Vehicle control method, device, equipment, medium and product in car-following state

Abstract

The present application provides a method, device, equipment, medium and product for controlling a vehicle in a following state, and relates to the field of autonomous driving control technology. The method includes: obtaining a first constraint relationship model of the current vehicle in the following state; obtaining the actual state and speed information of the current vehicle at the current moment, and substituting them into the first constraint relationship model to obtain a second constraint relationship model; obtaining the cost function of the current vehicle at the current moment; determining the acceleration of the current vehicle at N moments when the cost function outputs the minimum value under the constraint of the second constraint relationship model, and controlling the driver and the autonomous driving system to accelerate the current vehicle based on the acceleration of the current vehicle at the current moment included in the acceleration of the N moments. The method of the present application is used to solve the problem of how the driver and the autonomous driving system can collaboratively control the vehicle in the following state.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving control technology, and in particular to a method, device, equipment, medium and product for controlling a vehicle in a following state. Background Art

[0002] The popularization of autonomous driving functions helps reduce driver fatigue and traffic accidents, and is crucial to the safe driving of vehicles.

[0003] In existing technologies, autonomous driving systems are usually unable to fully cope with all scenarios in complex traffic environments, so the driver is required to coordinate control with the autonomous driving system to improve vehicle safety during driving.

[0004] However, existing technologies mainly focus on the collaborative control methods between the driver and the automatic driving system during the vehicle lane change process. In the vehicle following state, there is a lack of collaborative control methods between the driver and the automatic driving system. Summary of the Invention

[0005] The present application provides a vehicle control method, device, equipment, medium and product in a following state, which are used to solve the problem of how the driver and the automatic driving system can coordinately control the vehicle in the following state.

[0006] In a first aspect, the present application provides a vehicle control method in a car-following state, the method comprising:

[0007] Get the first constraint relationship model of the current vehicle in the following state, the first constraint relationship model includes: N state transition models, N+1 reference state models, N+1 relative acceleration definition models, N+1 relative speed definition models, N+1 distance definition models, N is a positive integer greater than or equal to 0, the kth state transition model is used to describe the relationship between the actual state of the current vehicle at the k+1th moment and the actual state of the current vehicle at the kth moment, the acceleration of the current vehicle at the kth moment, and the acceleration of the front vehicle at the kth moment, k is greater than or equal to 0 and less than or equal to N is an integer, where the 0th moment is the current moment, the actual state includes: the actual distance between the current vehicle and the vehicle in front, and the actual relative speed between the current vehicle and the vehicle in front; the mth reference state model is used to describe the relationship between the reference state of the current vehicle at the mth moment and the speed information of the current vehicle at the mth moment, where m is an integer greater than or equal to 0 and less than or equal to N+1; the reference state includes the reference distance and the reference relative speed, where the reference relative speed is 0; the speed information includes: the relative speed between the current vehicle and the vehicle in front at the mth moment, and the absolute speed of the current vehicle at the mth moment;

[0008] Obtain the actual state and speed information of the current vehicle at the current moment, and substitute them into the first constraint relationship model to obtain the second constraint relationship model;

[0009] Obtain the cost function of the current vehicle at the current moment. The cost function is used to indicate the difference between the N reference states and the N actual states of the current vehicle and the total acceleration when the current vehicle is controlled through N accelerations.

[0010] Determine the acceleration of the current vehicle at N moments when the cost function outputs a minimum value under the constraints of the second constraint relationship model, and control the driver and the automatic driving system to accelerate the current vehicle based on the acceleration of the current vehicle at the current moment included in the accelerations at the N moments.

[0011] In one possible design, controlling the driver and the automatic driving system to accelerate the current vehicle according to the acceleration of the current vehicle at the current moment included in the accelerations at the N moments includes:

[0012] Using the acceleration of the current vehicle at the current moment as the automatic driving system acceleration, and prompting the current vehicle acceleration at the current moment as the expected driver acceleration to the driver;

[0013] obtaining the acceleration input by the driver as the actual driver acceleration;

[0014] The actual driver acceleration and the automatic driving system acceleration are weighted to obtain the target acceleration, where the sum of the weight of the actual driver acceleration and the weight of the automatic driving system acceleration is 1;

[0015] The target acceleration is input into the braking system to accelerate the current vehicle.

[0016] In one possible design, the actual driver acceleration and the automated driving system acceleration are weighted to obtain a target acceleration, including:

[0017] Obtaining the actual distance between the current vehicle and the vehicle ahead at the current moment, and comparing the actual distance with the reference distance to obtain a comparison result;

[0018] determining a weight of the actual driver acceleration based on the comparison result and the reference distance, and determining a weight of the acceleration of the automated driving system based on the weight of the actual driver acceleration;

[0019] According to the weight of the actual driver acceleration and the weight of the automatic driving system acceleration, the actual driver acceleration and the automatic driving system acceleration are weighted and summed to obtain the target acceleration.

[0020] In one possible design, the weight of the actual driver acceleration is determined based on the comparison result and the reference distance, including:

[0021] If the comparison result is that the actual distance is greater than or equal to the reference distance, a weight of the actual driver acceleration is determined based on the difference between the actual distance and the reference distance and the reference distance, wherein the weight of the actual driver acceleration is positively correlated with the difference and negatively correlated with the reference distance;

[0022] If the comparison result is that the actual distance is less than the reference distance, the weight of the actual driver acceleration is determined to be 0.

[0023] In a possible design, the second constraint relationship model further includes:

[0024] The N accelerations of the current vehicle are all within a preset acceleration range, the relative speeds between the current vehicle and the vehicle in front at N moments are all within a preset speed range, and the distances between the current vehicle and the vehicle in front at N moments are all within a preset distance range.

[0025] In one possible design, the k-th state transition model is as follows:

[0026]

[0027] Among them, x k+1 is the actual state of the vehicle at time k+1, x k is the actual state of the vehicle at time k, u k is the acceleration of the vehicle at time k, a fk is the acceleration of the vehicle ahead at time k, I is the identity matrix, Δt is the time difference between two adjacent moments.

[0028] In one possible design, the reference distance is calculated as follows:

[0029]

[0030] Among them, d ref is the reference distance between the current vehicle and the vehicle ahead at the current moment, V rel is the relative speed between the current vehicle and the vehicle in front at the current moment, T delay is the braking delay time of the current vehicle, f(μ) is the preset braking coefficient, V is the absolute speed of the current vehicle at the current moment, a max is the current maximum deceleration of the vehicle.

