Self-adaptive cruise control method and device and vehicle

Abstract

The invention relates to the technical field of vehicle control, in particular to a self-adaptive cruise control method and device and a vehicle. The method comprises the following steps: acquiring state parameters related to a vehicle and a front vehicle; the current collision time between the vehicle and the front vehicle is obtained, and a variable adjustment coefficient is determined based on the collision time; sliding mode control parameters in the sliding mode control law are adjusted according to the variable adjustment coefficient, and an expected acceleration is calculated according to the state parameters; and setting the torque and brake pressure of an own vehicle engine according to the expected acceleration so as to control the own vehicle to advance along with the front vehicle. Therefore, the problem of poor comfort caused by an auxiliary driving control algorithm can be effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, in particular to a self-adaptive cruise control method, device and vehicle. BACKGROUND

[0002] With the increasing of current intelligent driving, intelligent driving will become a standard configuration of vehicles. Among them, safety and comfort are the focus of intelligent auxiliary driving.

[0003] In actual scenarios, different running scenarios often require different approaching effects. Fixed approaching effects often lead to a contradiction between car-following comfort and car-following deceleration timeliness. That is, car-following comfort is good but car-following deceleration is not timely, or car-following deceleration is timely but car-following comfort is poor. In order to solve this problem, the traditional method is to use different controllers to make different patches in different scenarios to correct the sense of touch or safety. However, the switching between multiple controllers will cause the mutation of output values, and there are still problems of incoherent deceleration, poor sense of touch and comfort. SUMMARY

[0004] Therefore, the embodiments of the present application provide a self-adaptive cruise control method, device and vehicle, which can effectively solve the problem of poor comfort caused by auxiliary driving control algorithm.

[0005] In a first aspect, the embodiments of the present application provide a self-adaptive cruise control method, comprising: obtaining state parameters related to a host vehicle and a preceding vehicle; obtaining 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; setting engine torque and brake pressure of the host vehicle according to the desired acceleration, to control the host vehicle to follow the preceding vehicle.

[0006] In some embodiments, the construction method of the sliding mode control law comprises: constructing a variable sliding mode surface function containing a slope coefficient, constructing a variable approaching law containing a gain coefficient based on the variable sliding mode surface function, and constructing a longitudinal motion model; constructing the sliding mode control law according to the longitudinal motion model, the variable sliding mode surface function and the variable approaching law; dynamically adjusting the slope coefficient and the gain coefficient in the sliding mode control law by using the variable adjustment coefficient.

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

[0008] wherein, u represents the desired acceleration of the ego vehicle, a represents the variable adjustment coefficient, c represents the slope coefficient of the variable sliding mode surface function, v re represents the relative speed between the ego vehicle and the front vehicle, represents the acceleration of the front vehicle, k represents the gain coefficient in the variable reaching law, Δ s represents the vehicle distance error between the ego vehicle and the front vehicle, ε represents a positive number, S represents the variable sliding mode surface function, sgn( S ) represents S the sign function of

[0009] In some embodiments, the method for constructing the longitudinal motion model comprises: adopting a second-order dynamic model to describe the integral relationship between the distance and speed, speed and acceleration related to the ego vehicle and the front vehicle, so as to construct a relationship between the current state error and the desired acceleration of the ego vehicle; wherein the current state error comprises the vehicle distance error and the relative speed between the ego vehicle and the front vehicle.

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

[0011] the method for constructing the relationship between the current state error and the desired acceleration of the ego vehicle comprises:

[0012] wherein, x represents the state vector, Δ s represents the vehicle distance error between the ego vehicle and the front vehicle, v re represents the relative speed between the ego vehicle and the front vehicle, A represents a matrix describing the dynamic relationship between state variables, u represents the desired acceleration of the ego vehicle, B represents a control matrix describing the influence relationship of control input on state variables, Φ is an external disturbance vector, τb is the brake time constant of the ego vehicle, and δ is the acceleration of the front vehicle.

[0013] In some embodiments, the variable sliding mode surface function is constructed according to the vehicle distance error between the ego vehicle and the front vehicle, the rate of change of the vehicle distance error, and the slope coefficient of the variable sliding mode surface function; The variable reaching law is constructed according to the gain coefficient, the sign function and the variable sliding mode surface function.

