subsequently, the slip rate of each wheel is calculated based on the reference speed and the wheel speed of each wheel, and then the slip status of the four wheels is monitored in real time based on the slip rate.

[0137] The prediction layer 20 mainly implements the following functions:

[0138] 1) Vehicle trajectory prediction. The vehicle trajectory in the human driving scenario is predicted based on the first driving information, i.e., the vehicle slip state, the first reference speed, the steering wheel angle, the road adhesion of each wheel, the wheel end driving force braking torque, the yaw torque, etc.; the vehicle trajectory in the self-driving scenario is predicted based on the first driving information, i.e., the vehicle slip state, the automatic driving planning control output, etc. Among them, the parameters of the automatic driving planning control output include but are not limited to lateral deviation, heading angle, curvature, longitudinal distance, speed, and acceleration.

[0139] 2) Wheel driving trajectory prediction. According to the vehicle coordinates, reference vehicle speed, vehicle steering wheel angle, navigation road information, self-driving planning information, vehicle driving trajectory prediction and basic vehicle parameters (such as wheelbase, track width, suspension steering system parameters such as direction speed ratio), i.e., the second driving information, the form trajectory of each wheel is predicted, and the driving trajectory of all wheels 22 is predicted, including the left front wheel driving trajectory prediction, the right front wheel driving trajectory prediction, the left rear wheel driving trajectory prediction and the right rear wheel driving trajectory prediction.

[0140] 3) The predicted driving trajectories 22 of all wheels are input into the second road adhesion coefficient distribution map 13 under the self-vehicle coordinates, and the road adhesion coefficient change curve of each wheel in the preset time domain is predicted. Then, the vertical force time domain change curve of each wheel is predicted according to the self-vehicle posture perception information. The road adhesion coefficient change curve and the wheel vertical force time domain change curve 23 are combined to finally predict the first road adhesion curve TrcX(Fx,t)24 of each wheel in the preset time domain, including the first road adhesion of the left front wheel in the preset time domain, the first road adhesion of the right front wheel in the preset time domain, the first road adhesion of the left rear wheel in the preset time domain, and the first road 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 slip time prediction 31. The first time when the target wheel may slip is predicted based on the road adhesion curve TrcX(Fx,t) 24 of each wheel output by the prediction layer 20 and the wheel end driver demand torque 32. The number of the target wheels is one or more.

[0143] 2) Minimum change gradient 32. The minimum change gradient 32 is determined 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.

[0144] 3) Electric drive and brake system wheel-end torque distribution 34. The wheel end determines how the electric drive and brake system coordinate to meet the wheel-end target torque according to the power configuration (front drive, rear drive, four-wheel drive, distributed drive, etc.) to achieve the electric drive and brake system wheel-end torque distribution. Specifically, it can be based on the minimum change gradient, controlling the electric drive torque execution 35 or the brake torque execution 36 corresponding to the target wheel to execute the output torque from the current torque to smoothly approach the target torque. The target torque is the first road adhesion of the target wheel at the first time.

[0145] 4) Slip rate-based feedback control 37, specifically refers to determining the slip rate of the target wheel based on the second reference vehicle speed at the first time and the wheel speed of the target wheel; and determining whether the target wheel is slipping based on the slip rate of the target wheel and the target slip rate.

[0146] 5) Monitor the execution details of feedback control 38. That is, monitor the slip rate changes of each wheel in real time. If the front-end predictive control algorithm is accurate, the slip rate is in a stable state and the wheel will not slip. If there is a deviation in the front-end predictive control link and the wheel slips, the closed-loop control algorithm is quickly activated to quickly suppress the wheel slip through the slip rate closed-loop torque reduction to restore the vehicle to a steady state. Specifically, the closed-loop torque reduction of the slip rate quickly suppresses the wheel slip, which specifically refers to adjusting the output torque of the electric drive and brake modules corresponding to the target wheel based on the slip rate of the target wheel so that the target wheel does not slip.

[0147] 6) Correcting the road adhesion coefficient empirical database 39. 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 prediction module, and the perception prediction module corrects the local and cloud road adhesion coefficient empirical database in real time according to the back-end feedback.

[0148] based on Fig.11 The overall architecture diagram of the wheel slip system shown in the figure, the embodiment of the present invention provides a wheel slip control process, Fig.12 The process diagram of the anti-skid control method in the embodiment of the present invention is shown in FIG. Figure 10 One, such as Fig.12 As shown, the following steps are included:

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

[0150] The driver or the automatic driving system starts driving the vehicle. The human driver operates the accelerator or brake, and the self-driving system sends the wheel-end required driving torque or braking required torque to the basic vehicle braking and drive system in real time through the system's planning control module.

