Vehicle control methods, devices, equipment and storage media

Abstract

This application discloses a vehicle control method, apparatus, device, and storage medium. The vehicle control method includes: determining a current steering angle constraint using current driving condition data; wherein the current driving condition data includes at least one of a curvature representation value at the current moment and a vehicle speed at the current moment, the curvature representation value reflecting the curvature of the future driving segment; constructing a cost function for model predictive control using a vehicle state tracking error term and a steering angle control term at the current moment; determining the optimal steering angle when the steering angle control term meets the current steering angle constraint and the cost function meets preset conditions; and controlling the front wheel steering angle of the vehicle at the current moment to be the optimal steering angle. Through the above method, the current steering angle constraint can be adaptively adjusted according to changes in current driving condition data, achieving stable and comfortable tracking control of the vehicle.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a vehicle control method, apparatus, device, and storage medium. Background Technology

[0002] Existing autonomous driving control algorithms often employ algorithms such as PID, LQR (Linear Quadratic Regulator), and MPC (Model Predictive Control). Compared to PID and LQR algorithms, MPC can calculate the required steering angle while considering actual physical constraints and state constraints. However, the design of fixed control constraints makes it difficult to balance the following performance and comfort during vehicle operation. Summary of the Invention

[0003] The main technical problem addressed by this application is to provide a vehicle control method, device, equipment, and computer-readable storage medium that can adaptively adjust the current steering angle constraint according to changes in current driving condition data, thereby achieving stable and comfortable tracking control of the vehicle.

[0004] To address the aforementioned technical problems, this application provides a vehicle control method comprising: determining a current steering angle constraint using current driving condition data; wherein the current driving condition data includes at least one of a curvature representation value at the current moment and a vehicle speed at the current moment, the curvature representation value reflecting the curvature of the future driving segment of the vehicle; constructing a cost function for model predictive control using a vehicle state tracking error term and a steering angle control term at the current moment; determining the optimal steering angle when the steering angle control term meets the current steering angle constraint and the cost function meets preset conditions; and controlling the front wheel steering angle of the vehicle at the current moment to be the optimal steering angle.

[0005] Optionally, the current steering angle constraint is determined using the current driving condition data, including: determining the current adjustment coefficient using the current driving condition data; adjusting the reference steering angle using the current adjustment coefficient to obtain the current steering angle threshold; and generating the current steering angle constraint using the current steering angle threshold.

[0006] Optionally, the current driving condition data includes the curvature representation value at the current moment and the vehicle speed at the current moment; using the current driving condition data, determining the current adjustment coefficient includes: using the curvature representation value at the current moment to determine a first adjustment value; wherein the first adjustment value is positively correlated with the curvature representation value; using the vehicle speed at the current moment to determine a second adjustment value; wherein the second adjustment value is negatively correlated with the vehicle speed at the current moment; and using the product of the first adjustment value and the second adjustment value as the current adjustment coefficient.

[0007] Optionally, determining a first adjustment value using the curvature representation value at the current moment includes: multiplying the curvature representation value at the current moment by a first adjustment weight as the first adjustment value; and / or determining a second adjustment value using the vehicle speed at the current moment includes: determining the ratio of the vehicle speed at the current moment to a second adjustment weight; and using the difference between a preset value and the ratio as the second adjustment value.

[0008] Optionally, the reference steering angle is the maximum steering angle of the vehicle's steering actuator; and / or, the reference steering angle is adjusted using the current adjustment coefficient to obtain the current steering angle threshold, including: using the product of the current adjustment coefficient and the reference steering angle as the current steering angle threshold.

[0009] Optionally, the current driving condition data includes the curvature representation value at the current moment. The steps to obtain the curvature representation value at the current moment include: obtaining the planned curvature corresponding to several planned trajectory points in the pre-aiming time domain; and determining the curvature representation value at the current moment using at least each planned curvature.

[0010] Optionally, at least each planned curvature is used to determine the curvature representation value of each planned curvature at the current moment, including: combining each planned curvature to obtain a comprehensive planned curvature value; and using the comprehensive planned curvature value as the curvature representation value at the current moment.