[0014] In some embodiments, the variable sliding mode surface function S is expressed by the following formula:

[0015] wherein e represents a vehicle distance error between the ego vehicle and the preceding vehicle; represents a change rate of the vehicle distance error; and c represents a slope coefficient of the variable sliding mode surface function. The variable reaching law is expressed by the following formula:

[0016] wherein, k represents a gain coefficient in the variable reaching law, ε represents a positive number, S represents a variable sliding mode surface function, sgn( S ) represents a sign function of S .

[0017] In some embodiments, the obtaining of the current collision time between the ego vehicle and the preceding vehicle and the determination of the variable adjustment coefficient based on the collision time comprises: calculating the collision time according to a relative distance and a relative speed between the ego vehicle and the preceding vehicle; determining the variable adjustment coefficient according to the collision time.

[0018] In a second aspect, the embodiments of the present application provide a self-adaptive cruise control device, comprising: a parameter obtaining device, configured to obtain state parameters related to an ego vehicle and a preceding vehicle; an adjustment coefficient determining module, configured to obtain a current collision time between the ego vehicle and the preceding vehicle and determine a variable adjustment coefficient based on the collision time; a desired acceleration calculating module, configured to adjust a sliding mode control parameter in a sliding mode control law according to the variable adjustment coefficient and calculate a desired acceleration according to the state parameters; a control module, configured to set engine torque and brake pressure of the ego vehicle according to the desired acceleration, so as to control the ego vehicle to follow the preceding vehicle.

[0019] In a third aspect, the embodiments of the present application provide a vehicle, which follows a preceding vehicle by using the self-adaptive cruise control method provided in the first aspect of the present application.

[0020] The embodiments of the present application have the following beneficial effects: The application obtains state parameters related to the ego vehicle and the preceding vehicle, obtains a current collision time between the ego vehicle and the preceding vehicle, determines a variable adjustment coefficient based on the collision time, adjusts a sliding mode control parameter in a sliding mode control law according to the variable adjustment coefficient, calculates an expected acceleration according to the state parameters, sets a torque of an engine of the ego vehicle and a brake pressure according to the expected acceleration, and controls the ego vehicle to follow the preceding vehicle. The application determines the variable adjustment coefficient according to the collision time, adjusts the sliding mode control parameter in the control law in real time through the variable adjustment coefficient, and can effectively solve the problem of poor comfort caused by an auxiliary driving control algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some of the embodiments of the application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0022] Figure 1 A flowchart of an adaptive cruise control method of an embodiment of the application is shown; Figure 2 A schematic diagram showing the relationship between a vehicle distance error e on a sliding surface and a target speed in an adaptive cruise control method of an embodiment of the application is shown; Figure 3a A schematic diagram showing the comparison results of a following vehicle scene simulation of the adaptive cruise control method of an embodiment of the application and the prior art is shown; Figure 3b Another schematic diagram showing the comparison results of a following vehicle scene simulation of the adaptive cruise control method of an embodiment of the application and the prior art is shown; Figure 4a A schematic diagram showing the change of an expected acceleration in a preceding vehicle frequently cutting in and out scene in an adaptive cruise control method of an embodiment of the application is shown; Figure 4b A schematic diagram showing the change of a target speed and a current speed of the ego vehicle in a preceding vehicle frequently cutting in and out scene in an adaptive cruise control method of an embodiment of the application is shown; Figure 4c A schematic diagram showing the change of an expected vehicle distance and a current vehicle distance in a preceding vehicle frequently cutting in and out scene in an adaptive cruise control method of an embodiment of the application is shown; Figure 5a A schematic diagram showing the change of an expected acceleration in a preceding vehicle frequently cutting in and out scene in an adaptive cruise control method of an embodiment of the application is shown; Figure 5b ​Fig. 1 shows a schematic diagram of the change of target speed and current speed of the ego vehicle in the adaptive cruise control method of the embodiment of the present application when following the preceding vehicle in the scenario of frequent acceleration and deceleration of the preceding vehicle; Figure 5c Fig. 2 shows a schematic diagram of the change of desired distance and current distance in the adaptive cruise control method of the embodiment of the present application when following the preceding vehicle in the scenario of frequent acceleration and deceleration of the preceding vehicle. DETAILED DESCRIPTION

