Vehicle control method, system and equipment, storage medium and vehicle

Abstract

The invention provides a vehicle control method, system and device, a medium and a vehicle. The method comprises the following steps: acquiring a first deceleration value, a target deceleration value and an execution braking air pressure value of a target vehicle; in response to the fact that a first difference value between the first deceleration value and the target deceleration value is larger than a preset threshold value, a second deceleration value is determined according to the first deceleration value and vehicle weight information and operation gradient information of the target vehicle; a brake control model is optimized according to the execution brake air pressure value and the second deceleration value, and an optimized brake control model is obtained; and according to the optimized brake control model, the target deceleration value and the vehicle weight information and the operation gradient information of the target vehicle, determining the target brake air pressure of the target vehicle. According to the embodiment of the invention, accurate braking of the automatic driving vehicle can be realized, so that the operation safety is improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control, and specifically relates to a vehicle control method, system, device, storage medium (computer-readable storage medium), and vehicle. Background Art

[0002] With the rapid development of intelligent driving, the demand for precise vehicle control is becoming increasingly strong.

[0003] Most of the existing braking system algorithms currently add a self-learning function, which continuously and dynamically learns and adjusts braking data during multiple brakings of the vehicle. For example, the wheel-end air pressure execution value. However, the disadvantage of this self-learning algorithm is that it completely depends on the previous braking execution situation and cannot fully consider other influencing conditions that may disappear during the next braking. Summary of the Invention

[0004] Embodiments of the present invention provide a vehicle control method, system, device, storage medium, and vehicle to solve the technical problem of the relatively low operating safety of existing vehicle braking control solutions.

[0005] On the one hand, embodiments of the present application provide a vehicle control method, including:

[0006] Obtain the first deceleration value, target deceleration value, and execution braking air pressure value of the target vehicle;

[0007] In response to the first difference between the first deceleration value and the target deceleration value being greater than a preset threshold, determine a second deceleration value according to the first deceleration value, the vehicle weight information, and the running slope information of the target vehicle;

[0008] Optimize the braking control model according to the execution braking air pressure value and the second deceleration value to obtain an optimized braking control model;

[0009] Determine the target braking air pressure of the target vehicle according to the optimized braking control model, the target deceleration value, the vehicle weight information, and the running slope information of the target vehicle.

[0010] On the second hand, embodiments of the present application provide a vehicle control system. The system includes an autonomous driving system and a braking system, where:

[0011] The autonomous driving system is configured to determine the target deceleration value; determine the first deceleration value of the target vehicle according to the running detection data of the target vehicle; and send the first deceleration value and the target deceleration value to the braking system;

[0012] A braking system, which is configured to determine a second deceleration value according to a first deceleration value, vehicle weight information of a target vehicle, and running slope information when a first difference between the first deceleration value and a target deceleration value is greater than a preset threshold; optimize a braking control model according to an execution braking air pressure value and the second deceleration value to obtain an optimized braking control model; and determine a target braking air pressure of the target vehicle according to the optimized braking control model, the target deceleration value, vehicle weight information of the target vehicle, and running slope information.

[0013] In a third aspect, an embodiment of the present application provides a vehicle control device, which includes:

[0014] One or more processors;

[0015] A memory; and

[0016] One or more applications, where the one or more applications are stored in the memory and configured to be executed by the processor to implement the steps in the above vehicle control method.

[0017] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which a computer program is stored, and the computer program is loaded by a processor to execute the steps in the above vehicle control method.

[0018] In a fifth aspect, an embodiment of the present application provides a vehicle, which includes the above vehicle control system.

[0019] Advantages of the embodiments of the present application:

[0020] Compared with the vehicle braking method based on the self-learning function in the prior art, in this embodiment, a first deceleration value, a target deceleration value, and an execution braking air pressure value of a target vehicle can be first obtained. When a first difference between the first deceleration value and the target deceleration value is greater than a preset threshold, in combination with vehicle weight information and running slope information of the target vehicle, a second deceleration value of the target vehicle is calculated, and the braking control model is optimized according to the second deceleration value and the execution braking air pressure value, avoiding braking air pressure prediction errors caused by factors such as vehicle weight and running slope, and problems such as insufficient vehicle braking or braking overshoot. Furthermore, the optimized braking control model can be used to accurately calculate the target braking air pressure corresponding to the target deceleration value, realizing accurate braking of the vehicle, and thus ensuring the operation safety of the autonomous vehicle. Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those skilled in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0022] Figure 1 is the first process schematic diagram provided in the embodiments of the present disclosure;

[0023] Figure 2 is the second process schematic diagram provided in the embodiments of the present disclosure;

[0024] Figure 3 is the second process schematic diagram provided in the embodiments of the present disclosure;

[0025] Figure 4 is the structural schematic diagram of the vehicle control system provided in the embodiments of the present disclosure;

[0026] Figure 5 is the structural schematic diagram of the vehicle control device provided in the embodiments of the present disclosure. Detailed Embodiments

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present invention.

[0028] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0029] In this application, the term "exemplary" is used to mean "serving as an example, illustration, or instance". Any embodiment described as "exemplary" in this application is not necessarily to be construed as more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the present invention. In the following description, details are set forth for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed in this application.

[0030] It can be understood that large vehicles, such as trucks, buses, and other heavy vehicles, generally use pneumatic brakes, which ensure smooth braking force, reliable operation, and less wear on the brakes.

[0031] Taking the scenario of an autonomous truck precisely parking or reversing as an example, the autonomous truck includes an autonomous driving system and a braking system. The control of braking for the autonomous truck generally involves the autonomous driving system sending a deceleration control instruction, and the braking system performing different wheel-end pneumatic responses according to the deceleration value to generate corresponding braking forces. However, since the weight of the truck varies greatly due to different load ranges, the same wheel-end pneumatic value will produce different deceleration values under different vehicle weights, and the specific wheel-end pneumatic value cannot accurately correspond to the different deceleration values actually executed by the vehicle.

