Adaptive cruise control method, device and vehicle

Abstract

The application relates to the technical field of vehicle control, in particular to a self-adaptive cruise control method, device and vehicle. The method comprises the following steps: acquiring state parameters related to a host vehicle and a preceding vehicle; acquiring a current collision time between the host vehicle and the preceding vehicle, and determining a variable adjustment coefficient based on the collision time; adjusting a sliding mode control parameter in a sliding mode control law according to the variable adjustment coefficient, and calculating a desired acceleration according to the state parameters; and setting a torque of an engine of the host vehicle and brake pressure according to the desired acceleration, so as to control the host vehicle to follow the preceding vehicle. Thus, the problem of poor comfort caused by an auxiliary driving control algorithm can be effectively solved.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to an adaptive cruise control method, device and vehicle. Background Technology

[0002] With the increasing prevalence of intelligent driving, it is likely to become a standard feature in automobiles. Safety and comfort are key aspects of intelligent driver assistance systems.

[0003] In real-world scenarios, different operating conditions often require different approximation effects. A fixed approximation effect often leads to a contradiction between following comfort and timely deceleration. That is, good following comfort but untimely deceleration, or timely deceleration but poor following comfort. The traditional approach to solve this problem is to use different controllers with different patches for different scenarios to correct either the perceived comfort or the sense of safety. However, switching between multiple controllers can cause abrupt changes in output values, still resulting in inconsistent deceleration, poor following comfort, and other issues. Summary of the Invention

[0004] In view of this, embodiments of this application provide an adaptive cruise control method, device, and vehicle, which can effectively solve problems such as poor comfort caused by driver assistance control algorithms.

[0005] In a first aspect, embodiments of this application provide an adaptive cruise control method, including: Obtain the status parameters related to the vehicle and the vehicle in front; The current collision time between the vehicle and the preceding vehicle is obtained, and a variable adjustment coefficient is determined based on the collision time; The sliding mode control parameters within the sliding mode control law are adjusted according to the variable adjustment coefficient, and the desired acceleration is calculated based on the state parameters. The engine torque and braking pressure of the vehicle are set according to the desired acceleration to control the vehicle to follow the vehicle in front.

[0006] In some embodiments, the method for constructing the sliding mode control law includes: A variable sliding surface function containing slope coefficients is constructed, and a variable reaching law containing gain coefficients is constructed based on the variable sliding surface function to construct a longitudinal motion model; The sliding mode control law is constructed based on the longitudinal motion model, the variable sliding surface function, and the variable approach law. The slope coefficient and the gain coefficient in the sliding mode control law are dynamically adjusted using the variable adjustment coefficient.

[0007] In some embodiments, the sliding mode control law is expressed by the following formula:

[0008] in, u Let represent the desired acceleration of the vehicle, 'a' represent the variable adjustment coefficient, and 'c' represent the slope coefficient of the variable sliding surface function. v re This indicates the relative speed between the vehicle and the vehicle in front. This indicates the acceleration of the vehicle in front. k Δ represents the gain coefficient in the variable reaching law. s This indicates the distance error between the vehicle and the vehicle in front. ε Represents positive numbers. S Denotes the variable sliding surface function, sgn( S )express S The symbolic function.

[0009] In some embodiments, the method for constructing the longitudinal motion model includes: A second-order dynamics model is used to describe the calculus and integral relationships between the distance and speed, and speed and acceleration of the vehicle and the vehicle in front, so as to construct the relationship between the current state error and the expected acceleration of the vehicle; wherein, the current state error includes the distance error between the vehicle and the vehicle in front and the relative speed.

[0010] In some embodiments, the second-order dynamic model is expressed by the following formula:

[0011] The relationship between the current state error and the vehicle's expected acceleration is constructed as follows:

[0012] in, x Denotes the state vector, Δ s This indicates the distance error between the vehicle and the vehicle in front. v re Let A represent the relative speed between the vehicle and the vehicle in front, and let A be the matrix describing the dynamic relationship between the state variables. u Let B represent the desired acceleration of the vehicle, B represent the control matrix describing the influence of the control input on the state variables, Φ be the external disturbance vector, τb be the braking time constant of the vehicle, and δ be the acceleration of the vehicle in front.