[0031] In one possible design, obtaining the first constraint relationship model of the current vehicle in the following state includes:

[0032] Construct the kth state-space equation of the current vehicle. The kth state-space equation is used to describe the relationship between the differential result of the current vehicle's true state at the k+1th time, the true state of the current vehicle at the kth time, the acceleration of the current vehicle at the kth time, and the acceleration of the preceding vehicle at the kth time.

[0033] The kth state space equation is discretized to obtain the kth state transition model.

[0034] In a second aspect, the present application provides a vehicle control device in a following state, the device comprising:

[0035] An acquisition module is used to obtain the first constraint relationship model of the current vehicle in the following state. The first constraint relationship model includes: N state transition models, N+1 reference state models, N+1 relative acceleration definition models, N+1 relative speed definition models, and N+1 distance definition models. N is a positive integer greater than or equal to 0. The kth state transition model is used to describe the relationship between the actual state of the current vehicle at the k+1th moment and the actual state of the current vehicle at the kth moment, the acceleration of the current vehicle at the kth moment, and the acceleration of the front vehicle at the kth moment. k is greater than or equal to 0 and less than or an integer equal to N, the 0th moment is the current moment, the actual state includes: the actual distance between the current vehicle and the vehicle in front, the actual relative speed between the current vehicle and the vehicle in front, the mth reference state model is used to describe the relationship between the reference state of the current vehicle at the mth moment and the speed information of the current vehicle at the mth moment, m is an integer greater than or equal to 0 and less than or equal to N+1, the reference state includes the reference distance and the reference relative speed, the reference relative speed is 0, and the speed information includes: the relative speed between the current vehicle and the vehicle in front at the mth moment, and the absolute speed of the current vehicle at the mth moment;

[0036] A substitution module is used to obtain the actual state and speed information of the current vehicle at the current moment, and substitute it into the first constraint relationship model to obtain the second constraint relationship model;

[0037] A cost function module is used to obtain the cost function of the current vehicle at the current moment. The cost function is used to indicate the difference between N reference states and N actual states of the current vehicle and the total acceleration when the current vehicle is controlled through N accelerations;

[0038] The acceleration module is used to determine the acceleration of the current vehicle at N moments when the cost function outputs a minimum value under the constraints of the second constraint relationship model, and control the driver and the automatic driving system to accelerate the current vehicle based on the acceleration of the current vehicle at the current moment included in the acceleration of the N moments.

[0039] In one possible design, the acceleration module includes: an autonomous driving system acceleration module, an actual driver acceleration module, a weighting module, and an input module;

[0040] an automatic driving system acceleration module, configured to use the current vehicle acceleration at a current moment as the automatic driving system acceleration, and to prompt the driver with the current vehicle acceleration at a current moment as the expected driver acceleration;

[0041] an actual driver acceleration module, configured to obtain the acceleration input by the driver as the actual driver acceleration;

[0042] a weighting module, configured to weight the actual driver acceleration and the automatic driving system acceleration to obtain a target acceleration, wherein the sum of the weight of the actual driver acceleration and the weight of the automatic driving system acceleration is 1;

[0043] The input module is used to input the target acceleration into the braking system to accelerate the current vehicle.

[0044] In one possible design, the weighting module includes: a comparison module, a weight module and a summation module;

[0045] A comparison module is used to obtain the actual distance between the current vehicle and the vehicle in front at the current moment, and compare the actual distance with the reference distance to obtain a comparison result;

[0046] a weighting module, configured to determine a weight of the actual driver acceleration based on the comparison result and the reference distance, and to determine a weight of the acceleration of the automatic driving system based on the weight of the actual driver acceleration;

[0047] The summation module is used to perform weighted summation of the actual driver acceleration and the automatic driving system acceleration according to the weight of the actual driver acceleration and the weight of the automatic driving system acceleration to obtain a target acceleration.

[0048] In one possible design, the weight module includes: a first determination module and a second determination module;

[0049] a first determining module configured to, if the comparison result shows that the actual distance is greater than or equal to the reference distance, determine a weight of the actual driver acceleration based on a difference between the actual distance and the reference distance and the reference distance, wherein the weight of the actual driver acceleration is positively correlated with the difference and negatively correlated with the reference distance;

[0050] The second determining module is configured to determine the weight of the actual driver acceleration to be 0 if the comparison result shows that the actual distance is less than the reference distance.

[0051] In a possible design, the second constraint relationship model further includes:

[0052] The N accelerations of the current vehicle are all within a preset acceleration range, the relative speeds between the current vehicle and the vehicle in front at N moments are all within a preset speed range, and the distances between the current vehicle and the vehicle in front at N moments are all within a preset distance range.

[0053] In one possible design, the k-th state transition model is as follows:

[0054]

[0055] Among them, x k+1 is the actual state of the vehicle at time k+1, x k is the actual state of the vehicle at time k, u k is the acceleration of the vehicle at time k, a fk is the acceleration of the vehicle ahead at time k, I is the identity matrix, Δt is the time difference between two adjacent moments.

[0056] In one possible design, the reference distance is calculated as follows:

[0057]

[0058] Among them, d ref is the reference distance between the current vehicle and the vehicle ahead at the current moment, V rel is the relative speed between the current vehicle and the vehicle in front at the current moment, T delay is the braking delay time of the current vehicle, f(μ) is the preset braking coefficient, V is the absolute speed of the current vehicle at the current moment, a max is the current maximum deceleration of the vehicle.

[0059] In one possible design, the acquisition module includes: a building block and a discrete module;

[0060] A construction module is used to construct the kth state space equation of the current vehicle, where the kth state space equation is used to describe the relationship between the differential result of the actual state of the current vehicle at the k+1th time, the actual state of the current vehicle at the kth time, the acceleration of the current vehicle at the kth time, and the acceleration of the preceding vehicle at the kth time;

[0061] The discretization module is used to discretize the k-th state space equation to obtain the k-th state transition model.

[0062] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0063] Memory stores computer-executable instructions;

[0064] The processor executes the computer-executable instructions stored in the memory to implement a vehicle control method in a following state according to the first aspect of the invention.

[0065] In a fourth aspect, the present application provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement a vehicle control method in a following state according to the invention content of the first aspect.

[0066] In a fifth aspect, the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it is used to implement a vehicle control method in a following state according to the invention content of the first aspect.