[0032] The autonomous driving system is a complex system including multiple components and levels, usually divided into two levels: the upper computer and the lower computer. Among them, the upper computer is the high-level control part of the autonomous driving system, responsible for making decisions and planning the entire driving process. It usually makes high-level decisions such as path planning, traffic decision-making, and behavior planning based on sensor data, map information, user input, etc. The lower computer is the low-level control part of the autonomous driving system, responsible for executing the decisions made by the upper computer and controlling the underlying actions of the vehicle. According to the plan of the upper computer, the lower computer generates corresponding instructions and sends them to the vehicle's power system, braking system, and steering system to enable the vehicle to implement the behavior planned by the upper computer.

[0033] Therefore, according to the background technology description of this application, most of the existing braking system algorithms currently will add a self-learning function for wheel-end pneumatic execution, and continuously dynamically learn and adjust the wheel-end pneumatic execution value during multiple brakings of the vehicle. However, the disadvantage of this self-learning algorithm is that it completely depends on the previous braking execution situation and cannot effectively isolate other influencing conditions that may disappear during the next braking.

[0034] The other influencing conditions are mainly manifested in the following two aspects:

[0035] 1) Influence of road surface gradient: Taking the EBS (Electronic Braking System) braking compensation algorithm of a vehicle as an example, this compensation algorithm does not take road conditions as one of the variables for training. If the vehicle was on an uphill slope during the previous braking execution, since the vehicle weight will generate a certain amount of resistance to the vehicle, compared with a horizontal road surface, the same braking air pressure will make the vehicle show a greater deceleration. The braking system will wrongly think that the current braking air pressure can produce a greater deceleration effect. Then, when the vehicle brakes next time, if the required deceleration sent by the autonomous driving system remains unchanged, the braking system will reduce the braking air pressure value. When the road conditions change (such as driving from an uphill slope to a horizontal road surface), there may be a situation of insufficient braking. On the contrary, if the vehicle was on a downhill slope during the previous braking execution, after accounting for the braking effect of this time into the self-learning algorithm, then when the road conditions where the vehicle is located change (such as driving from a downhill slope to a horizontal road surface), there may be a situation of overshoot braking during the next braking.

[0036] 2) Influence of whether the clutch is disengaged: The current logic of the speed at which the vehicle clutch disengages quickly will refer to the degree of change in vehicle speed. If it is detected that the vehicle speed changes sharply, the clutch will disengage faster, that is, it will disengage faster when the deceleration is relatively large. On this basis, if the current deceleration request is small, resulting in a relatively long time for the vehicle clutch to disengage or it cannot be completely disengaged, there will still be part of the engine's idling driving force output during vehicle braking. Due to the existence of this part of the idling driving force, the deceleration shown by the vehicle with the current braking air pressure will become smaller. Therefore, the self-learning function of the braking system will cause the braking system to increase the braking air pressure value during the next braking, thus resulting in a situation of overshoot braking.

[0037] Generally speaking, ignoring the above factors during the self-learning process of the braking system algorithm will lead to a relatively low control accuracy of autonomous driving vehicles, and further lead to relatively low operation safety, especially having a greater impact on vehicle braking in scenarios such as precise parking or reversing of autonomous driving vehicles.

[0038] Therefore, to solve the above problems, the present application proposes a vehicle control method, system, device, computer-readable storage medium, and vehicle.

[0039] Referring to Figure 1 , Figure 1 is a schematic flowchart of an embodiment of the vehicle control method in an embodiment of the present application. The vehicle control method in this embodiment can be applied to the braking system of a vehicle. This vehicle control method includes the following S10 - S40:

[0040] S10, obtain the first deceleration value, target deceleration value, and execution braking air pressure value of the target vehicle;

[0041] In this embodiment, the target vehicle can be a vehicle using pneumatic braking, such as a truck or a bus, and the target vehicle can include an autonomous driving system and a braking system.

[0042] On this basis, the braking system can obtain the first deceleration value, the target deceleration value, and the execution braking air pressure value of the target vehicle. Among them, the first deceleration value is the actual deceleration value shown by the vehicle, the target deceleration value is the required deceleration value calculated by the autonomous driving system according to the current driving requirements, and the execution braking air pressure value is the braking air pressure value actually executed by the braking system.

[0043] Specifically, for example, the autonomous driving system can collect the speed of the vehicle in real time through sensors in the target vehicle. For example, the relative speed of the vehicle measured by the vehicle radar, or the relative speed of the vehicle can also be calculated through on-vehicle GPS or IMU data. Furthermore, the change in speed can be calculated by comparing consecutive relative speeds of the vehicle, thereby obtaining the deceleration (i.e., the first deceleration value in this embodiment). Or, in this embodiment, the vehicle wheel speed can also be measured by a wheel speed meter, and then the first deceleration value can be obtained through differential calculation. Or, the first deceleration value of the target vehicle can be directly collected through an acceleration sensor. The determination method of the vehicle deceleration value can be selected according to actual needs and is not limited here.

[0044] In addition, the autonomous driving system can calculate the overall deceleration value required by the vehicle (i.e., the target deceleration value in this embodiment) in real time according to the current operating state of the target vehicle. For example, the deceleration required for the target vehicle to decelerate to the target vehicle speed, or the deceleration required for the target vehicle to stop at a certain position.

[0045] Furthermore, the autonomous driving system can send the above calculated first deceleration value and target deceleration value to the braking system of the target vehicle.

[0046] The braking system can calculate the execution braking air pressure value corresponding to the target deceleration value according to the first deceleration value and the target deceleration value of the target vehicle sent by the autonomous driving system, and can also calculate according to the braking force required by the target vehicle and the brake parameters (such as the brake pressure-braking force relationship curve).