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

[0024] The components of the embodiments of the present application generally described and illustrated in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0025] Hereinafter, the terms "include", "have", and their conjugates used in various embodiments of the present application are only intended to denote that specific features, numbers, steps, operations, elements, components, or combinations thereof are present, and should not be construed as excluding the possibility of adding one or more other features, numbers, steps, operations, elements, components, or combinations thereof in advance. In addition, the terms "first", "second", "third", and the like are used only to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0026] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present application belong. The terms (such as those defined in commonly used dictionaries) will be interpreted as having a meaning that is the same as the contextual meaning in the relevant technical field and will not be interpreted in an idealized or overly formal sense, unless clearly defined in various embodiments of the present application.

[0027] Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The following embodiments and features in the embodiments can be combined with each other without conflict.

[0028] The traditional PI control method cannot meet the actual requirements of the vehicle. Therefore, in order to improve the dynamic quality of ACC (adaptive cruise control), the application utilizes the advantages of sliding mode control (SMC) such as insensitivity to disturbance and parameters and fast response to realize the control function of following the preceding vehicle. The fundamental difference between the sliding mode control strategy in the application and the conventional control is the discontinuity of control, that is, a switching characteristic that changes the structure of the control system over time. This characteristic can make the control system make small and high-frequency up and down movements along the specified state trajectory under certain conditions, which is called "sliding mode". Moreover, the application also adjusts the sliding mode surface slope and the reaching law change in the dynamic sliding mode process to realize the customizable dynamic response, combines TTC (time to collision) to express the weight ratio of comfort and safety, and uses a single controller to realize the effects of following the vehicle at a proper deceleration and time as well as smooth following and high body comfort.

[0029] The application first provides a vehicle, which is exemplarily followed by a preceding vehicle by using the adaptive cruise control method of the embodiments of the application. Exemplarily, the vehicle of the application includes an ACC (adaptive cruise control) controller, and the adaptive cruise control method of the application is implemented by the ACC controller to follow the preceding vehicle.

[0030] The adaptive cruise control method will be described below in combination with some specific embodiments.

[0031] Figure 1 A flowchart of the adaptive cruise control method of the embodiments of the application is shown. Exemplarily, the adaptive cruise control method includes the following steps: S100, obtaining state parameters related to the host vehicle and the preceding vehicle.

[0032] Exemplarily, the state parameters include the speed of the preceding vehicle, the speed of the host vehicle, and the distance error between the host vehicle and the preceding vehicle. The preceding vehicle is a vehicle traveling in front of the host vehicle in the same lane.

[0033] S200, obtaining the current time to collision (TTC) between the host vehicle and the preceding vehicle, and determining a variable adjustment coefficient based on the time to collision.

[0034] In one embodiment, the existing ACC controller often uses fixed control parameters, which is difficult to balance the safety and comfort requirements in different scenarios. The embodiments of the application introduce TTC (time to collision) as a dynamic adjustment factor to achieve a dynamic balance between safety and comfort in a single controller, avoiding the control output mutation and experience fragmentation caused by multiple controller switching.

[0035] Exemplarily, obtaining the current time to collision (TTC) between the host vehicle and the preceding vehicle, and determining a variable adjustment coefficient based on the time to collision, includes: S210, calculate the time to collision (TTC) according to the relative distance and the relative speed between the ego vehicle and the preceding vehicle.

[0036] That is, the relative distance divided by the relative speed gives the time to collision (TTC) between the ego vehicle and the preceding vehicle.

[0037] S220, determine the variable adjustment coefficient according to the TTC.

[0038] Exemplarily, the construction process of the table includes: (1) Determine the division interval of TTC. Divide the value range of TTC into several intervals. The interval division should be determined comprehensively in combination with real vehicle scenarios, following behavior models, safety level evaluation standards, etc.

[0039] (2) Define the a value corresponding to each interval. According to the TTC interval, set the corresponding a value, and the principle is as follows: The smaller the TTC, the larger the a value → emphasize the response speed; The larger the TTC, the smaller the a value → emphasize the comfort.