[0013] In some embodiments, the variable sliding surface function is constructed based on the distance error between the vehicle and the preceding vehicle, the rate of change of the distance error, and the slope coefficient of the variable sliding surface function. The variable reaching law is constructed based on the gain coefficient, sign function, and variable sliding surface function.

[0014] In some embodiments, the variable sliding surface function S Expressed using the following formula:

[0015] Where e represents the distance error between the vehicle and the vehicle in front; represents the rate of change of the vehicle distance error; c represents the slope coefficient of the variable sliding surface function; The variable reaching law is expressed by the following formula:

[0016] in, k This represents the gain coefficient in the variable reaching law. ε Represents positive numbers. S Denotes the variable sliding surface function, sgn( S )express S The symbolic function.

[0017] In some embodiments, obtaining the current collision time between the vehicle and the preceding vehicle, and determining a variable adjustment coefficient based on the collision time, includes: The collision time is calculated based on the relative distance and relative speed between the vehicle and the vehicle in front. The variable adjustment coefficient is determined by looking up a table based on the collision time.

[0018] Secondly, embodiments of this application provide an adaptive cruise control device, comprising: A parameter acquisition device is used to acquire state parameters related to the vehicle and the vehicle in front. The adjustment coefficient determination module is used to obtain the current collision time between the self-vehicle and the preceding vehicle, and to determine the variable adjustment coefficient based on the collision time; The desired acceleration calculation module is used to adjust the sliding mode control parameters within the sliding mode control law according to the variable adjustment coefficient, and to calculate the desired acceleration based on the state parameters. The control module is used to set the engine torque and braking pressure of the vehicle according to the desired acceleration, so as to control the vehicle to follow the vehicle in front.

[0019] Thirdly, embodiments of this application provide a vehicle that follows a vehicle ahead using an adaptive cruise control method as provided in the first aspect of this application.

[0020] The embodiments of this application have the following beneficial effects: This application obtains state parameters related to the vehicle and the preceding vehicle; obtains the current collision time between the two vehicles and determines a variable adjustment coefficient based on the collision time; adjusts the sliding mode control parameters within the sliding mode control law according to the variable adjustment coefficient, and calculates the desired acceleration based on the state parameters; and sets the torque and braking pressure of the vehicle's engine according to the desired acceleration to control the vehicle to follow the preceding vehicle. This application determines the variable adjustment coefficient based on the collision time and adjusts the sliding mode control parameters within the control law in real time using the variable adjustment coefficient, which can effectively solve the problem of poor comfort caused by assisted driving control algorithms. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart of an adaptive cruise control method according to an embodiment of this application is shown; Figure 2 This illustrates the relationship between the distance error e on the sliding surface and the adaptive cruise control method according to an embodiment of this application. Relationship diagram; Figure 3a This diagram illustrates a comparison of the adaptive cruise control method of this application embodiment with the simulation results of a following scenario in the prior art. Figure 3b This illustration shows another schematic diagram comparing the adaptive cruise control method of this application embodiment with the simulation results of following vehicle scenarios in the prior art; Figure 4a This diagram illustrates the change in desired acceleration in the adaptive cruise control method of this application under scenarios where the preceding vehicle frequently cuts in and out. Figure 4b This diagram illustrates the changes in target speed and current speed of the vehicle in an adaptive cruise control method according to an embodiment of this application, under scenarios where the preceding vehicle frequently enters and exits the lane. Figure 4c This diagram illustrates the changes in the desired distance and the distance to the preceding vehicle in the adaptive cruise control method according to an embodiment of this application under scenarios where the preceding vehicle frequently cuts in and out. Figure 5a This illustration shows a schematic diagram of the change in desired acceleration in the adaptive cruise control method of this application, where the vehicle in front frequently accelerates and decelerates while following the vehicle in front. Figure 5bThis illustration shows the changes in target speed and current speed of the vehicle in a scenario where the vehicle is following a vehicle and the vehicle is frequently accelerating or decelerating, in the adaptive cruise control method of this application embodiment. Figure 5c The diagram illustrates the changes in the desired distance and the current distance in the adaptive cruise control method of this application, where the vehicle in front frequently accelerates and decelerates. Detailed Implementation