[0047] S20, in response to a first difference between the first deceleration value and the target deceleration value being greater than a preset threshold, determine a second deceleration value according to the first deceleration value, the vehicle weight information, and the running slope information of the target vehicle;

[0048] In this embodiment, the braking system can obtain the first deceleration value, the target deceleration value, and the execution braking air pressure value of the target vehicle.

[0049] According to the above description, ignoring the road surface gradient and the clutch disconnection during the self-learning process of the braking system algorithm will result in a low control accuracy of the autonomous vehicle, and further lead to a low operation safety. Therefore, in this embodiment, the braking system can calculate a first difference between the first deceleration value and the target deceleration value, and determine whether the first difference is greater than a preset threshold. If the first difference is greater than the threshold, it means that factors such as the road surface gradient or the clutch disconnection affect the vehicle's air braking. The target vehicle needs to eliminate the interference of the above factors. Therefore, the braking system can calculate a second deceleration value of the target vehicle according to the first deceleration value, the vehicle weight information, and the running gradient information of the target vehicle.

[0050] Among them, the vehicle weight information and the running gradient information of the target vehicle can be calculated by the autonomous driving system and sent to the braking system. For example, when the target vehicle is accelerating, the autonomous driving system can calculate the acceleration of the target vehicle, and based on this acceleration, the inclination degree of the target vehicle relative to the ground can be inferred, so as to calculate the running gradient information. In addition, the driving force of the current vehicle can also be obtained by subtracting the resistance generated by wind resistance, friction resistance, and vehicle weight from the torque value actually executed by the engine, and the current vehicle weight information of the target vehicle can be estimated in real time according to Newton's second law F = ma.

[0051] In another embodiment, if the first difference between the first deceleration value and the target deceleration value is less than or equal to the preset threshold, the braking system can perform self-learning according to the above-mentioned execution braking air pressure value and the target deceleration value.

[0052] Specifically, the target vehicle can be made to drive on the same road surface, and the actual deceleration values shown by the vehicle under the same target deceleration value requested by the autonomous driving system in the no-load and full-load situations are tested. Then, the difference between the actual deceleration value of the vehicle under full load and the actual deceleration value of the vehicle under no load is used as the above-mentioned preset threshold.

[0053] S30. Optimize the braking control model according to the execution braking air pressure value and the second deceleration value to obtain an optimized braking control model;

[0054] It should be noted that the braking system can execute the wheel-end air pressure self-learning function and continuously dynamically learn and adjust the wheel-end air pressure execution value during multiple brakings of the vehicle. Therefore, in this embodiment, an initial braking control model can be constructed, and the mapping relationship between the deceleration value that the braking system can generate and the braking air pressure value can be included in the braking control model. Through this braking control model, the braking system can obtain the braking air pressure value corresponding to the deceleration value of the target vehicle and brake the vehicle according to this braking air pressure value.

[0055] In this embodiment, in order to achieve precise control of an autonomous vehicle, after calculating the second deceleration value, the control system can optimize the braking control model according to the above-mentioned execution braking air pressure value and the second deceleration value to obtain an optimized braking control model.

[0056] It can be understood that in this embodiment, optimizing the braking control model is actually optimizing the mapping relationship between the deceleration value that the braking system can generate and the braking air pressure value, eliminating the influence of road surface slope and clutch disconnection on the above mapping relationship, so that there can be a strict mapping relationship between the deceleration value that the braking system can generate and the braking air pressure value.

[0057] S40. Determine the target braking air pressure of the target vehicle according to the optimized braking control model, the target deceleration value, and the vehicle weight information and running slope information of the target vehicle.

[0058] In this embodiment, after the braking system optimizes the initial braking control model to obtain an optimized braking control model, it can calculate the target braking air pressure of the target vehicle according to the optimized braking control model, the target deceleration value, and the vehicle weight information and running slope information of the target vehicle.

[0059] It can be understood that in this embodiment, the target braking air pressure calculated by the braking system takes into account the influence of factors such as road surface slope or clutch disconnection on the deceleration value compared to the above-mentioned execution braking air pressure value, so that when the weight of the target vehicle changes, the execution of the braking air pressure can be adjusted in real time to meet the deceleration requirements under different vehicle weights. For example, if the vehicle is on an uphill slope this time, the braking system can determine the braking air pressure required to meet the vehicle deceleration request according to the correspondence between deceleration and braking air pressure in the optimized braking control model, avoiding the resistance generated by the vehicle weight when the vehicle is on an uphill slope from affecting the finally presented deceleration value of the vehicle, and making the braking control more precise.

[0060] It should be noted that if the braking control model before optimization is adopted in this embodiment, the self-learning function of the braking system will use the deceleration value shown by the vehicle during learning. Due to the existence of ramp resistance, the deceleration shown by the vehicle will be greater than the deceleration value generated by the braking system through the braking air pressure, so that the self-learning function of the braking system will think that a greater deceleration value can be generated by adopting the current braking air pressure. Therefore, when facing the same target deceleration request next time, the braking system may generate a smaller execution air pressure accordingly, resulting in insufficient braking.

[0061] Therefore, compared with the vehicle braking method based on the self-learning function in the prior art, in this embodiment, the first deceleration value, the target deceleration value, and the execution braking air pressure value of the target vehicle can be obtained first. When the first difference between the first deceleration value and the target deceleration value is greater than a preset threshold, the vehicle weight information and the running slope information of the target vehicle are combined to calculate the second deceleration value of the target vehicle. Then, according to the second deceleration value and the execution braking air pressure value, the braking control model is optimized, avoiding the braking air pressure prediction error caused by factors such as vehicle weight and running slope, and problems such as insufficient vehicle braking or braking overshoot. Furthermore, the optimized braking control model can be used to accurately calculate the target braking air pressure corresponding to the target deceleration value, realizing accurate vehicle braking, and thus ensuring the operation safety of autonomous vehicles.