[0151] S41: Acquire first perception information.

[0152] The vehicle perception module perceives the road type, environmental information, key targets, and vehicle position in real time to obtain the first perception information.

[0153] S42: Importing the first perception information into an empirical database of road adhesion coefficient.

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

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

[0156] S44: Vehicle driving trajectory prediction.

[0157] Based on the first driving information, the vehicle's driving trajectory is predicted. Specifically, the vehicle's driving trajectory in a human driving scenario is predicted based on the vehicle's skidding state, reference vehicle speed, direction angle, road adhesion of each wheel, wheel-end driving braking torque, yaw torque, etc.; the vehicle's driving trajectory in a self-driving scenario is predicted based on the vehicle's skidding state, automatic driving planning control output, etc.

[0158] S45: Predicting the driving trajectory of each wheel.

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

[0160] S46: Road adhesion coefficient of each wheel under the driving trajectory.

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

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

[0163] Based on the posture perception information, the vertical force of each wheel in the preset time domain, that is, the vertical dynamic load, is predicted.

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

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

[0166] S49: Slip time prediction.

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

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

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

[0170] S51: Control according to the torque requirement of each wheel.

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

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

[0173] S53: Check whether the target wheel is slipping.

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

[0175] Monitor the target wheel slip rate in real time. If the front-end predictive control algorithm is accurate, the slip rate is stable and the target wheel will not slip. If the front-end predictive control link deviates and the target wheel slips, the closed-loop control algorithm is activated quickly to quickly suppress the target wheel slip through the slip rate closed-loop torque reduction to restore the vehicle to a steady state. Based on the slip rate deviation between the target wheel slip rate and the target slip rate, adjust the output torque of the electric drive and brake modules corresponding to the target wheel.

[0176] S55: Cloud online database.

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

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

[0179] In this way, by predicting the moment when a certain wheel will slip in the future and controlling the driving and braking torque of the corresponding wheel in advance, the wheel torque is smoothed to the predicted adhesion boundary before the predicted slip occurs, which not only fully utilizes the ground adhesion, but also prevents the vehicle from becoming unstable due to wheel slip, and ensures driving comfort. If there is a deviation in the prediction of the wheel slip moment, and the wheel slips when driving on the corresponding road surface, the wheel-end closed-loop torque control algorithm based on the slip rate will be automatically activated to quickly suppress the wheel slip. At the same time, the road surface information where the wheel slips is fed back to the perception layer and the prediction layer. After the perception layer and the prediction layer perform learning and correction, the vehicle and other connected vehicles will not slip when they encounter the same road surface next time. As the number of vehicles and mileage using this algorithm increases, 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] The present invention proposes a reference vehicle speed calculation method based on static target recognition and static target coordinate system. The method is based on radar camera static target recognition, constructs a static target coordinate system based on multiple static targets, and measures the "historical vehicle speed" of the vehicle at the previous moment in the static target coordinate system. At the same time, the system delay of the camera and radar is considered, and the change ΔV of the vehicle speed within the system delay Δt time is predicted. Finally, the historical vehicle speed is superimposed on the system delay compensation vehicle speed to obtain the reference vehicle speed at the current moment. Specific implementation methods are as follows Fig.13 The reference speed calculation process is shown below:

[0181] S60: Multiple static target recognition.

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

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

[0184] That is, the distance between each static target and the vehicle at the current time is determined, and the distance between each static target and the vehicle at the slip time, that is, the first time, is determined.

[0185] S62: The vehicle coordinate system is transformed into the static target coordinate system.

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

[0187] S64: Obtaining the first road adhesion of each wheel during the slipping time.

[0188] S65: Calculate the vehicle speed that needs to be compensated for system delay based on the vehicle posture information.

[0189] Specifically, the sum of the first road adhesion of each wheel during the slip time is determined, and then the preset resistance is subtracted to obtain the longitudinal force of the vehicle; the ratio of the longitudinal force of the vehicle to the vehicle mass and the coefficient delay time is used to obtain the compensated vehicle speed. The preset resistance includes but is not limited to slope resistance, rolling resistance, acceleration resistance and wind resistance. The specific resistance can be determined based on the vehicle posture information.