[0023] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0024] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0025] In the following text, the terms "comprising," "having," and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more combinations thereof. Furthermore, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0026] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0027] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0028] Traditional PI control methods cannot meet the actual requirements of vehicles. Therefore, to improve the dynamic quality of ACC (Adaptive Cruise Control), this application utilizes the advantages of sliding mode control (SMC), such as insensitivity to disturbances and parameters and fast response speed, to achieve the control function of following the vehicle in front. The fundamental difference between the sliding mode control strategy in this application and conventional control lies in the discontinuity of control, that is, a switching characteristic that causes the control system structure to change over time. This characteristic allows the control system to make small-amplitude, high-frequency up-and-down movements along a predetermined state trajectory under certain conditions, which is the so-called "sliding mode". Moreover, this application also adjusts the changes in the sliding surface slope and the reaching law during the dynamic sliding process to achieve a customizable dynamic response. Combined with TTC (Time to Collision) to express the weight ratio of comfort and safety, a single controller is used to achieve timely deceleration while following the vehicle, smooth following, and high user comfort.

[0029] This application first provides a vehicle, exemplary in that the vehicle follows a vehicle ahead using the adaptive cruise control method of the embodiments of this application. Exemplarily, the vehicle includes an ACC (Adaptive Cruise Control) controller, through which the adaptive cruise control method of this application is implemented to follow the vehicle ahead.

[0030] The adaptive cruise control method will be explained below with reference to some specific embodiments.

[0031] Figure 1 A flowchart of an adaptive cruise control method according to an embodiment of this application is shown. Exemplarily, the adaptive cruise control method includes the following steps: S100: Obtain the status parameters related to the vehicle and the vehicle in front.

[0032] For example, the state parameters include the speed of the vehicle in front, the speed of the vehicle itself, and the distance error between the vehicle and the vehicle in front. The vehicle in front is the vehicle traveling in the same lane as the vehicle in front.

[0033] S200 obtains the current collision time between the vehicle and the vehicle in front, and determines the variable adjustment coefficient based on the collision time.

[0034] In one implementation, existing ACC controllers often use fixed control parameters, making it difficult to balance safety and comfort requirements in different scenarios. This application's embodiment introduces TTC (Time to Collision) as a dynamic adjustment factor, achieving a dynamic balance between safety and comfort within a single controller, avoiding abrupt changes in control output and a fragmented user experience caused by switching between multiple controllers.

[0035] Example: The current collision time between the vehicle and the vehicle in front is obtained, and a variable adjustment coefficient is determined based on the collision time, including: S210 calculates the collision time based on the relative distance and relative speed between the vehicle and the vehicle in front.

[0036] That is, the collision time between the vehicle and the vehicle in front is obtained by dividing the relative distance by the relative speed.

[0037] S220, the variable adjustment coefficient is determined by looking up the table based on the collision time.

[0038] As an example, the table construction process includes: (1) Determine the TTC range. Divide the TTC value range into several ranges. The range division should be determined comprehensively based on real vehicle scenarios, following behavior models, safety level assessment standards, etc.

[0039] (2) Define the value of a for each interval. Based on the TTC intervals, set the corresponding value of a according to the following principles: The smaller the TTC, the larger the a value → emphasizing response speed; The higher the TTC, the lower the a value → emphasizing comfort.

[0040] (3) Simulation verification and vehicle calibration.

[0041] Regarding simulation verification, simulation platforms (such as MATLAB / Simulink, Prescan, CarSim, etc.) are used to simulate different TTC scenarios. The control effects under different α values ​​are verified (such as convergence speed, chattering degree, comfort score, etc.); the mapping relationship between α value and TTC is preliminarily determined.

[0042] Regarding real-vehicle calibration, in real-vehicle testing, the driver's comfort feedback under different TTC scenarios is recorded; indicators such as the rate of change of acceleration (Jerk) and the convergence speed of following distance error are analyzed; the a value is adjusted based on actual experience, and the lookup table relationship is optimized.