[0062] In one embodiment, in the above S10, "obtaining the first deceleration value, the target deceleration value, and the execution braking air pressure value of the target vehicle" may include:

[0063] S101, receiving the first deceleration value of the target vehicle determined by the autonomous driving system according to the running detection data of the target vehicle;

[0064] S102, in response to the deceleration control instruction of the autonomous driving system, determining the target deceleration value and the execution braking air pressure value.

[0065] It should be noted that in this embodiment, the autonomous driving system of the target vehicle can calculate the first deceleration value of the target vehicle according to the running detection data, where the running detection data may include the actual speed of the target vehicle.

[0066] For example, the autonomous driving system can measure the relative speed of the vehicle through the vehicle radar, or calculate the relative speed of the vehicle through the vehicle position data collected by the on-vehicle GPS or IMU (inertial measurement unit), and then calculate the change in speed to obtain the deceleration value of the target vehicle (i.e., the first deceleration value in this embodiment). In addition, in this embodiment, the first deceleration value of the target vehicle can also be collected by an acceleration sensor. It can be understood that the first deceleration value of the target vehicle is actually a performance parameter shown by the whole vehicle during driving.

[0067] On this basis, the autonomous driving system can send the calculated first deceleration value to the braking system of the target vehicle.

[0068] It should be noted that in addition to receiving the first deceleration value calculated by the autonomous driving system, the braking system can also calculate the above first deceleration value according to the received running detection data of the target vehicle. It can be specifically configured according to actual needs and is not limited here.

[0069] In addition, the autonomous driving system can trigger a deceleration control instruction during driving and send it to the braking system. The braking system can respond to the above deceleration control instruction and obtain the target deceleration value corresponding to the deceleration control instruction and the corresponding execution braking air pressure value.

[0070] Specifically, for example, the autonomous driving system can calculate the overall deceleration value required by the vehicle (i.e., the target deceleration value in this embodiment) in real time according to the current operating state of the target vehicle. For example, the deceleration required for the target vehicle to decelerate to the target vehicle speed, or the deceleration required for the target vehicle to stop at a certain position, and trigger the corresponding deceleration control instruction.

[0071] The braking system can respond to the above deceleration control instruction, obtain the target deceleration value of the target vehicle, and can calculate the execution braking air pressure value corresponding to the target deceleration value according to the braking force required by the target vehicle and the brake parameters (such as the brake pressure-braking force relationship curve).

[0072] It can be seen that in this embodiment, it can be ensured that the execution air pressure value of the braking system can meet the request of the autonomous driving system for the deceleration value. That is, the autonomous driving system can send the estimated values of the vehicle weight and the road surface slope to the braking system, and then the braking system calculates the possible vehicle resistance according to the current vehicle weight and the road surface slope, so as to remove this part of the deceleration value generated by the vehicle weight and the road surface slope when calculating the execution deceleration control instruction. The deceleration control instruction requested by the autonomous driving system no longer considers the influence generated by the vehicle weight and the road surface slope, but gives a total deceleration control instruction for the overall vehicle demand to be executed, which can avoid the problem of inaccurate deceleration execution caused by the repeated calculation of this part of the vehicle resistance by the autonomous driving system and the braking system.

[0073] In one embodiment, in the above S20, "determine the second deceleration value according to the first deceleration value, the vehicle weight information and the running slope information of the target vehicle" may include:

[0074] S201, determine the deceleration compensation value according to the vehicle weight information and the running slope information of the target vehicle;

[0075] S202, determine the second deceleration value according to the first deceleration value and the deceleration compensation value.

[0076] In this embodiment, the braking system can determine the deceleration compensation value according to the vehicle weight information and the running slope information of the target vehicle when it obtains the first deceleration value, the target deceleration value and the execution braking air pressure value of the target vehicle, and the first difference between the first deceleration value and the target deceleration value is greater than the preset threshold.

[0077] Specifically, for example, the braking system can calculate the resistance caused by environmental factors (including wind resistance, friction resistance, etc.), and then calculate the ramp resistance based on the vehicle weight information and the running slope information sent by the autonomous driving system, and calculate the deceleration value generated by the ramp resistance (i.e., the deceleration compensation value in this embodiment).

[0078] Furthermore, the braking system can determine the second deceleration value of the target vehicle according to the above first deceleration value and the deceleration compensation value. For example, subtract the deceleration compensation value from the first deceleration value to obtain the second deceleration value.

[0079] It should be noted that in this embodiment, according to the above description, the autonomous driving system can send the estimated vehicle weight information and running slope information to the braking system. The braking system calculates the possible vehicle resistance based on the vehicle weight information and the running slope information, so as to remove this part of the deceleration compensation value generated by the vehicle weight and the road surface slope when calculating and executing the deceleration control instruction. The deceleration control instruction requested by the autonomous driving system no longer considers the influence generated by the vehicle weight and the road surface slope, but gives a total deceleration control instruction for the overall vehicle demand to execute, which can avoid the problem of inaccurate deceleration execution caused by the repeated calculation of this part of the vehicle resistance by the autonomous driving system and the braking system.

[0080] For example, the autonomous driving system calculates that a target deceleration value of -1 m / s² is required to make the vehicle travel to the target state according to the current vehicle model, and the autonomous driving system also calculates that the deceleration compensation value generated by the road surface slope and the vehicle weight is -0.2 m / s². At this time, the autonomous driving system believes that only a deceleration control instruction including -0.8 m / s² needs to be requested from the braking system to meet the requirements. However, after the braking system receives the deceleration control instruction including -0.8 m / s², it calculates again the deceleration compensation value of -0.2 m / s² generated by the vehicle weight and the slope. Then the braking system determines that the deceleration value of -0.6 m / s² can be executed to meet the request of the autonomous driving system. In this way, the actual execution is significantly less than the deceleration request of the autonomous driving system.