[0067] The present application provides a vehicle control method, device, equipment, medium and product in a following state, the method comprising: obtaining a first constraint relationship model of the current vehicle in the following state, the first constraint relationship model comprising: N state transition models, N+1 reference state models, N+1 relative acceleration definition models, N+1 relative speed definition models, and N+1 distance definition models, where N is a positive integer greater than or equal to 0, the kth state transition model is used to describe the relationship between the actual state of the current vehicle at the k+1th moment and the actual state of the current vehicle at the kth moment, the acceleration of the current vehicle at the kth moment, and the acceleration of the front vehicle at the kth moment, k is an integer greater than or equal to 0 and less than or equal to N, the 0th moment is the current moment, the actual state comprises: the actual distance between the current vehicle and the front vehicle, the actual relative speed between the current vehicle and the front vehicle, the mth reference state model is used to describe the reference state of the current vehicle at the mth moment The relationship between the state and the speed information of the current vehicle at the mth moment, where m is an integer greater than or equal to 0 and less than or equal to N+1, the reference state includes a reference distance and a reference relative speed, the reference relative speed is 0, and the speed information includes: the relative speed between the current vehicle and the vehicle in front at the mth moment, and the absolute speed of the current vehicle at the mth moment; obtaining the actual state and speed information of the current vehicle at the current moment, and substituting them into the first constraint relationship model to obtain the second constraint relationship model; obtaining the cost function of the current vehicle at the current moment, the cost function is used to indicate the difference and total acceleration between the N reference states and the N actual states of the current vehicle when the current vehicle is controlled by N accelerations; determining the acceleration of the current vehicle at N moments when the cost function outputs a minimum value under the constraints of the second constraint relationship model, and controlling the driver and the automatic driving system to accelerate the current vehicle according to the acceleration of the current vehicle at the current moment included in the accelerations of the N moments.The following technical effects are achieved: by obtaining a first constraint relationship model of the current vehicle in a following state and substituting the actual state and speed information of the current vehicle at the current moment into the first constraint relationship model to obtain a second constraint relationship model, then, under the constraints of the second constraint relationship model, solving the N accelerations corresponding to the current vehicle from the current moment to the N-1th moment in the future when the output value of the cost function is minimized, and then controlling the driver and the automatic driving system to accelerate the current vehicle according to the acceleration corresponding to the current moment, achieving real-time adjustment of the current vehicle's acceleration during the following process to cope with various complex and changing traffic conditions, thereby further improving the safety and comfort of vehicle driving; according to the algorithm model for calculating the braking danger distance, the braking danger distance of the current vehicle is calculated in real time, and the braking danger distance is defined as a reference distance. Then, according to the dynamically changing reference distance, a state transition model is constructed to obtain the first constraint relationship model, so as to calculate the acceleration of the current vehicle at the current moment and multiple moments in the future according to the first constraint relationship model, so that the calculated value used to control the acceleration of the current vehicle is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0069] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0070] Figure 1 A flow chart of a vehicle control method in a following state provided in an embodiment of the present application Figure 1 ;

[0071] Figure 2 A flow chart of a vehicle control method in a following state provided in an embodiment of the present application Figure 2 ;

[0072] Figure 3 A schematic structural diagram of a vehicle control device in a following state provided by an embodiment of the present application;

[0073] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0074] Reference numerals:

[0075] 310-acquisition module; 320-substitution module; 330-cost function module; 340-acceleration module;

[0076] 410 - processor; 420 - memory; 430 - communication component; 440 - bus. DETAILED DESCRIPTION

[0077] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0078] In the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit differences. It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described in this application as "exemplary" or "for example" should not be interpreted as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way. In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more.

[0079] It should be noted that the phrase "at the time of..." in the embodiments of the present application can refer to the instantaneous occurrence of a certain situation or a period of time after the occurrence of a certain situation, and the embodiments of the present application do not specifically limit this. Furthermore, the vehicle control method in the car-following state provided in the embodiments of the present application is merely an example, and a vehicle control method in the car-following state may include more or less content.

[0080] The widespread adoption of autonomous driving technology can help reduce driver fatigue, lower the incidence of traffic accidents, and thus improve road safety. However, in complex traffic environments, existing autonomous driving systems still struggle to independently handle all possible situations. This requires drivers to coordinate control with the autonomous driving system in certain situations to ensure driving safety.

[0081] Current research and technological development focuses primarily on collaborative control methods between the driver and the automated driving system during lane changes, while less attention has been paid to collaborative control methods during car-following situations. This means that in the more common car-following situation, where the current vehicle maintains a certain distance and speed behind the vehicle ahead, how to effectively achieve collaborative control between the driver and the automated driving system to cope with unforeseen situations such as sudden deceleration or braking of the vehicle ahead remains a key issue that needs to be addressed.

[0082] Based on this, the embodiments of the present application propose a vehicle control method, device, equipment, medium, and product in a car-following state, which can be used in the field of autonomous driving control technology. The method aims to solve the above-mentioned technical problems of the existing technology, fill this gap in the existing technology, and provide a solution for achieving efficient coordinated control of the driver and the autonomous driving system in the vehicle-following state. The method optimizes the interaction mechanism between the driver and the autonomous driving system, enabling the two to work together more intelligently and efficiently. During the car-following process, the current vehicle acceleration is adjusted in real time to cope with various complex and changing traffic conditions, thereby further improving the safety and comfort of vehicle driving.

[0083] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0084] Figure 1 A flow chart of a vehicle control method in a following state provided in an embodiment of the present application Figure 1 .like Figure 1 As shown, the method includes:

[0085] S101: Obtain a first constraint relationship model of the current vehicle in a car-following state.

[0086] In the embodiments of the present application, the execution entity of a vehicle control method in a car-following state may be an electronic control unit (ECU) in the vehicle. This ECU may be a controller specifically designed for vehicle control in a car-following state, or it may be an existing brake control unit (BCU) in the vehicle, without specific limitation. In addition, the acceleration in the embodiments of the present application refers to the braking acceleration during the vehicle braking process. In the embodiments of the present application, for ease of description, the magnitude of the acceleration value refers to the absolute value of the acceleration.

[0087] The first constraint relationship model includes: N state transition models, N+1 reference state models, N+1 relative acceleration definition models, N+1 relative velocity definition models, and N+1 distance definition models, where N is a positive integer greater than or equal to 0.

[0088] The kth state transition model is used to describe the relationship between the actual state of the current vehicle at the k+1th moment and the actual state of the current vehicle at the kth moment, the acceleration of the current vehicle at the kth moment, and the acceleration of the preceding vehicle at the kth moment, where k is an integer greater than or equal to 0 and less than or equal to N, and the 0th moment is the current moment.

[0089] Specifically, the N state transition models include from the 0th moment (i.e., the current moment, k is 0) to the first moment (k is 1), the first moment to the second moment (k is 2), the second moment to the third moment (k is 3), and so on, until the N-1th moment (k is N-1) to the Nth moment (k is N), a total of N state transition models.