[0043] S300 adjusts the sliding mode control parameters within the sliding mode control law according to the variable adjustment coefficient, and calculates the desired acceleration based on the state parameters.

[0044] Exemplarily, the state parameters include the speed of the preceding vehicle, the speed of the vehicular vehicle, and the distance error between the vehicular and preceding vehicles. Understandably, in step S300, the sliding mode control parameters within the sliding mode control law are adjusted according to the variable adjustment coefficient, and the desired acceleration is calculated based on the state parameters, including: The desired distance is calculated using the speed of the vehicle in front, the speed of the vehicle itself, and the time difference. The distance error is obtained by combining the expected distance and the actual distance between the vehicle and the vehicle in front. The values ​​of the variable adjustment coefficient, the vehicle distance error, etc., are input into the formula of the sliding mode control law containing the variable adjustment coefficient to calculate the desired acceleration of the vehicle.

[0045] The S400 sets the engine torque and braking pressure of the vehicle according to the desired acceleration to control the vehicle to follow the vehicle in front.

[0046] Controlling your vehicle's movement to follow the vehicle in front includes: timely deceleration and gradual following.

[0047] In one implementation, to balance safety and comfort requirements in different scenarios, this application employs a sliding mode controller with adjustable sliding mode control parameters to control vehicle following. The purpose of this application is to implement a sliding mode control algorithm that calculates the desired acceleration based on distance error. First, this application constructs a variable sliding mode surface function, a variable reaching law, and a longitudinal motion model. Based on the variable sliding mode surface function, the variable reaching law, and the longitudinal motion model, the sliding mode control law of the sliding mode controller is obtained to achieve the goal of calculating the desired acceleration based on distance error.

[0048] Exemplary methods for constructing sliding mode control laws include: S510: Construct a variable sliding surface function containing slope coefficients, and based on the variable sliding surface function, construct a variable reaching law containing gain coefficients to build a longitudinal motion model.

[0049] S520: Construct the sliding mode control law based on the longitudinal motion model, the variable sliding surface function, and the variable reaching law. For example, by substituting the longitudinal motion model into the variable sliding surface function and combining it with the variable reaching law, the sliding mode control law is obtained.

[0050] The sliding mode controller's sliding mode control parameters are dynamically adjusted based on real-time changes in the driving scenario (such as TTC and time of collision), thereby achieving flexible control over the sliding mode controller's response characteristics. This not only improves the adaptability and robustness of the control system but also enables a dynamic trade-off between "safety" and "comfort" in different driving scenarios.

[0051] Understandably, both the variable sliding surface function and the variable reaching law include sliding mode control parameters. The variable sliding surface function is a sliding surface function whose sliding mode control parameters can be dynamically adjusted based on the current collision time. The variable reaching law is a reaching law (variable reaching law) whose sliding mode control parameters can be dynamically adjusted based on the current collision time.

[0052] The S530 uses a variable adjustment coefficient to dynamically adjust the slope coefficient and gain coefficient in the sliding mode control law. The slope coefficient C and gain coefficient K are values ​​calculated through simulation or calibrated on a real vehicle. A variable adjustment coefficient a (derived from a table using TTC) is then introduced based on these values.

[0053] As an example, when TTC is small, the slope coefficient of the variable sliding surface function and the gain coefficient of the variable reaching law are automatically increased, thereby accelerating the convergence of the error between the actual distance and the expected distance between the two vehicles, reducing the feeling of pressure, and improving comfort and safety (timely deceleration); when TTC is large, the slope coefficient of the variable sliding surface function is reduced, the controller response speed is reduced, and comfort is improved (smooth following).

[0054] Furthermore, for faster calculation, the sliding mode control law is expressed by the following formula: (1) in, u Let represent the desired acceleration of the vehicle, 'a' represent the variable adjustment coefficient, and 'c' represent the slope coefficient of the variable sliding mode surface function. v re This indicates the relative speed between the vehicle and the vehicle in front. Indicates the acceleration of the vehicle in front. k Δ represents the gain coefficient in the variable reaching law. s This indicates the distance error between the vehicle and the vehicle in front. ε Represents positive numbers. S Denotes the variable sliding surface function, sgn( S )express S The sign function. Understandably, the sliding mode control parameters for a variable sliding surface function include the slope coefficient. The sliding mode control parameters for a variable reaching law include the gain coefficient.