[0081] In this embodiment, the autonomous driving system only calculates the target deceleration value required by the vehicle and sends it to the braking system. The braking system calculates the deceleration compensation value based on the vehicle weight information and the running slope information sent by the autonomous driving system, and then removes this part of the deceleration compensation value from the target deceleration value and executes the corresponding braking pressure, which can avoid the problem of insufficient final deceleration execution / excessive deceleration execution caused by the repeated calculation of the vehicle resistance by the autonomous driving system and the braking system.

[0082] Based on this, in the above S40, "determine the target braking air pressure of the target vehicle according to the optimized braking control model, the target deceleration value, and the vehicle weight information and operating gradient information of the target vehicle", as Figure 2 shown, it may include:

[0083] S401, determine a third deceleration value according to the target deceleration value and the deceleration compensation value;

[0084] S402, input the third deceleration value into the optimized braking control model to obtain the target braking air pressure.

[0085] In this embodiment, after the braking system optimizes the braking control model according to the execution braking air pressure value and the second deceleration value to obtain the optimized braking control model, it can use the optimized braking control model to calculate the target braking air pressure corresponding to the target deceleration value.

[0086] Specifically, for example, the braking system can subtract the obtained deceleration compensation value from the target deceleration value to obtain a third deceleration value. Furthermore, the third deceleration value can be input into the optimized braking control model to obtain the target braking air pressure output by the optimized braking control model.

[0087] It should be noted that in this embodiment, compared with the mapping relationship between the deceleration value and the braking air pressure before optimization in the optimized braking control model, the influence of vehicle weight and operating gradient on the vehicle deceleration value is eliminated, and an accurate mapping relationship between the deceleration value and the braking air pressure is achieved, avoiding problems such as insufficient braking or braking overshoot when the vehicle executes the braking air pressure, resulting in poor safety of the autonomous vehicle.

[0088] In one embodiment, in the above S30, "optimize the braking control model according to the execution braking air pressure value and the second deceleration value to obtain the optimized braking control model" may include:

[0089] S301, in response to the second difference between the second deceleration value and the target deceleration value being less than or equal to the preset threshold, use the second deceleration value and the execution braking air pressure value as the input value and the output value respectively to optimize the braking control model to obtain the optimized braking control model.

[0090] In this embodiment, after the braking system calculates the second deceleration value based on the first deceleration value, the target deceleration value, the vehicle weight information, and the running slope information of the target vehicle, if the second difference between the second deceleration value and the target deceleration value is less than or equal to the above preset threshold, the second deceleration value can be used as the input value of the braking control model, and the above execution braking air pressure value can be used as the output value of the braking control model to optimize the braking control model.

[0091] Specifically, for example, the braking system can import the above second deceleration value and execution braking air pressure value as a new mapping relationship into the braking control model, so that the target execution braking air pressure value corresponding to the target deceleration value of the target vehicle can be calculated by using the optimized braking control model subsequently.

[0092] In another embodiment, when the second difference between the second deceleration value and the target deceleration value is less than or equal to the preset threshold, the target deceleration value can be input into the braking control model to obtain the first execution braking air pressure value output by the braking control model, and the second deceleration value can be input into the braking control model to obtain the second execution braking air pressure value output by the braking control model. Furthermore, according to the first execution braking air pressure and the second execution braking air pressure, the compensation braking air pressure value generated by the vehicle weight information and the running slope information of the target vehicle can be determined, and the first execution braking air pressure value can be replaced by the sum of the above first execution braking air pressure value and the compensation braking air pressure value to optimize the braking control model.

[0093] In addition to the above braking control model optimization method, in the above S30, "optimize the braking control model according to the execution braking air pressure value and the second deceleration value to obtain the optimized braking control model" may include:

[0094] S302, in response to the second difference between the second deceleration value and the target deceleration value being greater than the preset threshold, obtain the vehicle driving information during the braking process of the target vehicle; wherein, the vehicle driving information includes at least one of the clutch connection state, the engine speed, and the transmission shaft speed;

[0095] S303, in response to the vehicle driving information meeting the driving termination condition, use the second deceleration value and the execution braking air pressure value as the input value and the output value respectively to optimize the braking control model to obtain the optimized braking control model.

[0096] It should be noted that in this embodiment, after the braking system calculates the second deceleration value based on the first deceleration value, the target deceleration value, the vehicle weight information, and the running slope information of the target vehicle, if the second difference between the second deceleration value and the target deceleration value is greater than the above preset threshold, the braking system can obtain the vehicle driving information during the braking process of the target vehicle.

[0097] Among them, the vehicle drive information in this embodiment includes at least one of the clutch connection state, engine speed, and transmission shaft speed.

[0098] It can be understood that the clutch slip ratio is the ratio of the difference between the engine speed and the transmission input shaft speed to the engine speed. In this embodiment, the clutch slip ratio is taken as an example to illustrate the drive termination condition in this embodiment.

[0099] Specifically, the implementation of this solution is illustrated below by setting 90% as the clutch slip ratio threshold. At this time, the vehicle drive information in this embodiment does not meet the drive termination condition, including at least one of the following:

[0100] 1. From the start of executing the deceleration control instruction to the end of execution, the clutch slip ratio is less than 90% (or other set threshold);

[0101] 2. From the start of executing the deceleration control instruction to the end of execution, the duration of the clutch slip ratio in a state less than 90% exceeds a preset duration.

[0102] On this basis, if from the start of executing the deceleration control instruction to the end of execution, the clutch slip ratio exceeds 90% all the time, and / or the duration of the clutch slip ratio less than 90% does not exceed the preset duration, the braking system can use the above second deceleration value and the execution braking air pressure value as input values and output values respectively to optimize the braking control model, so as to obtain an optimized braking control model.

[0103] It should be noted that when the clutch slip ratio is 0%, the clutch is in a fully engaged state; when the clutch slip ratio is 100%, the clutch is in a fully disengaged state. When the vehicle is traveling at a certain speed and the clutch is just disengaged, since the vehicle will not stop immediately, the transmission input shaft speed will still have a certain rotational speed value, but at this time the clutch is already fully disengaged and there is no power output from the engine end. Therefore, in this embodiment, when the clutch slip ratio threshold is set to 90%, it is regarded that the clutch is already in a fully disengaged state.