[0090] Furthermore, the k-th state transition model is as follows:

[0091]

[0092] Among them, x k+1 is the actual state of the vehicle at time k+1, x k is the actual state of the vehicle at time k, u k is the acceleration of the vehicle at time k, a fk is the acceleration of the vehicle ahead at time k, I is the identity matrix, Δt is the time difference between two adjacent moments and is a preset value. That is, the actual state of the current vehicle at time k+1 is calculated based on the actual state of the current vehicle at time k, the acceleration at time k, and the acceleration of the preceding vehicle at time k.

[0093] The actual state includes: the actual distance between the current vehicle and the vehicle in front, and the actual relative speed between the current vehicle and the vehicle in front, which can be obtained through the sensors of the current vehicle.

[0094] The mth reference state model is used to describe the relationship between the reference state of the current vehicle at the mth moment and the speed information of the current vehicle at the mth moment, where m is an integer greater than or equal to 0 and less than or equal to N+1. The reference state includes the reference distance and the reference relative speed, and the reference relative speed is 0. Reference state x ref =[d ref , v ref ] T=[d ref ,0] T , where d ref is the reference distance, v ref is the reference relative speed, which is set to 0. A reference relative speed of 0 means that for the autonomous driving system, in order to maintain a stable following state, the desired speed of the current vehicle is the same as the speed of the vehicle in front. In other words, the reference state at any moment m is determined by the reference distance d. ref and 0. Reference distance d ref It is calculated based on two variables: the relative speed between the current vehicle and the vehicle in front at the mth moment, the speed of the current vehicle at the mth moment, and three fixed parameters: system delay time, braking factor and maximum deceleration of the current vehicle.

[0095] Furthermore, the calculation formula of the reference distance is as follows:

[0096]

[0097] Among them, d ref is the reference distance between the current vehicle and the vehicle ahead at the current moment, V rel is the relative speed between the current vehicle and the vehicle in front at the current moment, T delay is the braking delay time of the current vehicle, which refers to the time required for the autonomous driving system to detect potential danger and start to perform braking operations. In the algorithm model (Seungwuk Moon model) for calculating the braking danger distance of the automatic emergency braking (AEB) system, this value can usually be set to 1.2 seconds. f(μ) is the preset braking coefficient (such as 0.2), V is the absolute speed of the current vehicle at the current moment, and a max The maximum deceleration of the current vehicle refers to the absolute value of the maximum braking acceleration that the current vehicle can achieve. In the Seungwuk Moon model, it can be set to 6m / S 2 . Reference distance d ref It can also be called the safety distance or braking danger distance between the current vehicle and the vehicle in front, which indicates the total distance traveled from the time the driver senses the danger and takes braking measures to the time the current vehicle comes to a complete stop at a specific speed.

[0098] Specifically, the existing Seungwuk Moon model can be used to calculate the reference distance between the current vehicle and the vehicle ahead at the current moment. As the calculation formula for the reference distance shows, since the speeds of the current vehicle and the vehicle ahead are constantly changing, the reference distance is also constantly changing.

[0099] The speed information includes: the relative speed between the current vehicle and the vehicle in front at the mth moment, and the absolute speed of the current vehicle at the mth moment.

[0100] Specifically, the actual distance between the current vehicle and the vehicle in front is calculated as follows:

[0101] d=d f -d h

[0102] Where d is the actual distance between the current vehicle and the vehicle in front, d f is the longitudinal position of the vehicle ahead, d h is the longitudinal position of the current vehicle. That is, the mth distance definition model is used to define the actual distance between the current vehicle and the preceding vehicle at the mth moment as the difference between the longitudinal position of the preceding vehicle at the mth moment and the longitudinal position of the current vehicle.

[0103] The relative speed between the current vehicle and the vehicle in front at the current moment Among them, V f is the speed of the vehicle in front, and V is the absolute speed of the current vehicle at the current moment. That is, the mth relative speed definition model is used to define the relative speed between the current vehicle and the vehicle in front at the mth moment as the difference between the speed of the vehicle in front at the mth moment and the absolute speed of the current vehicle. The actual state of the current vehicle x = [d, V rel ] T .

[0104] The relative acceleration between the current vehicle and the vehicle ahead Among them, a f is the acceleration of the vehicle in front, a h is the acceleration of the current vehicle at the current moment. That is, the mth relative acceleration definition model defines the relative acceleration between the current vehicle and the preceding vehicle at the mth moment as the difference between the preceding vehicle's acceleration and the current vehicle's acceleration at the mth moment.

[0105] S102: Obtain the actual state and speed information of the current vehicle at the current moment, and substitute them into the first constraint relationship model to obtain a second constraint relationship model.

[0106] Specifically, the ECU obtains the second constraint relationship model of the current vehicle at the current moment based on the actual state and speed information of the current vehicle at the current moment.

[0107] Furthermore, the second constraint relationship model also includes:

[0108] The N accelerations of the current vehicle are all within the preset acceleration range, the relative speeds between the current vehicle and the vehicle ahead at N moments are all within the preset speed range, and the distances between the current vehicle and the vehicle ahead at N moments are all within the preset distance range.

[0109] x min ≤x k ≤x max

[0110] u min ≤u k ≤u max

[0111] Among them, x k is the actual state of the current vehicle at time k, including the actual distance between the current vehicle and the vehicle in front, and the actual relative speed between the current vehicle and the vehicle in front at time k. min The minimum value of the preset state, including the minimum value of the preset distance range between the current vehicle and the vehicle in front, and the minimum value of the preset speed range between the current vehicle and the vehicle in front. max It is the maximum value of the preset state, including the maximum value of the preset distance range between the current vehicle and the vehicle in front, and the maximum value of the preset speed range between the current vehicle and the vehicle in front. k is the acceleration of the current vehicle at time k, u min is the minimum value of the preset acceleration range, u max It is the maximum value of the preset acceleration range.

[0112] S103: Obtain the cost function of the current vehicle at the current moment.

[0113] Specifically, the cost function is used to indicate the difference and total acceleration between N reference states and N actual states of the current vehicle when the current vehicle is controlled through N accelerations.

[0114] The cost function constructed based on Model Predictive Control (MPC) is:

[0115]

[0116] Among them, minJ is the output value of the cost function, is the reference state of the current vehicle at time k, Q = diag (Q d , Q v ), Q d is the error weight of the actual distance between the current vehicle and the vehicle in front, representing the penalty for the error in the actual distance; Q vis the error weight of the actual relative speed between the current vehicle and the preceding vehicle, representing the penalty for the error in the actual relative speed. R is the weight of the current vehicle's acceleration, representing the penalty for the input (acceleration). N is the actual state of the current vehicle at time N, is the reference state of the current vehicle at time N, and F is the terminal weight, which is used to control the error of the actual distance and the error of the actual relative speed at the final moment (time N).