[0055] sgn(S) is S The sign function is a key function used in sliding mode control (SMC) to implement discontinuous control laws. Its role is to rapidly approach and maintain the system state on the sliding surface, thereby ensuring the system's robustness and dynamic response. The introduction of ε is to enhance the convergence speed of the system during the sliding mode approach phase, especially near the sliding surface. S When it approaches 0, it enables the system to quickly reach and remain on the sliding surface.

[0056] Furthermore, a variable sliding surface function is constructed based on the distance error between the vehicle and the preceding vehicle, the rate of change of the distance error, and the slope coefficient of the variable sliding surface function.

[0057] Demonstratively, variable sliding surface function S (A first-order system) is represented by the following formula: (2) Where e represents the distance error between the vehicle and the vehicle in front; This indicates the rate of change of the distance error. is the derivative of e; c represents the slope coefficient of the variable sliding surface function.

[0058] Formula (2) satisfies the Hurwitz stability criterion, c>0. For example... Figure 2 As shown, on the sliding surface e and As it approaches e=0, =0.

[0059] In this application's embodiments, 'c' is not a fixed value, but a dynamically adjusted value based on the value of 'a' obtained from a table using the TTC (Total Time Tolerance). Specifically, when the TTC is small (i.e., there is a risk of rear-end collision), increasing the value of 'c' results in a steeper sliding surface, faster system response, and improved convergence speed. When the TTC is large (i.e., higher safety), decreasing the value of 'c' results in a smoother sliding surface, slower system response, and improved comfort. This application achieves dynamic amplification or reduction of 'c' by introducing a variable adjustment coefficient 'a'.

[0060] Furthermore, a variable reaching law is constructed based on the gain coefficient, the sign function, and the variable sliding surface function. Exemplarily, the variable reaching law is expressed by the following formula: (3) in, k This represents the gain coefficient in a variable reaching law. ε Represents positive numbers. S Denotes the variable sliding surface function, sgn( S )express S The symbolic function. k It is a positive number to ensure that the system state can quickly reach the sliding surface and maintain the sliding mode; ε It is also a positive number.

[0061] Formula (3) is the exponential reaching law. Wherein, k It's not a fixed value, but a dynamically adjustable value of 'a' obtained from a table based on TTC. When TTC is small (e.g., <2s), k Increasing the value accelerates the rate at which the system state approaches the sliding surface; when TTC is large, k Decreasing the value reduces the approach speed and decreases control jitter. Similarly, by introducing a variable adjustment coefficient 'a', this can be achieved... k Dynamically zoom in or out.

[0062] In one implementation, the method for constructing the longitudinal motion model includes: A second-order dynamics model is used to describe the calculus and integral relationships between the distance and velocity, and velocity and acceleration of the vehicle and the vehicle in front, so as to construct the relationship between the current state error and the expected acceleration of the vehicle; where the current state error includes the distance error between the vehicle and the vehicle in front and the relative velocity.

[0063] As an example, the second-order dynamic model is expressed by the following formula:

[0064] Construct the relationship between the current state error and the vehicle's expected acceleration, including:

[0065] in, x Denotes the state vector, Δ s This represents the distance error between the vehicle and the vehicle in front, specifically the difference between the actual distance and the expected distance. Δ s It is the input value, which is the difference between the actual distance obtained from the sensing distance measurement and the calculated current expected distance. v re This indicates the relative speed between the vehicle and the vehicle in front, i.e., the speed difference between the two vehicles. u This represents the desired acceleration of the vehicle, i.e., the control input. A represents a matrix describing the dynamic relationship between state variables; the first row of matrix A represents: Δ... s The rate of change is v re The second row in matrix A represents: v re The rate of change is 0, meaning that the relative velocity remains constant in the absence of control input and external disturbances. B represents the control matrix describing the influence of the control input on the state variables; the first row of matrix B represents Δ... s It is not directly affected by control input. The second row of matrix B represents v. re The rate of change is -u, meaning the vehicle's acceleration is opposite to the desired acceleration. Φ is the external disturbance vector, τb is the vehicle's braking time constant, representing the vehicle's response time to braking. δ is the acceleration of the vehicle in front. The first row of matrix Φ represents Δ... s The rate of change is affected by -τb. The second row in matrix Φ represents v. re The rate of change of is affected by δ, which is the acceleration of the vehicle in front.