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

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

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

[0193] An embodiment of the present invention provides an anti-skid control device. Fig.14 FIG. 1 is a schematic diagram of the structure of the anti-skid control device in an embodiment of the present invention. Fig.14 As shown, the anti-skid control device 140 includes:

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

[0195] The prediction unit 1401 is further configured to predict a first time when the target wheel slips based on the first road 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 adhesion corresponding to the first time is used as the target torque.

[0197] In an embodiment of the present invention, based on the predicted first time when the target wheel slips, the output torque of the electric drive and braking module corresponding to the target wheel is controlled in advance to smoothly approach the corresponding target torque from the current torque, thereby preventing the wheel from slipping and the vehicle from becoming unstable, thereby ensuring the comfort and safety of vehicle driving.

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

[0199] In some embodiments, the control unit 1402 is also used to determine a minimum change gradient based on the first road adhesion of the target wheel at the current time and the first road adhesion of the target wheel at the first time; 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 used to determine a road adhesion difference based on the first road adhesion of the target wheel at the current time and the first road adhesion of the target wheel at the first time; and determine the minimum change gradient based on the road adhesion difference and the time difference between the current time and the first time.

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

[0202] In some embodiments, the prediction unit 1401 is also used to obtain 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; based on the correspondence between the perception information and the road adhesion coefficient distribution map, determine the first road adhesion coefficient distribution map corresponding to the first perception information; convert the second road adhesion coefficient distribution map in the world coordinate system into the first road adhesion coefficient distribution map in the vehicle coordinate system.

[0203] In some embodiments, the prediction unit 1401 is also used to predict the vehicle driving trajectory based on the first driving related information; and predict the predicted driving trajectory of each wheel in the self-vehicle coordinate system based on the vehicle 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 includes but is not limited to the vehicle slip state, the first reference vehicle speed, the steering wheel angle, the second road adhesion of each wheel, the required torque of each wheel, and the yaw moment; the second driving related information includes but is not limited to the position of each wheel in the self-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 also included, which is used to determine the slip rate 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 rate of the target wheel and the target slip rate; when it is determined that the target wheel is slipping, adjust the output torque of the electric drive and braking module corresponding to the target wheel based on the slip rate of the target wheel so that the target wheel does not slip.

[0205] In some embodiments, a determination unit is also included for 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 a recognition and tracking system; the longitudinal resultant force of the vehicle is determined based on the first road adhesion and a preset resistance of each wheel at the first time; the compensated vehicle speed is determined based on the longitudinal resultant force of the vehicle, the vehicle mass and the delay time of the recognition and tracking system; and the second reference vehicle speed is determined based on the historical vehicle speed and the compensated vehicle speed.

[0206] In some embodiments, the monitoring unit is also used to determine the slip rate deviation of the target wheel based on the slip rate of the target wheel and the target slip rate; determine the torque adjustment value of the target wheel based on the slip rate 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 also used to determine a proportional control term based on the slip rate deviation and the proportional coefficient of the target wheel; determine an integral control term based on the slip rate deviation and the integral coefficient of the target wheel; determine a differential control term based on the slip rate deviation and the differential 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 differential control term.

[0208] In some embodiments, an updating unit is further included, which is used to update the first road adhesion coefficient distribution map based on the slip rate deviation of the target wheel and the driving trajectory coordinates of the target wheel.

[0209] The embodiment of the present invention further provides another vehicle, Fig.15 FIG. 1 is a schematic diagram of a vehicle structure in an embodiment of the present invention. Fig.15 As shown, the vehicle 150 includes: a processor 1501 and a memory 1502 configured to store a computer program that can be run on the processor;

[0210] The processor 1501 is configured to execute the method steps in the aforementioned embodiment when running a computer program.

[0211] Of course, in practical applications, Fig.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 achieve connection and communication between these components. In addition to the data bus, the bus system 1503 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Fig.15 Various buses are labeled as bus system 1503.

[0212] In practical applications, the processor may be at least one of an application-specific integrated circuit (ASIC), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a controller, a microcontroller, and a microprocessor. It is understandable that for different devices, the electronic device used to implement the functions of the processor may also be other, and the embodiments of the present invention do not specifically limit this.

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

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

[0215] Optionally, the computer-readable storage medium can be applied to any one of the methods in the embodiments of the present invention, and the computer program enables the computer to execute the corresponding processes implemented by the processor in each method in the embodiments of the present invention. For the sake of brevity, they are not described here.