[0117] In one possible implementation, F=Q.

[0118] S104. Determine the acceleration of the current vehicle at time N+1 when the cost function outputs a minimum value under the constraints of the second constraint relationship model, and control the driver and the automatic driving system to accelerate the current vehicle based on the acceleration of the current vehicle at the current moment included in the acceleration at time N+1.

[0119] Specifically, when the output value minJ of the cost function is the smallest, the current vehicle's corresponding distances from u0 to u from the current moment to the N-1th moment in the future are calculated. N-1 There are N accelerations in total. Then, the driver and the automatic driving system are controlled to accelerate the current vehicle according to the acceleration u0 corresponding to the current moment.

[0120] This embodiment provides a vehicle control method in a following state, the method comprising: obtaining a first constraint relationship model of the current vehicle in the following state, the first constraint relationship model comprising: N state transition models, N+1 reference state models, N+1 relative acceleration definition models, N+1 relative speed definition models, and N+1 distance definition models, N is a positive integer greater than or equal to 0, the kth state transition model is used to describe the relationship between the actual state of the current vehicle at the k+1th moment and the actual state of the current vehicle at the kth moment, the acceleration of the current vehicle at the kth moment, and the acceleration of the front vehicle at the kth moment, k is an integer greater than or equal to 0 and less than or equal to N, the 0th moment is the current moment, the actual state comprises: the actual distance between the current vehicle and the front vehicle, the actual relative speed between the current vehicle and the front vehicle, the mth reference state model is used to describe the relationship between the reference state of the current vehicle at the mth moment and the current vehicle. The method comprises the following steps: obtaining a first constraint relationship model and a second constraint relationship model; obtaining a cost function of the current vehicle at the current moment, the cost function being used to indicate the difference and total acceleration between N reference states and N actual states of the current vehicle when the current vehicle is controlled by N accelerations; determining the acceleration of the current vehicle at N moments when the cost function outputs a minimum value under the constraints of the second constraint relationship model, and controlling the driver and the automatic driving system to accelerate the current vehicle according to the acceleration of the current vehicle at the current moment included in the accelerations of the N moments.The following technical effects are achieved: by obtaining a first constraint relationship model of the current vehicle in a following state and substituting the actual state and speed information of the current vehicle at the current moment into the first constraint relationship model to obtain a second constraint relationship model, then, under the constraints of the second constraint relationship model, solving the N accelerations corresponding to the current vehicle from the current moment to the N-1th moment in the future when the output value of the cost function is minimized, and then controlling the driver and the automatic driving system to accelerate the current vehicle according to the acceleration corresponding to the current moment, achieving real-time adjustment of the current vehicle's acceleration during the following process to cope with various complex and changing traffic conditions, thereby further improving the safety and comfort of vehicle driving; according to the algorithm model for calculating the braking danger distance, the braking danger distance of the current vehicle is calculated in real time, and the braking danger distance is defined as a reference distance. Then, according to the dynamically changing reference distance, a state transition model is constructed to obtain the first constraint relationship model, so as to calculate the acceleration of the current vehicle at the current moment and multiple moments in the future according to the first constraint relationship model, so that the calculated value used to control the acceleration of the current vehicle is more accurate.

[0121] Figure 2 A flow chart of a vehicle control method in a following state provided in an embodiment of the present application Figure 2 In one possible example, Figure 2 As shown, this embodiment Figure 1 Based on the embodiment, how the driver and the automatic driving system accelerate the current vehicle according to the current acceleration is described in detail. Figure 2 As shown, the method includes:

[0122] S201: Obtain a first constraint relationship model of the current vehicle in a car-following state.

[0123] S202: Obtain the actual state and speed information of the current vehicle at the current moment, and substitute them into the first constraint relationship model to obtain a second constraint relationship model.

[0124] S203: Obtain the cost function of the current vehicle at the current moment.

[0125] S204 : Determine the acceleration of the current vehicle at N moments when the cost function outputs a minimum value under the constraints of the second constraint relationship model.

[0126] S201-S204 are similar to S101-S104 and will not be described in detail in this embodiment.

[0127] S205: Using the acceleration of the current vehicle at the current moment as the acceleration of the automatic driving system, and prompting the driver with the acceleration of the current vehicle at the current moment as the expected driver acceleration.

[0128] In the embodiment of the present application, the driver and the automatic driving system are controlled to accelerate the current vehicle according to the acceleration of the current vehicle at the current moment included in the accelerations at N moments. This includes using the acceleration of the current vehicle at the current moment as the acceleration of the automatic driving system, and using the acceleration of the current vehicle at the current moment as the expected driver acceleration prompt to the driver. Different levels of brake takeover prompts can be output to the driver according to the numerical value of the expected driver acceleration. For example: when the expected driver acceleration is less than the first preset acceleration threshold (such as 0.315m / S 2 ), output a first-level takeover prompt (such as: please lightly press the brake to take over); when the expected driver acceleration is greater than or equal to the first preset acceleration threshold and less than or equal to the second preset acceleration threshold (such as 1m / S 2 ), a second-level takeover prompt is output (such as: please brake to take over); when the expected driver acceleration is greater than the second preset acceleration threshold, a third-level takeover prompt is output (such as: please step on the brake deeply to take over).

[0129] Furthermore, the ECU can send a brake takeover prompt to a display device (such as the dashboard of the current vehicle), a lighting device or a vibration device, etc. The brake takeover prompt can be a text prompt, a voice prompt or light flashing of different frequencies, a vibration prompt, etc.

[0130] S206 : Acquire the acceleration input by the driver as the actual driver acceleration.

[0131] Specifically, when the driver receives a brake takeover prompt and inputs a braking acceleration to perform a braking operation on the current vehicle, the ECU can further obtain the acceleration input by the driver as the actual driver acceleration.

[0132] S207: Obtain the actual distance between the current vehicle and the vehicle ahead at the current moment, and compare the actual distance with the reference distance to obtain a comparison result.

[0133] In an embodiment of the present application, the ECU can weight the actual driver acceleration and the autonomous driving system acceleration to obtain a target acceleration, where the sum of the weights of the actual driver acceleration and the autonomous driving system acceleration is 1. Specifically, during the process of the driver and the autonomous driving system jointly controlling the current vehicle, it is necessary to ensure that the current vehicle can successfully avoid obstacles while also preventing the autonomous driving system from prematurely intervening in vehicle control and affecting the driver's normal driving operations. Therefore, it is necessary to minimize the interference of the autonomous driving system with the driver's normal driving. Therefore, in this embodiment, a reference distance is used as a calculation parameter to determine the weight of the actual driver acceleration. Specifically, the ECU can first obtain the actual distance between the current vehicle and the vehicle ahead at the current moment through a sensor, and then compare the actual distance with the reference distance to obtain a comparison result.