[0066] This dynamic model describes the longitudinal motion of the vehicle under adaptive cruise control. The distance error Δ can be adjusted by controlling the input u, i.e., the vehicle's desired acceleration. s and relative velocity v re This is to achieve a safe distance and speed matching with the vehicle in front.

[0067] Based on the above formulas (4), (5), and (6), we can deduce that:

[0068] Formula (9) is simplified to:

[0069] The derivation of formula (1) is described below: Based on the variable sliding surface function S =c×e+ =0, let Δ s For e, for The sliding mode control law can be obtained as follows:

[0070] and then:

[0071] Substituting the second-order dynamic model into the equations (7) and (8), we get:

[0072] because = - ,in It's the acceleration of the car in front. It is the acceleration of the vehicle, therefore:

[0073] According to the variable reaching law, we have:

[0074] Then solve u ,get:

[0075] Substituting the variable adjustment coefficient 'a' to correct the sliding mode control parameters 'c' and 'k', we obtain formula (1):

[0076] Among them, 'a' is determined based on the TTC value. When the TTC is small, this value can be calibrated to provide greater deceleration when there is a risk of rear-end collision.

[0077] In the adaptive cruise control method of this application embodiment, the sliding mode controller calculates the desired acceleration u based on the distance error and relative speed. Then, the actuator achieves the desired vehicle speed and distance through torque and braking pressure. This control method has good robustness and adaptability, and can effectively cope with the uncertainty of the acceleration of the vehicle in front. Based on this, implementing a variable sliding surface and a variable sliding mode convergence law can meet the customer's customized needs for products in mass production, and adjust the weight ratio of safety and comfort in different scenarios.

[0078] In real-world scenarios, different scenarios often require different approach effects. A fixed approach effect often creates a contradiction between comfortable following motion and timely deceleration. To address this issue, this application uses a single controller designed with adjustable control effects, achieving both strong robustness and customizable motion feel. It can satisfy timely response to stopping and decelerating while also allowing for adjustment of the following response. This application uses TTC as a coefficient for safety and a sense of pressure; the TTC is used to determine the required compensation value C from a lookup table. k The value of 'a' is increased when the TTC is small, thereby increasing the sliding mode slope and the approach law, which accelerates the convergence of the error between the actual distance and the expected distance, reduces the feeling of pressure, and improves comfort. The beneficial effects of the adaptive cruise control method of this application embodiment are explained below based on simulation results after the real vehicle perception data is fed back in: like Figure 3a (The horizontal axis in the figure represents the desired acceleration, and the vertical axis represents time.) Figure 3b (The horizontal axis in the figure represents the collision time, and the vertical axis represents time.) This shows the simulation comparison results of a following vehicle scenario. Figure 3a , 3b The curves marked with △ (red lines) correspond to the control method with fixed reaching law and fixed sliding surface, while the curves marked with ○ (light blue lines) correspond to the control method with variable reaching law and variable sliding surface of this application. Figure 3a The red line represents the expected acceleration (a_out) with a fixed approach law and a fixed sliding surface, while the light blue curve represents the expected acceleration (a_out) output when the variable coefficient a is adjusted according to the TTC (time of collision). Comparing the two lines, it can be seen that a smaller TTC can provide negative acceleration compensation to improve safe braking. Figure 3b The red line represents the TTC results of closed-loop simulation with fixed reaching law and fixed sliding surface. The light blue line represents the TTC results of closed-loop simulation after adjusting the variable coefficient 'a' according to the TTC changes. It can be seen that the TTC increases significantly when it is at its minimum. In summary, the data shows that variable sliding surface and variable reaching law can independently adjust the acceleration value when TTC is small, but do not affect the acceleration value when TTC is large, thus decoupling the reaching rate between safe and dangerous scenarios.