[0011] Optionally, at least the curvature of each planned curvature is used to determine the curvature representation value at the current moment, including: combining the planned curvatures to obtain the comprehensive planned curvature value; and selecting the curvature representation value at the current moment from the comprehensive planned curvature value and the curvature representation value at the previous moment.

[0012] Optionally, the curvature characterization value at the current moment is selected from the comprehensive curvature value of the planned curvature and the curvature characterization value at the previous moment, including: taking the maximum value between the comprehensive curvature value of the planned curvature and the curvature characterization value at the previous moment as the curvature characterization value at the current moment.

[0013] Optionally, the composite value of the planned curvature is the maximum value among all planned curvatures.

[0014] Optionally, the step of obtaining the vehicle state tracking error term includes: using the system dynamics model to predict the vehicle state data at the current moment to obtain vehicle state prediction data for several future moments; obtaining the expected vehicle state data for each future moment; and generating the vehicle state tracking error term using the vehicle state prediction data and the expected vehicle state data for each future moment. The vehicle state data at the current moment includes the vehicle's sideslip angle at the current moment, the vehicle state prediction data for each future moment includes the predicted sideslip angle of the vehicle at the corresponding future moment, and the expected vehicle state data for each future moment includes the first expected sideslip angle of the vehicle at the corresponding future moment.

[0015] Optionally, for each future time moment, the step of obtaining the first expected centroid sideslip angle at the future time moment includes: determining the second expected centroid sideslip angle at the future time moment using the expected longitudinal velocity at the future time moment, the expected front wheel steering angle at the future time moment, and the first vehicle mechanical parameters; determining the maximum centroid sideslip angle at the future time moment constrained by the maximum lateral acceleration using the expected longitudinal velocity at the future time moment, the maximum lateral acceleration provided by the road surface, and the second vehicle mechanical parameters; and determining the first expected centroid sideslip angle at the future time moment based on the smaller of the absolute value of the second expected centroid sideslip angle at the future time moment and the absolute value of the maximum centroid sideslip angle at the future time moment.

[0016] Optionally, the vehicle state data at the current moment also includes the vehicle's yaw rate at the current moment, and the vehicle state prediction data for each future moment also includes the predicted yaw rate of the vehicle at the corresponding future moment, and the vehicle state expectation data for each future moment includes the vehicle's first expected yaw rate at the corresponding future moment; for each future moment, the step of obtaining the first expected yaw rate for the future moment includes: using the expected longitudinal velocity, the expected front wheel steering angle, and the third vehicle mechanical parameters at the future moment to determine the second expected yaw rate for the future moment; using the expected longitudinal velocity and the maximum lateral acceleration provided by the road surface at the future moment to determine the maximum yaw rate constrained by the maximum lateral acceleration at the future moment; selecting the smaller value from the absolute value of the second expected yaw rate and the absolute value of the maximum yaw rate at the future moment, and determining the first expected yaw rate for the future moment based on the smaller value.

[0017] To address the aforementioned technical problems, another technical solution adopted in this application is: providing a vehicle control device, comprising: a constraint determination module, used to determine the current steering angle constraint using current driving condition data; wherein the current driving condition data includes at least one of the curvature representation value at the current moment and the vehicle speed at the current moment, the curvature representation value being used to reflect the curvature of the future driving road segment; a construction module, used to construct a cost function for model predictive control using the vehicle state tracking error term and the steering angle control term at the current moment; an optimization module, used to determine the optimal steering angle when the steering angle control term meets the current steering angle constraint and the cost function meets preset conditions; and a control module, used to control the front wheel steering angle of the vehicle at the current moment to be the optimal steering angle.

[0018] To solve the above-mentioned technical problems, another technical solution adopted in this application is: to provide an electronic device, including a memory and a processor coupled to each other, wherein the memory stores program instructions; and the processor is used to execute the program instructions stored in the memory to implement the above-mentioned vehicle control method.

[0019] To solve the above-mentioned technical problems, another technical solution adopted in this application is to provide a computer-readable storage medium for storing program instructions that can be executed by a processor to implement the above-mentioned vehicle control method.