[0134] S208: If the comparison result is that the actual distance is greater than or equal to the reference distance, determine the weight of the actual driver acceleration according to the difference between the actual distance and the reference distance, and the reference distance.

[0135] In an embodiment of the present application, the ECU can determine the weight of the actual driver's acceleration based on the comparison result and the reference distance. Specifically, if the comparison result shows that the actual distance is greater than or equal to the reference distance, the weight of the actual driver's acceleration is determined based on the difference between the actual distance and the reference distance, as well as the reference distance. The weight of the actual driver's acceleration is positively correlated with the difference and negatively correlated with the reference distance. Specifically, when the actual distance is greater than or equal to the reference distance, the calculation formula for determining the weight of the actual driver's acceleration is:

[0136]

[0137] Where λ is the weight of the actual driver acceleration, d is the actual distance between the current vehicle and the vehicle in front, and d ref is the reference distance between the current vehicle and the vehicle in front at the current moment.

[0138] The formula for calculating the weight of the actual driver's acceleration shows that when the actual distance is greater than or equal to the reference distance, the weight of the actual driver's acceleration gradually decreases as the distance between the current vehicle and the vehicle ahead gradually decreases. Since the sum of the weight of the actual driver's acceleration and the weight of the automated driving system's acceleration is 1, the weight of the automated driving system's acceleration gradually increases to ensure the current vehicle's driving safety and avoid collisions with the vehicle ahead.

[0139] S209 : If the comparison result shows that the actual distance is less than the reference distance, determine that the weight of the actual driver acceleration is 0.

[0140] Specifically, when the actual distance is less than the reference distance, the current vehicle will be completely controlled by the autonomous driving system and brake in time to avoid a collision between the current vehicle and the vehicle in front.

[0141] S210. Determine the weight of the acceleration of the automatic driving system according to the weight of the actual driver acceleration.

[0142] In this embodiment, the sum of the weight of the actual driver acceleration and the weight of the automatic driving system acceleration is 1. After determining the weight of the actual driver acceleration at the current moment, the weight of the automatic driving system acceleration can be determined to be 1-λ.

[0143] S211. Based on the weight of the actual driver acceleration and the weight of the automatic driving system acceleration, perform a weighted summation of the actual driver acceleration and the automatic driving system acceleration to obtain a target acceleration.

[0144] In this embodiment, the calculation formula of the target acceleration is:

[0145] a=λa D +(1-λ)a A

[0146] Among them, a is the target acceleration for accelerating the current vehicle, a D is the actual driver acceleration, a A The acceleration of the autonomous driving system, here a A =u0.

[0147] S212: Input the target acceleration into the braking system to accelerate the current vehicle.

[0148] In this embodiment, after calculating the target acceleration, the ECU can perform braking control on the current vehicle according to the target acceleration.

[0149] Furthermore, obtaining the first constraint relationship model of the current vehicle in the following state also includes: constructing the kth state space equation of the current vehicle, the kth state space equation is used to describe the relationship between the differential result of the actual state of the current vehicle at the k+1th moment and the actual state of the current vehicle at the kth moment, the acceleration of the current vehicle at the kth moment, and the acceleration of the front vehicle at the kth moment.

[0150] Specifically, the kth state space equation is:

[0151]

[0152] Then, the kth state space equation is discretized to obtain the kth state transition model. Here, the forward Euler method can be used to Discretize it and get

[0153] An embodiment of the present application provides a vehicle control method in a following state, which can promptly prompt the driver to control the vehicle by sending a brake takeover prompt to a display device (such as the dashboard of the current vehicle), a lighting device, a vibration device, or other vehicle equipment; using a reference distance as a calculation parameter to determine the weight of the actual driver's acceleration, thereby achieving a smooth switching of control rights of the current vehicle between the driver and the automatic driving system, and improving the driving safety and comfort of the current vehicle; when the actual distance is greater than or equal to the reference distance, as the distance between the current vehicle and the vehicle in front gradually decreases, the weight of the actual driver's acceleration also gradually decreases, and when the actual distance is less than the reference distance, the current vehicle is completely controlled by the automatic driving system to achieve timely emergency braking and avoid a collision between the current vehicle and the vehicle in front.

[0154] In an embodiment of the present invention, the electronic device or main control device can be divided into functional modules according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present invention is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0155] Figure 3 This is a schematic diagram of the structure of a vehicle control device in a following state provided by an embodiment of the present application. Figure 3 As shown, the vehicle control device in the following state includes: an acquisition module 310, a substitution module 320, a cost function module 330 and an acceleration module 340;

[0156] The acquisition module 310 is used to obtain the first constraint relationship model of the current vehicle in the following state. The first constraint relationship model includes: N state transition models, N+1 reference state models, N+1 relative acceleration definition models, N+1 relative speed definition models, and N+1 distance definition models. N is a positive integer greater than or equal to 0. The kth state transition model is used to describe the relationship between the actual state of the current vehicle at the k+1th moment and the actual state of the current vehicle at the kth moment, the acceleration of the current vehicle at the kth moment, and the acceleration of the preceding vehicle at the kth moment. k is greater than or equal to 0, and An integer less than or equal to N, where the 0th moment is the current moment, the actual state includes: the actual distance between the current vehicle and the vehicle ahead, and the actual relative speed between the current vehicle and the vehicle ahead. The mth reference state model is used to describe the relationship between the reference state of the current vehicle at the mth moment and the speed information of the current vehicle at the mth moment, where m is an integer greater than or equal to 0 and less than or equal to N+1. The reference state includes a reference distance and a reference relative speed, where the reference relative speed is 0. The speed information includes: the relative speed between the current vehicle and the vehicle ahead at the mth moment, and the absolute speed of the current vehicle at the mth moment.

[0157] The substitution module 320 is used to obtain the actual state and speed information of the current vehicle at the current moment and substitute it into the first constraint relationship model to obtain the second constraint relationship model;

[0158] A cost function module 330 is used to obtain a cost function of the current vehicle at a current moment, where the cost function is used to indicate the difference between N reference states and N actual states of the current vehicle and the total acceleration when the current vehicle is controlled using N accelerations;

[0159] The acceleration module 340 is used to determine the acceleration of the current vehicle at N moments when the cost function outputs a minimum value under the constraints of the second constraint relationship model, and control the driver and the automatic driving system to accelerate the current vehicle based on the acceleration of the current vehicle at the current moment included in the acceleration of the N moments.