[0079] like Figure 4a , Figure 4b , Figure 4c This is a diagram illustrating the state parameters (expected acceleration, target speed, current speed of the vehicle, expected distance to the vehicle, and current distance to the vehicle) in a scenario where the vehicle in front frequently cuts in and out. Figure 5a , Figure 5b , Figure 5c A diagram illustrating the changes in state parameters when following a vehicle in front, which frequently accelerates and decelerates. Figure 4a , Figure 4b , Figure 4c , Figure 5a , Figure 5b , Figure 5c As can be seen from the above, when the perceived speed of the vehicle in front fluctuates, the vehicle's speed remains relatively stable without fluctuation, demonstrating strong anti-interference capabilities. The distance reflects the positional relationship between the vehicle and the vehicle in front; the distance between the two vehicles remained within a certain safety value throughout the simulation, indicating that no collision occurred. The comparison figures above show that this application utilizes sliding mode control, which decouples the scene and allows for independent adjustment of the sliding surface slope, system convergence speed, and system chattering characteristics. This application eliminates feedforward control, using the rate of change of speed to control the acceleration response, allowing for customized response positivity. This application does not use perceived acceleration information, which, considering the perception characteristics of a single V-camera, reduces the problem of perceived acceleration affecting the vehicle's acceleration response and thus minimizing motion perception issues. Furthermore, the control algorithm of this application is simple, easy to port, and maintain.

[0080] This application also provides an adaptive cruise control device. Exemplarily, the adaptive cruise control device includes: A parameter acquisition device is used to acquire state parameters related to the vehicle and the vehicle in front. The adjustment coefficient determination module is used to obtain the current collision time between the vehicle and the vehicle in front, and determine the variable adjustment coefficient based on the collision time; The desired acceleration calculation module is used to adjust the sliding mode control parameters within the sliding mode control law according to the variable adjustment coefficient, and to calculate the desired acceleration based on the state parameters. The control module is used to set the engine torque and braking pressure of the vehicle according to the desired acceleration, so as to control the vehicle to follow the vehicle in front.

[0081] It is understood that the device in this embodiment corresponds to the adaptive cruise control method in the above embodiments, and the options in the above embodiments are also applicable to this embodiment, so they will not be described again here.

[0082] This application also provides a terminal device, exemplary of which includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to enable the terminal device to perform the functions of the various modules in the above-described adaptive cruise control method or the above-described adaptive cruise control device.

[0083] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0084] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0085] This application also provides a computer-readable storage medium for storing the computer program used in the aforementioned terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0087] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0088] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0089] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. An adaptive cruise control method, characterized in that, include: Obtain the status parameters related to the vehicle and the vehicle in front; The current collision time between the vehicle and the preceding vehicle is obtained, and a variable adjustment coefficient is determined based on the collision time; The sliding mode control parameters within the sliding mode control law are adjusted according to the variable adjustment coefficient, and the desired acceleration is calculated based on the state parameters. The engine torque and braking pressure of the vehicle are set according to the desired acceleration to control the vehicle to follow the vehicle in front. The method for constructing the sliding mode control law includes: A variable sliding surface function containing a slope coefficient is constructed, and a variable reaching law containing a gain coefficient is constructed based on the variable sliding surface function to construct a longitudinal motion model; the sliding control law is constructed according to the longitudinal motion model, the variable sliding surface function, and the variable reaching law; the slope coefficient and the gain coefficient in the sliding control law are adjusted by the variable adjustment coefficient. The sliding mode control law is expressed by the following formula: in, u Let represent the desired acceleration of the vehicle, 'a' represent the variable adjustment coefficient, and 'c' represent the slope coefficient of the variable sliding surface function. v re This indicates the relative speed between the vehicle and the vehicle in front. This indicates the acceleration of the vehicle in front. k Δ represents the gain coefficient in the variable reaching law. s This indicates the distance error between the vehicle and the vehicle in front. ε Represents positive numbers. S Denotes the variable sliding surface function, sgn( S )express S The symbolic function.