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

[0041] Regarding simulation verification, use a simulation platform (such as MATLAB / Simulink, Prescan, CarSim, etc.) to simulate different TTC scenarios. Verify the control effect (such as convergence speed, chattering degree, comfort score, etc.) under different a values; preliminarily determine the mapping relationship between a value and TTC.

[0042] Regarding real vehicle calibration, record the comfort feedback of the driver under different TTC scenarios in real vehicle testing; analyze indicators such as jerk, following distance error convergence speed, etc.; adjust the a value according to the actual experience and optimize the table lookup relationship.

[0043] S300, adjust the sliding mode control parameters in the sliding mode control law according to the variable adjustment coefficient, and calculate the expected acceleration according to the state parameters.

[0044] Exemplarily, the state parameters include the speed of the preceding vehicle, the speed of the ego vehicle, and the distance error between the ego vehicle and the preceding vehicle. Understandably, in step S300, the sliding mode control parameters in the sliding mode control law are adjusted according to the variable adjustment coefficient, and the expected acceleration is calculated according to the state parameters, including: Calculate the expected distance using the speed of the preceding vehicle, the speed of the ego vehicle, and the time distance; Obtain the distance error between the ego vehicle and the preceding vehicle according to the expected distance and the actual distance obtained; Input the value of the variable adjustment coefficient, the distance error, etc. into the formula of the sliding mode control law containing the variable adjustment coefficient, and calculate the expected acceleration of the ego vehicle.

[0045] S400, setting engine torque and brake pressure of the ego vehicle according to the expected acceleration to control the ego vehicle to follow the preceding vehicle.

[0046] Controlling the ego vehicle to follow the preceding vehicle includes timely deceleration and smooth following.

[0047] In an embodiment, in order to balance the safety and comfort requirements in different scenarios, the application adopts a sliding mode controller with adjustable sliding mode control parameters to control the following vehicle. The purpose of the application is to realize a sliding mode control algorithm based on distance error to obtain expected acceleration. First, the application constructs a variable sliding surface function, a variable reaching law and a longitudinal motion model, and obtains a sliding mode control law of the sliding mode controller based on the variable sliding surface function, the variable reaching law and the longitudinal motion model, to achieve the purpose of obtaining expected acceleration based on distance error.

[0048] Exemplarily, the method for constructing the sliding mode control law includes: S510, constructing a variable sliding surface function containing a slope coefficient, constructing a variable reaching law containing a gain coefficient based on the variable sliding surface function, and constructing a longitudinal motion model.

[0049] S520, constructing a sliding mode control law according to the longitudinal motion model, the variable sliding surface function and the variable reaching law. For example, substituting the longitudinal motion model into the variable sliding surface function, and combining the variable reaching law, to obtain the sliding mode control law.

[0050] According to the real-time changes of the driving scene (such as TTC, time to collision), the sliding mode control parameters of the sliding mode controller are dynamically adjusted, so as to realize flexible control of the response characteristics of the sliding mode controller. Therefore, not only the adaptability and robustness of the control system are improved, but also the dynamic trade-off between "safety" and "comfort" in different driving scenarios is realized.

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

[0052] S530, dynamically adjusting the slope coefficient and the gain coefficient in the sliding mode control law by using a variable adjustment coefficient. The slope coefficient C and the gain coefficient K are values obtained by simulation calculation or real vehicle calibration, and the variable adjustment coefficient a (a is obtained by looking up table according to TTC) is introduced on the basis of the 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. c represents a slope coefficient of the variable sliding mode surface function.

[0058] Equation (2) satisfies the Hurwitz stability criterion, and c > 0. As shown in FIG. 2, on the sliding mode surface, e tends to e = 0, and Figure 2 = 0.

[0059] In the embodiments of the present application, c is not a fixed value, but a value dynamically adjusted according to the value of a obtained by looking up the TTC. Specifically, when the TTC is small (i.e., there is a risk of rear-end collision), the value of c increases, the sliding mode surface is steeper, the system response is faster, and the convergence speed is improved. When the TTC is large (i.e., the safety is high), the value of c decreases, the sliding mode surface is gentler, the system response is slower, and the comfort is improved. By introducing the variable adjustment coefficient a, the present application realizes the dynamic amplification or reduction of c.