[0104] The length of time required for the clutch to disengage is generally related to the degree of change in vehicle speed. The greater the change in vehicle speed, the faster the clutch disengages, that is, the greater the deceleration, the faster the clutch disengages. For the threshold of the clutch disengagement time (i.e., the above preset duration in this embodiment), this embodiment can refer to the clutch disengagement time when executing a general common deceleration (excluding the cases of sudden braking or light braking of the vehicle), and take the average value of the time required for the clutch to disengage as the preset duration to determine whether the time required for the vehicle clutch to disengage exceeds the threshold when executing a smaller deceleration.

[0105] In another embodiment, if the above vehicle driving information does not meet the driving termination condition, it indicates that driving has an impact on braking. At this time, the second deceleration value is not suitable as an input value to optimize the braking control model.

[0106] Generally speaking, in this embodiment, as Figure 2 shown, the autonomous driving system calculates the target deceleration value, the first deceleration value, the vehicle weight information, and the running slope information, and sends them to the braking system. The braking system calculates the deceleration compensation value corresponding to the vehicle resistance generated by the vehicle weight and the running slope, and subtracts the deceleration compensation value from the first deceleration value to obtain the second deceleration value. When the difference between the second deceleration value and the target deceleration value is less than the preset threshold, the second deceleration value and the execution braking air pressure value are used as the input value and the output value respectively to optimize the braking control model; otherwise, the clutch slip rate during the braking process of the target vehicle is obtained. If the clutch slip rate exceeds 90% from the start of executing the deceleration control instruction to the end of execution, and / or the duration for which the clutch slip rate is less than 90% does not exceed the preset duration, the braking system can use the above second deceleration value and the execution braking air pressure value as the input value and the output value respectively to optimize and train the braking control model.

[0107] Therefore, the deceleration compensation value generated by the vehicle resistance is no longer removed from the target deceleration value issued by the autonomous driving system, avoiding the repeated calculation of the vehicle resistance by the autonomous driving system and the braking system; at the same time, by separately setting the difference threshold between the second deceleration value and the target deceleration value, the clutch slip rate threshold, and the threshold of the clutch disconnection time, deceleration values that do not meet the conditions are not included in the self-learning, thereby optimizing the drawback that the self-learning algorithm of the braking system in the prior art completely depends on the execution situation of the previous braking and cannot effectively exclude other influencing conditions, enabling the braking system of the vehicle to achieve more accurate deceleration control in continuous braking self-learning.

[0108] This embodiment also provides a vehicle control system, as Figure 4 shown, the system includes an autonomous driving system 1001 and a braking system 1002, where:

[0109] The autonomous driving system 1001 is configured to determine the target deceleration value; determine the first deceleration value of the target vehicle according to the running detection data of the target vehicle; and send the first deceleration value and the target deceleration value to the braking system;

[0110] The braking system 1002 is configured to, in response to a first difference between the first deceleration value and the target deceleration value being greater than a preset threshold, determine a second deceleration value according to the first deceleration value, the vehicle weight information, and the running slope information of the target vehicle; optimize a braking control model according to the execution braking air pressure value and the second deceleration value to obtain an optimized braking control model; and determine the target braking air pressure of the target vehicle according to the optimized braking control model, the target deceleration value, the vehicle weight information, and the running slope information of the target vehicle.

[0111] Optionally, the above braking system is further configured to:

[0112] Receive a first deceleration value from the autonomous driving system;

[0113] In response to a deceleration control instruction of the autonomous driving system, determine the target deceleration value and the execution braking air pressure value.

[0114] Optionally, the above braking system is further configured to:

[0115] Determine a deceleration compensation value according to the vehicle weight information and the running slope information of the target vehicle;

[0116] Determine the second deceleration value according to the first deceleration value and the deceleration compensation value.

[0117] Optionally, the above braking system is further configured to:

[0118] In response to a second difference between the second deceleration value and the target deceleration value being less than or equal to the preset threshold, use the second deceleration value and the execution braking air pressure value as input and output values respectively to optimize the braking control model, obtaining the optimized braking control model.

[0119] Optionally, the above braking system is further configured to:

[0120] In response to a second difference between the second deceleration value and the target deceleration value being greater than the preset threshold, obtain vehicle driving information during the braking process of the target vehicle; wherein the vehicle driving information includes at least one of a clutch connection state, an engine speed, and a transmission shaft speed;

[0121] In response to the vehicle driving information meeting a driving termination condition, use the second deceleration value and the execution braking air pressure value as input and output values respectively to optimize the braking control model, obtaining the optimized braking control model.

[0122] Optionally, the above braking system is further configured to:

[0123] Determine a third deceleration value according to the target deceleration value and the deceleration compensation value;

[0124] Input the third deceleration value into the optimized braking control model to obtain the target braking air pressure.

[0125] For the specific implementation of each of the above operations, reference may be made to the previous embodiments and will not be elaborated herein.

[0126] Correspondingly, an embodiment of the present disclosure further provides a vehicle control device, as Figure 5 shown, Figure 5 is a schematic structural diagram of the vehicle control device provided by the embodiment of the present disclosure. The vehicle control device 1100 includes a processor 1101 having one or more processing cores, a memory 1102 having one or more computer-readable storage media, and a computer program stored on the memory 1102 and executable on the processor. Among them, the processor 1101 is electrically connected to the memory 1102. Those skilled in the art can understand that the structure of the vehicle control device shown in the figure does not constitute a limitation on the vehicle control device, and may include more or fewer components than shown, or combine certain components, or arrange different components.

[0127] The processor 1101 is the control center of the vehicle control device 1100, connects various parts of the entire vehicle control device 1100 through various interfaces and lines, and executes various functions and processes data of the vehicle control device 1100 by running or loading software programs and / or units stored in the memory 1102, and calling data stored in the memory 1102, so as to monitor the vehicle control device 1100 as a whole. The processor 1101 may be a processor CPU, a graphics processing unit GPU, a network processor (NP), etc., and may implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present disclosure.