[0160] In one possible design, the acceleration module 340 includes: an autonomous driving system acceleration module, an actual driver acceleration module, a weighting module, and an input module;

[0161] an automatic driving system acceleration module, configured to use the current vehicle acceleration at a current moment as the automatic driving system acceleration, and to prompt the driver with the current vehicle acceleration at a current moment as the expected driver acceleration;

[0162] an actual driver acceleration module, configured to obtain the acceleration input by the driver as the actual driver acceleration;

[0163] a weighting module, configured to weight the actual driver acceleration and the automatic driving system acceleration to obtain a target acceleration, wherein the sum of the weight of the actual driver acceleration and the weight of the automatic driving system acceleration is 1;

[0164] The input module is used to input the target acceleration into the braking system to accelerate the current vehicle.

[0165] In one possible design, the weighting module includes: a comparison module, a weight module and a summation module;

[0166] A comparison module is used to obtain the actual distance between the current vehicle and the vehicle in front at the current moment, and compare the actual distance with the reference distance to obtain a comparison result;

[0167] a weighting module, configured to determine a weight of the actual driver acceleration based on the comparison result and the reference distance, and to determine a weight of the acceleration of the automatic driving system based on the weight of the actual driver acceleration;

[0168] The summation module is used to perform weighted summation of the actual driver acceleration and the automatic driving system acceleration according to the weight of the actual driver acceleration and the weight of the automatic driving system acceleration to obtain a target acceleration.

[0169] In one possible design, the weight module includes: a first determination module and a second determination module;

[0170] a first determining module configured to, if the comparison result shows that the actual distance is greater than or equal to the reference distance, determine a weight of the actual driver acceleration based on a difference between the actual distance and the reference distance and the reference distance, wherein the weight of the actual driver acceleration is positively correlated with the difference and negatively correlated with the reference distance;

[0171] The second determining module is configured to determine the weight of the actual driver acceleration to be 0 if the comparison result shows that the actual distance is less than the reference distance.

[0172] In a possible design, the second constraint relationship model further includes:

[0173] The N accelerations of the current vehicle are all within a preset acceleration range, the relative speeds between the current vehicle and the vehicle in front at N moments are all within a preset speed range, and the distances between the current vehicle and the vehicle in front at N moments are all within a preset distance range.

[0174] In one possible design, the k-th state transition model is as follows:

[0175]

[0176] Among them, x k+1 is the actual state of the vehicle at time k+1, xk is the actual state of the vehicle at time k, u k is the acceleration of the vehicle at time k, a fk is the acceleration of the vehicle ahead at time k, I is the identity matrix, Δt is the time difference between two adjacent moments.

[0177] In one possible design, the reference distance is calculated as follows:

[0178]

[0179] Among them, d ref is the reference distance between the current vehicle and the vehicle ahead at the current moment, V rel is the relative speed between the current vehicle and the vehicle in front at the current moment, T delay is the braking delay time of the current vehicle, f(μ) is the preset braking coefficient, V is the absolute speed of the current vehicle at the current moment, a max is the current maximum deceleration of the vehicle.

[0180] In one possible design, the acquisition module 310 includes: a building module and a discrete module;

[0181] A construction module is used to construct the kth state space equation of the current vehicle, where the kth state space equation is used to describe the relationship between the differential result of the actual state of the current vehicle at the k+1th time, the actual state of the current vehicle at the kth time, the acceleration of the current vehicle at the kth time, and the acceleration of the preceding vehicle at the kth time;

[0182] The discretization module is used to discretize the k-th state space equation to obtain the k-th state transition model.

[0183] This embodiment provides a vehicle control device in a car-following state, which can execute a vehicle control method in a car-following state in the above embodiment. Its implementation principle and technical effects are similar, and will not be described in detail in this embodiment.

[0184] In a specific implementation of the aforementioned vehicle control device in a car-following state, each module may be implemented as a processor, and the processor may execute computer-executable instructions stored in a memory, so that the processor executes the aforementioned vehicle control method in a car-following state.

[0185] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 4As shown, the electronic device includes: at least one processor 410 and a memory 420. The electronic device also includes a communication component 430. The processor 410, the memory 420 and the communication component 430 are connected via a bus 440.

[0186] In a specific implementation process, at least one processor 410 executes the computer-executable instructions stored in the memory 420, so that at least one processor 410 executes a vehicle control method in a following state as executed by the electronic device side above.

[0187] The specific implementation process of the processor 410 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0188] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented by a hardware processor or implemented by a combination of hardware and software modules in the processor.

[0189] The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk storage.

[0190] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0191] The above-mentioned functions implemented by the electronic device and the main control device have introduced the solutions provided by the embodiments of the present invention. It can be understood that in order to implement the above-mentioned functions, the electronic device or the main control device includes hardware structures and / or software modules corresponding to the execution of each function. In combination with the units and algorithm steps of the various examples described in the embodiments disclosed in the embodiments of the present invention, the embodiments of the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiments of the present invention.

[0192] The present application also provides a computer-readable storage medium, which stores computer-executable instructions. When a processor executes the computer-executable instructions, it is used to implement the vehicle control method in the following state as described above.

[0193] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0194] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in an electronic device or a main control device.

[0195] The present application also provides a computer program product, which includes a computer program. The computer program is stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium. At least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0196] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0197] So far, the technical solution of the present application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the scope of protection of the present application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solution of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solution to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A vehicle control method in a following state, characterized in that: include: Obtain a first constraint relationship model of the current vehicle in a following state, the first constraint relationship model including: N state transition models, N+1 reference state models, N+1 relative acceleration definition models, N+1 relative speed definition models, and N+1 distance definition models, where N is a positive integer greater than or equal to 0, and the kth state transition model is used to describe the relationship between the actual state of the current vehicle at the k+1th moment and the actual state of the current vehicle at the kth moment, the acceleration of the current vehicle at the kth moment, and the acceleration of the preceding vehicle at the kth moment, where k is an integer greater than or equal to 0 and less than or equal to N, and the 0th moment is an integer greater than or equal to 0 and less than or equal to N. The mth moment is the current moment, the actual state includes: the actual distance between the current vehicle and the vehicle in front, and the actual relative speed between the current vehicle and the vehicle in front; the mth reference state model is used to describe the relationship between the reference state of the current vehicle at the mth moment and the speed information of the current vehicle at the mth moment, where m is an integer greater than or equal to 0 and less than or equal to N+1; the reference state includes a reference distance and a reference relative speed, where the reference relative speed is 0; and the speed information includes: the relative speed between the current vehicle and the vehicle in front at the mth moment, and the absolute speed of the current vehicle at the mth moment; Obtaining the actual state and speed information of the current vehicle at the current moment, and substituting them into the first constraint relationship model to obtain a second constraint relationship model; Obtaining a cost function of the current vehicle at a current moment, the cost function being used to indicate differences between the N reference states and the N actual states of the current vehicle and a total acceleration when the current vehicle is controlled using the N accelerations; Determine the acceleration of the current vehicle at N moments when the cost function outputs a minimum value under the constraints of the second constraint relationship model, and control the driver and the automatic driving system to accelerate the current vehicle based on the acceleration of the current vehicle at the current moment included in the accelerations at the N moments.