[0060] Further, a variable reaching law is constructed according to the gain coefficient, the sign function and the variable sliding mode surface function. Exemplarily, the variable reaching law is expressed by the following formula: (3) wherein, k represents the gain coefficient in the variable reaching law, ε represents a positive number, S represents the variable sliding mode surface function, sgn( S ) represents the sign function of S . k is a positive number to ensure that the system state can quickly reach the sliding surface and maintain the sliding mode; ε is also a positive number.

[0061] Equation (3) is an exponential reaching law. Wherein, k is not a fixed value, but a value that can be dynamically adjusted according to the value of a obtained by looking up the TTC. When the TTC is small (such as < 2s), k the value of a increases to speed up the speed of the system state approaching the sliding mode surface; when the TTC is large, k the value of a decreases to reduce the approaching speed and reduce the control chattering. Similarly, by introducing the variable adjustment coefficient a, the dynamic amplification or reduction of k is realized.

[0062] In one embodiment, the method for constructing the longitudinal motion model comprises: adopting a second-order dynamic model to describe the integral relationship between the distance and speed related to the ego vehicle and the front vehicle, the speed and the acceleration, so as to construct a relationship between the current state error and the expected acceleration of the ego vehicle; wherein the current state error includes the vehicle distance error and the relative speed between the ego vehicle and the front vehicle.

[0063] ​​Exemplarily, the second-order dynamics model is represented by the following equations:

[0064] The relationship between the current state error and the desired acceleration of the ego vehicle is constructed, including:

[0065] wherein, x represents a state vector, Δ s represents the vehicle distance error between the ego vehicle and the preceding vehicle, i.e., the difference between the actual distance and the desired distance between the ego vehicle and the preceding vehicle. Δ s is an input value, which is the difference between the actual distance obtained by perception ranging and the current desired distance calculated. v re represents the relative speed between the ego vehicle and the preceding vehicle, i.e., the speed difference between the ego vehicle and the preceding vehicle. u represents the desired acceleration of the ego vehicle, i.e., the control input. A represents a matrix describing the dynamic relationship between the state variables; the first row in the matrix A represents that the rate of change of Δ re is v re . The second row in the matrix A represents that the rate of change of v re is 0, i.e., the relative speed remains unchanged without control input and external disturbance. B represents a control matrix describing the influence relationship of the control input on the state variables, the first row in the matrix B represents that Δ re is not directly affected by the control input. The second row in the matrix B represents that the rate of change of v re is -u, i.e., the acceleration of the ego vehicle is opposite to the desired acceleration. Φ is an external disturbance vector, τb is the brake time constant of the ego vehicle, representing the response time of the vehicle to braking. δ is the acceleration of the preceding vehicle. The first row in the matrix Φ represents that the rate of change of Δ is affected by -τb. The second row in the matrix Φ represents that the rate of change of v

[0001] is affected by δ, i.e., the acceleration of the preceding vehicle. The dynamics model describes the longitudinal motion of the vehicle under adaptive cruise control. By controlling the input u, i.e., the desired acceleration of the ego vehicle, the vehicle distance error Δ

[0002] and the relative speed v can be adjusted to achieve safe distance and speed matching with the preceding vehicle.

[0003] According to the above equations (4), (5), and (6), it is derived that:

[0004]

[0005] The equation (9) is simplified as:

[0069] The derivation process of formula (1) is introduced as follows: According to the variable sliding mode function S =c×e+ =0, let Δ s be e, be The sliding mode control law can be obtained:

[0070] Further:

[0071] Substitute the second-order dynamic model (i.e., substitute formula (7) and formula (8)) to obtain:

[0072] Since = - , wherein is the acceleration of the preceding vehicle, is the acceleration of the ego vehicle, so:

[0073] According to the variable reaching law, there is:

[0074] Further solve u , to obtain:

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

[0076] Wherein, a changes according to the TTC value, and the value is small when TTC is small. The value can be calibrated to provide a larger deceleration when there is a risk of rear-end collision.