[0128] In the embodiment of the present disclosure, the processor 1101 in the vehicle control device 1100 will load the instructions corresponding to the processes of one or more application programs into the memory 1102 according to the following steps, and the processor 1101 will run the application programs stored in the memory 1102 to implement various functions, such as:

[0129] Obtain the first deceleration value, the target deceleration value, and the execution braking air pressure value of the target vehicle;

[0130] In response to the first difference between the first deceleration value and the target deceleration value being greater than a preset threshold, determine a second deceleration value according to the first deceleration value, the vehicle weight information, and the running slope information of the target vehicle;

[0131] Optimize the braking control model according to the executed braking air pressure value and the second deceleration value to obtain an optimized braking control model;

[0132] Determine the target braking air pressure of the target vehicle according to the optimized braking control model, the target deceleration value, and the vehicle weight information and operating slope information of the target vehicle.

[0133] For the specific implementation of each of the above operations, reference may be made to the previous embodiments and will not be elaborated herein.

[0134] Optionally, as Figure 5 shown, the vehicle control device 1100 further includes: a touch display screen 1103, a radio frequency circuit 1104, an audio circuit 1105, an input unit 1106, and a power supply 1107. Among them, the processor 1101 is electrically connected to the touch display screen 1103, the radio frequency circuit 1104, the audio circuit 1105, the input unit 1106, and the power supply 1107 respectively. Those skilled in the art can understand that Figure 5 the vehicle control device structure shown in

[0135] The touch display screen 1103 can be used to display a graphical user interface and receive operation instructions generated by a user acting on the graphical user interface. The touch display screen 1103 may include a display panel and a touch panel. Among them, the display panel can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the vehicle control device. These graphical user interfaces can be composed of graphics, text, icons, videos, and any combination thereof. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. The touch panel can be used to collect touch operations of the user on or near it (such as operations of the user using any suitable object or accessory such as a finger or a stylus on or near the touch panel), and generate corresponding operation instructions, and the operation instructions execute the corresponding program. Optionally, the touch panel can include two parts: a touch detection system and a touch controller. Among them, the touch detection system detects the touch position of the user and detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection system, converts it into contact coordinates, and then sends it to the processor 1101, and can receive and execute the commands sent by the processor 1101. The touch panel can cover the display panel. After the touch panel detects a touch operation on or near it, it is transmitted to the processor 1101 to determine the type of touch event. Subsequently, the processor 1101 provides a corresponding visual output on the display panel according to the type of touch event. In the embodiments of the present disclosure, the touch panel and the display panel can be integrated into the touch display screen 1103 to implement input and output functions. However, in some embodiments, the touch panel and the touch panel can be implemented as two independent components to implement input and output functions. That is, the touch display screen 1103 can also be used as a part of the input unit 1106 to implement the input function.

[0136] The radio frequency circuit 1104 can be used to transmit and receive radio frequency signals to establish wireless communication with a network device or other vehicle control devices through wireless communication, and transmit and receive signals with the network device or other vehicle control devices.

[0137] The audio circuit 1105 can be used to provide an audio interface between the user and the vehicle control device through a speaker and a microphone. The audio circuit 1105 can convert the received audio data into an electrical signal and transmit it to the speaker, which converts it into a sound signal for output. On the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 1105, converted into audio data, and then the audio data is output to the processor 1101 for processing. After that, it is sent through the radio frequency circuit 1104 to, for example, another vehicle control device, or the audio data is output to the memory 1102 for further processing. The audio circuit 1105 may also include an earphone jack to provide communication between the peripheral earphone and the vehicle control device.

[0138] The input unit 1106 can be used to receive input digital, character information or user characteristic information (such as fingerprint, iris, face information, etc.), and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0139] The power supply 1107 is used to supply power to each component of the vehicle control device 1100. Optionally, the power supply 1107 can be logically connected to the processor 1101 through a power management system, so as to realize functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 1107 may also include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0140] Although Figure 5 not shown in the figure, the vehicle control device 1100 may also include a camera, a sensor, a Wi-Fi module, a Bluetooth module, etc., which will not be elaborated here.

[0141] In the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0142] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling relevant hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0143] Therefore, the embodiments of the present disclosure provide a computer-readable storage medium, in which multiple computer programs are stored. The computer programs can be loaded by a processor to execute any vehicle control method provided by the embodiments of the present disclosure. The computer programs can execute the following steps of the vehicle control method:

[0144] Obtain the first deceleration value, the target deceleration value, and the execution braking air pressure value of the target vehicle;

[0145] In response to a first difference between the first deceleration value and the target deceleration value being greater than a preset threshold, determine a second deceleration value according to the first deceleration value, the target deceleration value, and vehicle weight information and operating slope information of the target vehicle;

[0146] Optimize a braking control model according to the execution braking air pressure value and the second deceleration value to obtain an optimized braking control model;

[0147] Determine a target braking air pressure of the target vehicle according to the optimized braking control model, the target deceleration value, and vehicle weight information and operating slope information of the target vehicle.

[0148] For the specific implementation of each of the above operations, reference may be made to the previous embodiments, which will not be elaborated herein.

[0149] Wherein, the computer-readable storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0150] Since the computer program stored in the computer-readable storage medium can execute any vehicle control method provided in the embodiments of the present disclosure, the beneficial effects achievable by any vehicle control method provided in the embodiments of the present disclosure can be realized. For details, reference may be made to the previous embodiments, which will not be elaborated herein.

[0151] In the above vehicle control system, computer-readable storage medium, vehicle control device, and vehicle, the descriptions of each embodiment have their own focuses. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes and the beneficial effects that can be brought about by the above-described vehicle control system, computer-readable storage medium, vehicle control device, vehicle, and their corresponding units can refer to the description of the vehicle control method in the above embodiments, which will not be elaborated herein specifically.