2. The method according to claim 1, characterized in that The controlling the driver and the automatic driving system to accelerate the current vehicle according to the acceleration of the current vehicle at the current moment included in the accelerations at the N moments includes: using the acceleration of the current vehicle at the current moment as the automatic driving system acceleration, and prompting the acceleration of the current vehicle at the current moment as the expected driver acceleration to the driver; acquiring the acceleration input by the driver as the actual driver acceleration; weighting the actual driver acceleration and the automatic driving system acceleration to obtain a target acceleration, wherein the sum of the weight of the actual driver acceleration and the weight of the automatic driving system acceleration is 1; The target acceleration is input into a braking system to accelerate the current vehicle.

3. The method according to claim 2, characterized in that The weighting the actual driver acceleration and the automatic driving system acceleration to obtain a target acceleration includes: Obtaining an actual distance between the current vehicle and the preceding vehicle at a current moment, and comparing the actual distance with the reference distance to obtain a comparison result; determining a weight of the actual driver acceleration based on the comparison result and the reference distance, and determining a weight of the automatic driving system acceleration based on the weight of the actual driver acceleration; According to the weight of the actual driver acceleration and the weight of the automatic driving system acceleration, a weighted sum is performed on the actual driver acceleration and the automatic driving system acceleration to obtain the target acceleration.

4. The method according to claim 3, characterized in that The determining the weight of the actual driver acceleration according to the comparison result and the reference distance includes: If the comparison result is that the actual distance is greater than or equal to the reference distance, determining a weight of the actual driver acceleration based on a difference between the actual distance and the reference distance and the reference distance, wherein the weight of the actual driver acceleration is positively correlated with the difference and negatively correlated with the reference distance; If the comparison result is that the actual distance is less than the reference distance, the weight of the actual driver acceleration is determined to be 0.

5. The method according to claim 4, characterized in that The second constraint relationship model also includes: The N accelerations of the current vehicle are all within a preset acceleration range, the relative speed between the current vehicle and the vehicle in front at the N+1 moment is all within a preset speed range, and the distance between the current vehicle and the vehicle in front at the N+1 moment is all within a preset distance range.

6. The method according to any one of claims 1 to 5, characterized in that The k-th state transition model is as follows: Among them, the x k+1 is the actual state of the vehicle at time k+1, x k is the actual state of the vehicle at time k, u k is the acceleration of the vehicle at time k, a fk is the acceleration of the vehicle ahead at time k, I is the identity matrix, Δt is the time difference between two adjacent moments.

7. The method according to any one of claims 1 to 5, characterized in that The calculation formula of the reference distance is as follows: Among them, d ref is the reference distance between the current vehicle and the vehicle ahead at the current moment, V rel is the relative speed between the current vehicle and the preceding vehicle at the current moment, T delay is the braking delay time of the current vehicle, f(μ) is the preset braking coefficient, V is the absolute speed of the current vehicle at the current moment, a max is the maximum deceleration of the current vehicle.

8. The method according to any one of claims 1 to 5, characterized in that The obtaining of the first constraint relationship model of the current vehicle in the car-following state includes: Constructing a kth state-space equation for the current vehicle, wherein the kth state-space equation is used to describe a relationship between a differential result of the true state of the current vehicle at the k+1th time, the true state of the current vehicle at the kth time, the acceleration of the current vehicle at the kth time, and the acceleration of the preceding vehicle at the kth time; Discretization is performed on the kth state space equation to obtain the kth state transition model.

9. A vehicle control device in a following state, characterized in that: include: An acquisition module is used to obtain a first constraint relationship model of the current vehicle in a following state, wherein the first constraint relationship model includes: N state transition models, N+1 reference state models, N+1 relative acceleration definition models, N+1 relative speed definition models, and N+1 distance definition models, where N is a positive integer greater than or equal to 0, and the kth state transition model is used to describe the relationship between the actual state of the current vehicle at the k+1th moment and the actual state of the current vehicle at the kth moment, the acceleration of the current vehicle at the kth moment, and the acceleration of the preceding vehicle at the kth moment, where k is an integer greater than or equal to 0 and less than or equal to N, and the kth state transition model is used to describe the relationship between the actual state of the current vehicle at the k+1th moment and the actual state of the current vehicle at the kth moment, the acceleration of the current vehicle at the kth moment, and the acceleration of the preceding vehicle at the kth moment, and the k is an integer greater than or equal to 0 and less than or equal to N. The 0th moment is the current moment, the actual state includes: the actual distance between the current vehicle and the vehicle in front, and the actual relative speed between the current vehicle and the vehicle in front; the mth reference state model is used to describe the relationship between the reference state of the current vehicle at the mth moment and the speed information of the current vehicle at the mth moment, where m is an integer greater than or equal to 0 and less than or equal to N+1; the reference state includes a reference distance and a reference relative speed, where the reference relative speed is 0; and the speed information includes: the relative speed between the current vehicle and the vehicle in front at the mth moment, and the absolute speed of the current vehicle at the mth moment; a substitution module, configured to obtain the actual state and speed information of the current vehicle at a current moment, and substitute the information into the first constraint relationship model to obtain a second constraint relationship model; a cost function module, configured to obtain a cost function of the current vehicle at a current moment, the cost function being configured to indicate differences between the N reference states and the N actual states of the current vehicle and a total acceleration when the current vehicle is controlled using the N accelerations; an acceleration module for determining, under the constraints of the second constraint relationship model, the acceleration of the current vehicle at N moments when the cost function outputs a minimum value, and controlling the driver and the automatic driving system to accelerate the current vehicle based on the acceleration of the current vehicle at the current moment included in the accelerations at the N moments.

10. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the vehicle control method in the following state according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the vehicle control method in the following state according to any one of claims 1 to 8.

12. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the vehicle control method in the following state as claimed in any one of claims 1 to 8 is implemented.

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