[0077] In the adaptive cruise control method of the embodiment of the application, the sliding mode controller calculates the expected acceleration u according to the vehicle distance error and the relative speed, and then the actuator realizes the expected vehicle speed and vehicle distance through torque and brake pressure. This control method has good robustness and adaptability, and can effectively cope with the uncertainty of the acceleration of the preceding vehicle. On this basis, the variable sliding mode surface and the variable sliding mode reaching law can realize the customization demand of customers 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.) As shown, this is a comparison of simulation results for a following vehicle scenario. In Figure 3, the curve with △ (red line) corresponds to the control method with a fixed reaching law and a fixed sliding surface, while the curve with ○ (light blue) corresponds to the control method with a variable reaching law and a 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 5cIt can be seen from the above comparative diagram that the speed of the ego vehicle is relatively stable and does not appear to be jittering when the speed jitter perceived to the preceding vehicle is given, which reflects strong anti-interference performance; the distance reflects the positional relationship between the ego vehicle and the preceding vehicle, and the vehicle distance is not lower than a certain safety value in the entire simulation, indicating that no collision occurs. It can be known from the above comparative diagram that the application utilizes the sliding mode control, can decouple the scene, and individually adjusts the slope of the sliding surface, the convergence speed of the system and the chattering characteristics of the system. The application cancels the feedforward control, utilizes the rate of change of the speed to control the response of the acceleration, and can customize the positivity of the response. The application does not use the perceived acceleration information, and can reduce the problem of the sense of touch caused by the response of the perceived acceleration to the acceleration of the ego vehicle in view of the single V camera perception characteristics. Moreover, the control algorithm of the application is simple, and is easy to transplant and maintain.

[0080] The application further provides a kind of adaptive cruise control device. Illustratively, the adaptive cruise control device includes: Parameter acquisition device, for obtaining the state parameters related to ego vehicle and preceding vehicle; Adjusting coefficient determination module, for obtaining the current collision time between ego vehicle and preceding vehicle, and determining variable adjusting coefficient based on the collision time; Desired acceleration calculation module, for adjusting the sliding mode control parameter in sliding mode control law according to variable adjusting coefficient, and calculating desired acceleration according to state parameters; Control module, for setting the engine torque and brake pressure of ego vehicle according to desired acceleration, to control ego vehicle to follow preceding vehicle to travel.

[0081] It can be understood that the device of the embodiment corresponds to the adaptive cruise control method of the above-mentioned embodiment, and the optional items in the above-mentioned embodiment are also applicable to the embodiment, so they are not described here.

[0082] The application further provides a terminal device, illustratively, the terminal device includes a processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program, so that the terminal device executes the functions of each module in the above-mentioned adaptive cruise control method or the above-mentioned adaptive cruise control device.

[0083] The processor can be an integrated circuit chip with a processing capability of signals. The processor can be a general processor, including a central processing unit (CPU), a graphics processing unit (GPU), and a network processor (NP), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, a discrete gate or transistor logic device, a discrete hardware component, at least one of the above. The general processor can be a microprocessor or the processor can be any conventional processor or the like, which can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application.

[0084] The memory can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read only memory (PROM), an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM) and the like. The memory is used to store a computer program. After receiving an execution instruction, the processor can execute the computer program accordingly.

[0085] The present application also provides a computer readable storage medium for storing the computer program used in the terminal device. For example, the computer readable storage medium can include, but is not limited to, a U disk, a mobile hard disk, a read only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk and various program code storage media.

[0086] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can also be implemented by other means. The apparatus embodiments described above are only illustrative, for example, the flowcharts and structural diagrams in the drawings show the possible implementation architecture, function and operation of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logic function. It should also be noted that in alternative implementation, the functions noted in the block can also occur in different order from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the structural diagram and / or flowchart, and the combination of blocks in the structural diagram and / or flowchart, can be implemented by a dedicated hardware-based system for executing the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0087] In addition, each functional module or unit in the embodiments of the present 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 functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application.

[0089] The above description is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A method of adaptive cruise control, characterized by, The method comprises: acquiring state parameters related to the ego vehicle and the preceding vehicle; acquiring a current collision time between the ego 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; setting engine torque and brake pressure of the ego vehicle according to the desired acceleration, so as to control the ego vehicle to follow the preceding vehicle.