[0152] The above has introduced in detail a vehicle control method, system, vehicle control device, computer-readable storage medium, and vehicle provided by the embodiments of the present disclosure. Specific examples are used herein to elaborate on the principle and implementation manner of the present disclosure. The description of the above embodiments is only used to help understand the method and its core idea of the present disclosure; at the same time, for those skilled in the art, according to the idea of the present disclosure, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation on the present disclosure.

Claims

1. A vehicle control method, characterized in that, The method includes: Obtaining a first deceleration value, a target deceleration value, and an execution braking air pressure value of a target vehicle; In response to a first difference between the first deceleration value and the target deceleration value being greater than a preset threshold, determining a second deceleration value according to the first deceleration value, the vehicle weight information, and the running slope information of the target vehicle; Optimizing a braking control model according to the execution braking air pressure value and the second deceleration value to obtain an optimized braking control model; Determining a target braking air pressure of the target vehicle according to the optimized braking control model, the target deceleration value, the vehicle weight information, and the running slope information of the target vehicle.

2. The vehicle control method according to claim 1, wherein, The obtaining of the first deceleration value, the target deceleration value, and the execution braking air pressure value of the target vehicle includes: Receiving the first deceleration value of the target vehicle determined by an autonomous driving system according to the running detection data of the target vehicle; In response to a deceleration control instruction of the autonomous driving system, determining the target deceleration value and the execution braking air pressure value.

3. The vehicle control method according to claim 1, characterized in that, The determining of the second deceleration value according to the first deceleration value, the vehicle weight information, and the running slope information of the target vehicle includes: Determining a deceleration compensation value according to the vehicle weight information and the running slope information of the target vehicle; Determining the second deceleration value according to the first deceleration value and the deceleration compensation value.

4. The vehicle control method according to claim 3, characterized in that The determining of the target braking air pressure of the target vehicle according to the optimized braking control model, the target deceleration value, the vehicle weight information, and the running slope information of the target vehicle includes: Determining a third deceleration value according to the target deceleration value and the deceleration compensation value; Inputting the third deceleration value into the optimized braking control model to obtain the target braking air pressure.

5. The vehicle control method according to claim 1, wherein, The optimizing of the braking control model according to the execution braking air pressure value and the second deceleration value to obtain the optimized braking control model includes: In response to a second difference between the second deceleration value and the target deceleration value being less than or equal to the preset threshold, using the second deceleration value and the execution braking air pressure value as input values and output values respectively to optimize the braking control model to obtain the optimized braking control model.

6. The vehicle control method according to claim 1, wherein The optimizing of the braking control model according to the execution braking air pressure value and the second deceleration value to obtain the optimized braking control model includes: In response to the second difference between the second deceleration value and the target deceleration value being greater than the preset threshold, obtaining vehicle driving information during the braking process of the target vehicle; wherein, the vehicle driving information includes at least one of a clutch connection state, an engine speed, and a transmission shaft speed; In response to the vehicle driving information meeting a driving termination condition, using the second deceleration value and the execution braking air pressure value as input values and output values respectively to optimize the braking control model to obtain the optimized braking control model.

7. A vehicle control system, characterized in that, The system includes an autonomous driving system and a braking system, wherein: The autonomous driving system is used to determine a target deceleration value; determine a first deceleration value of the target vehicle according to the operation detection data of the target vehicle; and send the first deceleration value and the target deceleration value to the braking system. The braking system is used to, in response to a first difference between the first deceleration value and the target deceleration value being greater than a preset threshold, determine a second deceleration value according to the first deceleration value, the vehicle weight information, and the running slope information of the target vehicle; optimize a braking control model according to an execution braking air pressure value and the second deceleration value to obtain an optimized braking control model; and determine a target braking air pressure of the target vehicle according to the optimized braking control model, the target deceleration value, the vehicle weight information, and the running slope information of the target vehicle.

8. The vehicle control system according to claim 7, wherein, The braking system is further used for: Receiving the first deceleration value from the autonomous driving system. In response to a deceleration control instruction of the autonomous driving system, determining the target deceleration value and the execution braking air pressure value.

9. The vehicle control system according to claim 7, wherein The braking system is further used for: Determining a deceleration compensation value according to the vehicle weight information and the running slope information of the target vehicle. Determining the second deceleration value according to the first deceleration value and the deceleration compensation value.

10. The vehicle control system according to claim 7, characterized in that, The braking system is further used for: In response to a second difference between the second deceleration value and the target deceleration value being less than or equal to the preset threshold, using the second deceleration value and the execution braking air pressure value as input values and output values respectively to optimize the braking control model to obtain the optimized braking control model.

11. The vehicle control system according to claim 7, characterized in that, The braking system is further used for: In response to the second difference between the second deceleration value and the target deceleration value being greater than the preset threshold, obtaining vehicle driving information during the braking process of the target vehicle; wherein the vehicle driving information includes at least one of a clutch connection state, an engine speed, and a transmission shaft speed. In response to the vehicle driving information meeting a driving termination condition, using the second deceleration value and the execution braking air pressure value as input values and output values respectively to optimize the braking control model to obtain the optimized braking control model.

12. The vehicle control system according to claim 9, characterized in that, The braking system is further used for: Determining a third deceleration value according to the target deceleration value and the deceleration compensation value. Inputting the third deceleration value into the optimized braking control model to obtain the target braking air pressure.

13. A vehicle control device, characterized in that, The vehicle control device includes: One or more processors; A memory; and One or more applications, wherein the one or more applications are stored in the memory and are configured to be executed by the processor to implement the vehicle control method according to any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and the computer program is loaded by the processor to execute the steps in the vehicle control method according to any one of claims 1 to 6.

15. A vehicle, characterized in that, The vehicle includes the vehicle control system according to any one of claims 7 - 12.

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