[0020] The above scheme determines the current steering angle constraint using current driving condition data; constructs the cost function of model predictive control using the vehicle state tracking error term and steering angle control term at the current moment; determines the optimal steering angle when the steering angle control term meets the current steering angle constraint and the cost function meets preset conditions; and controls the front wheel steering angle of the vehicle at the current moment to be the optimal steering angle. The current driving condition data includes at least one of the curvature representation value and the vehicle speed at the current moment, with the curvature representation value reflecting the curvature of the future driving segment. Since the current driving condition data is constantly changing as the vehicle moves, and the current steering angle constraint is determined using this data, this method allows for adaptive adjustment of the current steering angle constraint based on changes in the current driving condition data, achieving stable and comfortable tracking control of the vehicle. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating an embodiment of the vehicle control method provided in this application;

[0022] Figure 2 This is a schematic flowchart of another embodiment of the vehicle control method provided in this application;

[0023] Figure 3 This is a flowchart illustrating an embodiment of the method for obtaining the curvature representation value at the current moment provided in this application;

[0024] Figure 4 This is a flowchart illustrating an embodiment of the method for determining the current adjustment coefficient provided in this application;

[0025] Figure 5 This is a schematic diagram of the two-degree-of-freedom vehicle model provided in this application;

[0026] Figure 6 This is a schematic diagram of the framework of an embodiment of the vehicle control device provided in this application;

[0027] Figure 7 This is a schematic diagram of the framework of an embodiment of the electronic device provided in this application;

[0028] Figure 8 This is a schematic diagram of a framework of an embodiment of the computer-readable storage medium provided in this application. Detailed Implementation

[0029] To make the purpose, technical solution and effects of this application clearer and more explicit, the following describes this application in further detail with reference to the accompanying drawings and embodiments.

[0030] In the following description, specific details such as particular system architectures, interfaces, and technologies are presented for illustrative purposes rather than for limiting purposes, in order to provide a thorough understanding of this application.

[0031] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the term "more" in this document means two or more. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more of a plurality of objects. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C. "Several" means at least one. The terms "first," "second," etc., in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0032] Please see Figure 1 , Figure 1 This is a schematic flowchart of an embodiment of the vehicle control method provided in this application. It should be noted that if substantially the same result is achieved, the method of this application is not necessarily identical. Figure 1 The illustrated process sequence is limited. For example... Figure 1 As shown, the method includes the following steps:

[0033] S11: Determine the current steering angle constraint using the current driving condition data.

[0034] Current driving condition data includes at least one of the curvature representation value at the current moment and the vehicle speed at the current moment. Current driving condition data may include only the curvature representation value at the current moment, or it may include only the vehicle speed at the current moment, or it may include both the curvature representation value at the current moment and the vehicle speed at the current moment.

[0035] The curvature representation value at the current moment reflects the curvature of the road segment the vehicle will travel on in the future. A larger curvature representation value at the current moment indicates a larger curvature of the road segment the vehicle will travel on in the future. Conversely, a smaller curvature representation value at the current moment indicates a smaller curvature of the road segment the vehicle will travel on in the future.

[0036] The current vehicle speed is the longitudinal speed in the vehicle's coordinate system (along the vehicle's direction of travel).

[0037] The current steering angle constraint adapts to changes in current driving conditions.

[0038] In one implementation, the current steering angle constraint is determined by considering only the curvature representation value at the current moment. When the curvature representation value at the current moment is large, a larger steering angle is required to cope with the large curvature change, and the range of the current steering angle constraint can be increased accordingly to maintain vehicle stability and comfort. When the curvature representation value at the current moment is small, the range of the current steering angle constraint can be decreased accordingly, allowing the vehicle to travel with a smaller steering angle when driving on slightly curved roads, thereby maintaining vehicle stability and comfort.

[0039] In another implementation, the current steering angle constraint is determined by considering only the vehicle speed at the current moment. The lower the vehicle speed at the current moment, the smaller the impact on the current steering angle constraint; the higher the vehicle speed at the current moment, the smaller the range of the current steering angle constraint can be, in order to ensure the safety and stability of vehicle driving.