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

[0217] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0218] In addition, all functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units. A person of ordinary skill in the art may understand that all or part of the steps of implementing the above method embodiments may be completed by hardware related to program instructions, and the above program may be stored in a computer-readable storage medium, which, when executed, executes the steps of the above method embodiments; and the above storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

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

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

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

[0222] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An anti-skid control method, characterized in that: The anti-skid control method comprises: Predicting the first road adhesion force of each wheel in a preset time domain based on the first road 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 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 posture perception information; Predicting the first time when the target wheel slips based on the first road adhesion of each wheel in a preset time domain and the required torque of each wheel; Based on the first time when the target wheel slips, the output torque of the electric drive and brake modules corresponding to the target wheel is controlled to smoothly approach the target torque from the current torque; wherein the first road adhesion corresponding to the first time is used as the target torque.

2. The anti-skid control method according to claim 1, characterized in that: The predicting the first time when the target wheel slips based on the first road adhesion of each wheel in a preset time domain and the required torque of each wheel includes: When it is determined that the road adhesion of the target wheel in the first time domain range is less than the required torque, the start time of the first time domain range is used as the first time when the target wheel slips.

3. The anti-skid control method according to claim 1 or 2, characterized in that: The step of controlling the output torque of the electric drive and brake 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 comprises: determining a minimum change gradient based on the first road adhesion of the target wheel at a current time and the first road adhesion of the target wheel at the first time; Based on the minimum change gradient, the output torque of the electric drive and brake modules corresponding to the target wheel is controlled to smoothly approach the target torque from the current torque.

4. The anti-skid control method according to claim 3, characterized in that: The determining the minimum change gradient based on the first road adhesion of the target wheel at the current time and the first road 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; The minimum change gradient is determined based on the road adhesion difference and a time difference between the current time and the first time.

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

6. The anti-skid control method according to claim 1 or 2, characterized in that: The anti-skid control method further comprises: Acquire first perception information; wherein 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; Determining a second road surface adhesion coefficient distribution map corresponding to the first perception information based on a correspondence between the perception information and the road surface adhesion coefficient distribution map; The second road adhesion coefficient distribution map in the world coordinate system is converted into the first road adhesion coefficient distribution map in the vehicle coordinate system.

7. The anti-skid control method according to claim 1 or 2, characterized in that: The anti-skid control method further comprises: Predicting a vehicle driving trajectory based on the first driving related information; Based on the vehicle driving trajectory and the second driving related information, predicting the predicted driving trajectory of each wheel in the vehicle coordinate system; Among them, the vehicle 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 the vehicle slip state, the first reference vehicle speed, the steering wheel angle, the second road adhesion of each wheel, the required torque of each wheel, and the yaw moment, the second driving related information includes but is 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.

8. The anti-skid control method according to claim 1 or 2, characterized in that: After the output torque of the electric drive and brake modules corresponding to the target wheel is controlled to smoothly approach the required torque from the current torque, the anti-skid control method further includes: determining a slip ratio of the target wheel based on a second reference vehicle speed and the wheel speed of the target wheel; Determining whether the target wheel is slipping based on the slip rate of the target wheel and a target slip rate; When it is determined that the target wheel is slipping, the output torque of the electric drive and brake module corresponding to the target wheel is adjusted based on the slip rate of the target wheel so that the target wheel does not slip.

9. The anti-skid control method according to claim 8, characterized in that: The anti-skid control method further comprises: Determine 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 a recognition and tracking system; determining a vehicle longitudinal resultant force based on a first road surface adhesion force and a preset resistance force of each wheel at the first time; Determining a compensation vehicle speed based on the longitudinal force of the vehicle, the mass of the vehicle, and the delay time of the identification and tracking system; The second reference vehicle speed is determined based on the historical vehicle speed and the compensated vehicle speed.

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

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

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

13. An anti-skid control device, characterized in that: The anti-skid control device comprises: A prediction unit, configured to predict the first road adhesion force of each wheel in a preset time domain based on the first road 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 the vehicle driving related information, and the vertical force of each wheel in the preset time domain is predicted based on the posture perception information; The prediction unit is further used to predict the first time when the target wheel slips based on the first road adhesion of each wheel in a preset time domain and the required torque of each wheel; A 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 adhesion corresponding to the first time is used as the target torque.

14. A vehicle, characterized in that: The vehicle comprises: a processor and a memory configured to store a computer program executable on the processor, Wherein, the processor is configured to execute the steps of the anti-skid control method according to any one of claims 1 to 12 when running the computer program.

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

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

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