2. The adaptive cruise control method according to claim 1, characterized in that, The method for constructing the longitudinal motion model includes: A second-order dynamics model is used to describe the calculus and integral relationships between the distance and speed, and speed and acceleration of the vehicle and the vehicle in front, so as to construct the relationship between the current state error and the expected acceleration of the vehicle; wherein, the current state error includes the distance error between the vehicle and the vehicle in front and the relative speed.

3. The adaptive cruise control method according to claim 2, characterized in that, The second-order dynamic model is expressed by the following formula: The relationship between the current state error and the vehicle's expected acceleration is constructed as follows: in, x Denotes the state vector, Δ s This indicates the distance error between the vehicle and the vehicle in front. v re Let A represent the relative speed between the vehicle and the vehicle in front, and let A be the matrix describing the dynamic relationship between the state variables. u Let B represent the desired acceleration of the vehicle, B represent the control matrix describing the influence of the control input on the state variables, Φ be the external disturbance vector, τb be the braking time constant of the vehicle, and δ be the acceleration of the vehicle in front.

4. The adaptive cruise control method according to claim 1, characterized in that, The variable sliding surface function is constructed based on the distance error between the vehicle and the vehicle in front, the rate of change of the distance error, and the slope coefficient of the variable sliding surface function. The variable reaching law is constructed based on the gain coefficient, sign function, and variable sliding surface function.

5. The adaptive cruise control method according to claim 4, characterized in that, The variable sliding surface function S The following formula is used to express this: Where e represents the distance error between the vehicle and the vehicle in front; represents the rate of change of the vehicle distance error; c represents the slope coefficient of the variable sliding surface function; The variable reaching law is expressed by the following formula: in, k This represents the gain coefficient in the variable reaching law. ε Represents positive numbers. S Denotes the variable sliding surface function, sgn( S )express S The symbolic function.

6. The adaptive cruise control method according to claim 1, characterized in that, The step of obtaining the current collision time between the self-vehicle and the preceding vehicle, and determining the variable adjustment coefficient based on the collision time, includes: The collision time is calculated based on the relative distance and relative speed between the vehicle and the vehicle in front. The variable adjustment coefficient is determined by looking up a table based on the collision time.

7. An adaptive cruise control device, characterized in that, include: A parameter acquisition device is used to acquire state parameters related to the vehicle and the vehicle in front. The adjustment coefficient determination module is used to obtain the current collision time between the self-vehicle and the preceding vehicle, and to determine the variable adjustment coefficient based on the collision time; The desired acceleration calculation module is used to adjust the sliding mode control parameters within the sliding mode control law according to the variable adjustment coefficient, and to calculate the desired acceleration based on the state parameters. The control module is used to set the engine torque and braking pressure of the vehicle according to the desired acceleration, so as to control the vehicle to follow the vehicle in front. The method for constructing the sliding mode control law includes: A variable sliding surface function containing a slope coefficient is constructed, and a variable reaching law containing a gain coefficient is constructed based on the variable sliding surface function to construct a longitudinal motion model; the sliding control law is constructed according to the longitudinal motion model, the variable sliding surface function, and the variable reaching law; the slope coefficient and the gain coefficient in the sliding control law are adjusted by the variable adjustment coefficient. The sliding mode control law is expressed by the following formula: in, u Let represent the desired acceleration of the vehicle, 'a' represent the variable adjustment coefficient, and 'c' represent the slope coefficient of the variable sliding surface function. v re This indicates the relative speed between the vehicle and the vehicle in front. This indicates the acceleration of the vehicle in front. k Δ represents the gain coefficient in the variable reaching law. s This indicates the distance error between the vehicle and the vehicle in front. ε Represents positive numbers. S Denotes the variable sliding surface function, sgn( S )express S The symbolic function.

8. A vehicle, characterized in that, The vehicle follows the vehicle in front using the adaptive cruise control method as described in any one of claims 1-6.

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