[0040] In another implementation, the current steering angle constraint can be determined by simultaneously considering two influencing factors: the curvature representation value at the current moment and the vehicle speed at the current moment.

[0041] S12: Construct the cost function of model predictive control using the vehicle state tracking error term and steering angle control term at the current moment.

[0042] The basic idea of ​​Model Predictive Control (MPC) is to build a dynamic model of the system and use this model at each control time step to predict the system's future behavior. Based on these predictions, an optimal control sequence can be generated. The system state is then adjusted by executing the first control action in the optimal control sequence, and then recalculated and executed at the next time step. This process is repeated to enable the system to optimize a specific performance metric over a future period.

[0043] In step S12, the vehicle state tracking error term is used to measure the difference between the predicted state and the desired state. The vehicle state tracking error term is obtained based on the predicted vehicle state data and the desired vehicle state data at several future times. The steering angle control term is the steering angle control sequence to be optimized.

[0044] S13: Determine the optimal steering angle when the steering angle control term meets the current steering angle constraint and the cost function meets the preset conditions.

[0045] In step S13, the optimal steering angle control sequence can be obtained when the steering angle control term meets the current steering angle constraint and the cost function meets the preset conditions. The first value in the optimal steering angle control sequence is taken as the optimal steering angle at the current moment. The optimal steering angle at the current moment meets the current steering angle constraint.

[0046] S14: Control the front wheel steering angle of the vehicle at the current moment to be the optimal steering angle.

[0047] Furthermore, after obtaining the optimal steering angle at the current moment, the front wheel steering angle of the vehicle at the current moment is controlled to be the optimal steering angle.

[0048] In this embodiment, the current steering angle constraint is determined using current driving condition data; a cost function for model predictive control is constructed using the vehicle state tracking error term and steering angle control term at the current moment; the optimal steering angle is determined when the steering angle control term meets the current steering angle constraint and the cost function meets preset conditions; and the front wheel steering angle of the vehicle at the current moment is controlled as the optimal steering angle. The current driving condition data includes at least one of the curvature representation value and the vehicle speed at the current moment, with the curvature representation value reflecting the curvature of the future driving segment. Since the current driving condition data changes continuously with the vehicle's movement, and the current steering angle constraint is determined using this data, this method allows for adaptive adjustment of the current steering angle constraint based on changes in the current driving condition data, achieving stable and comfortable tracking control of the vehicle.

[0049] Please see Figure 2 , Figure 2 This is a flowchart illustrating another embodiment of the vehicle control method provided in this application. It should be noted that if substantially the same result is achieved, the method of this application is not necessarily identical. Figure 2 The illustrated process sequence is limited. For example... Figure 2 As shown, the method includes the following steps:

[0050] S21: Obtain current driving condition data.

[0051] Current driving condition data includes at least one of the curvature representation value at the current moment and the vehicle speed at the current moment, wherein the curvature representation value at the current moment is used to reflect the curvature of the road segment the vehicle will travel on at the current moment. For example, current driving condition data may include both the curvature representation value at the current moment and the vehicle speed at the current moment.

[0052] Please see Figure 3 , Figure 3 This is a flowchart illustrating an embodiment of the method for obtaining the curvature representation value at the current moment provided in this application. Figure 3 As shown, the method includes the following steps:

[0053] S301: Determine the aiming time domain.

[0054] In step S301, the preview time and the vehicle speed at the current moment are obtained, and the preview time domain is determined based on the product of the preview time and the vehicle speed at the current moment. For example, the preview time domain can be represented by the following formula:

[0055] N pre =[v(k)*t pre ]

[0056] Where v(k) represents the vehicle speed at time k, and t pre This indicates the aiming time, and the symbol [·] indicates the rounding operation.

[0057] S302: Obtain the planning curvature corresponding to several planning trajectory points in the preview time domain.

[0058] The vehicle's trajectory planning module plans the vehicle's future driving trajectory, and the trajectory planning result includes the curvature of each planned trajectory point. Therefore, the planned curvature corresponding to several planned trajectory points in the preview time domain can be obtained through the vehicle's trajectory planning module.