2. The adaptive cruise control method according to claim 1, characterized in that, The method for constructing the sliding mode control law comprises: constructing a variable sliding mode surface function containing a slope coefficient, constructing a variable reaching law containing a gain coefficient based on the variable sliding mode surface function, and constructing a longitudinal motion model; constructing the sliding mode control law according to the longitudinal motion model, the variable sliding mode surface function and the variable reaching law; adjusting the slope coefficient and the gain coefficient in the sliding mode control law through the variable adjustment coefficient.

3. The adaptive cruise control method according to claim 2, characterized in that, The sliding mode control law is expressed by the following formula: wherein, u represents a desired acceleration of the ego vehicle, a represents a variable adjustment coefficient, and c represents a slope coefficient of the variable sliding mode surface function, v re represents a relative velocity between the ego vehicle and the front vehicle, represents an acceleration of the front vehicle, k represents a gain coefficient in the variable reaching law, Δ s represents a vehicle distance error between the ego vehicle and the front vehicle, ε represents a positive number, S represents a variable sliding mode surface function, sgn( S ) represents S a sign function of 4. The adaptive cruise control method according to claim 2, characterized in that, The method for constructing the longitudinal motion model comprises: using a second-order dynamic model to describe the integral relationship between distance and speed, speed and acceleration related to the ego vehicle and the preceding vehicle, so as to construct a relationship between a current state error and a desired acceleration of the ego vehicle; wherein the current state error comprises a vehicle distance error and a relative speed between the ego vehicle and the preceding vehicle.

5. The adaptive cruise control method according to claim 4, characterized in that, The second-order dynamic model is expressed by the following formula: The method for constructing the relationship between the current state error and the desired acceleration of the ego vehicle comprises: wherein, x denotes a state vector, Δ s denotes a vehicle distance error between the ego vehicle and the preceding vehicle, v re denotes a relative speed between the ego vehicle and the preceding vehicle, A denotes a matrix describing dynamic relationships between state variables, u denotes a desired acceleration of the ego vehicle, B denotes a control matrix describing influence relationships of control inputs on state variables, Φ is an external disturbance vector, τb is a brake time constant of the ego vehicle, and δ is an acceleration of the preceding vehicle.

6. The adaptive cruise control method according to claim 2, wherein, constructing the variable sliding mode surface function according to the vehicle distance error between the ego vehicle and the preceding vehicle, a rate of change of the vehicle distance error and a slope coefficient of the variable sliding mode surface function; constructing the variable reaching law according to the gain coefficient, a sign function and the variable sliding mode surface function.

7. The adaptive cruise control method according to claim 6, characterized in that, The variable sliding mode surface function S Is expressed using the following equation: wherein e represents a vehicle distance error between the ego vehicle and the preceding vehicle; denotes a rate of change of the vehicle distance error; and c represents a slope coefficient of the variable sliding mode surface function. The variable reaching law is expressed by the following formula: wherein, k represents a gain coefficient in the variable reaching law, ε represents a positive number, S represents a variable sliding mode surface function, sgn( S ) represents S a sign function of 8. The adaptive cruise control method according to claim 1, characterized by, The method for acquiring the current collision time between the ego vehicle and the preceding vehicle, and determining the variable adjustment coefficient based on the collision time comprises: calculating the collision time according to a relative distance and a relative speed between the ego vehicle and the preceding vehicle; determining the variable adjustment coefficient by looking up a table according to the collision time.

9. An adaptive cruise control device characterized by comprising: The method comprises: a parameter acquisition device for acquiring state parameters related to the ego vehicle and the preceding vehicle; an adjustment coefficient determination module for acquiring a current collision time between the ego vehicle and the preceding vehicle, and determining a variable adjustment coefficient based on the collision time; a desired acceleration calculation module for 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; a control module for setting engine torque and brake pressure of the ego vehicle according to the desired acceleration, so as to control the ego vehicle to follow the preceding vehicle.

10. A vehicle characterized by comprising: The vehicle follows the preceding vehicle by using the adaptive cruise control method according to any one of claims 1-8.

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