[0059] For example, the planning curvature corresponding to several planned trajectory points in the pre-aiming time domain can be represented as {κ0(k),κ1(k),...,κ...} N (k)}.

[0060] S303: Determine the curvature representation value at the current moment by utilizing at least the planned curvatures in the preview time domain.

[0061] In one embodiment, the comprehensive value of the planned curvature is obtained by integrating the planned curvatures in the pre-aiming time domain; the comprehensive value of the planned curvature is used as the curvature representation value at the current moment.

[0062] For example, the average value of each planned curvature in the preview time domain can be used as the comprehensive value of the planned curvature.

[0063] For example, the weighted value of each planned curvature in the preview time domain can be used as the comprehensive value of the planned curvature.

[0064] For example, to further improve the depiction of the curvature of the future road segment of the vehicle at the current moment, the maximum value of each planned curvature in the preview time domain is used as the comprehensive value of the planned curvature.

[0065] In another embodiment, after obtaining the comprehensive value of the planned curvature by integrating the planned curvatures in the overall preview time domain, the curvature characterization value at the current time is further selected from the comprehensive value of the planned curvature and the curvature characterization value at the previous time by combining the curvature characterization value at the previous time.

[0066] Specifically, in this implementation, the maximum value between the planned curvature composite value and the curvature characterization value at the previous moment is used as the curvature characterization value at the current moment.

[0067] In this implementation, the curvature representation value at the current moment is determined by further incorporating the curvature representation value from the previous moment. On one hand, if the curvature representation value from the previous moment is large, and the comprehensive planned curvature value in the preview time domain suddenly decreases, then under the influence of the curvature representation value from the previous moment, the curvature representation value at the current moment will not immediately drop to a very low level, thus allowing for a smooth transition of curvature and avoiding abrupt changes in curvature. On the other hand, if the curvature representation value from the previous moment is small, and the comprehensive planned curvature value in the preview time domain suddenly becomes large, the curvature change can be detected in advance, allowing for timely adjustment of the current steering angle constraint in subsequent processes to achieve stable and comfortable tracking control of the vehicle.

[0068] In a specific application, the curvature representation value at the current moment is determined by the following formula:

[0069] κ max (k)=max{κ max (k-1),κ0(k),κ1(k),...,κ N (k)}

[0070] Where, k max (k) represents the curvature representation value at the current moment, k max (k-1) represents the curvature representation value at the previous moment.

[0071] S22: Determine the current adjustment coefficient using current driving condition data.

[0072] Please see Figure 4 , Figure 4 This is a flowchart illustrating an embodiment of the method for determining the current adjustment coefficient provided in this application. Figure 4 As shown, the method includes the following steps:

[0073] S401: Determine the first adjustment value using the curvature representation value at the current moment.

[0074] The first adjustment value is positively correlated with the curvature representation value at the current moment.

[0075] In step S401, the product of the curvature representation value at the current moment and the first adjustment weight is used as the first adjustment value.

[0076] The first adjustment weight can be seen as a gain factor of the curvature representation value at the current moment, used to amplify or reduce the influence of the curvature representation value at the current moment.

[0077] In one example, the first adjustment weight is a fixed value.

[0078] In another example, a first mapping relationship can be pre-constructed, which includes preset adjustment weights corresponding to different degrees of road curvature. The corresponding first adjustment weight is obtained from the first mapping relationship using the actual degree of road curvature. In the first mapping relationship, the greater the degree of road curvature, the greater the corresponding preset adjustment weight; the smaller the degree of road curvature, the smaller the corresponding preset adjustment weight.

[0079] S402: Determine the second adjustment value using the current vehicle speed.

[0080] The second adjustment value is negatively correlated with the vehicle speed at the current moment.

[0081] Step S402 includes: determining the ratio of the current vehicle speed to the second adjustment weight; and using the difference between a preset value and the ratio as the second adjustment value. For example, the preset value is 1.

[0082] The second adjustment weight can be viewed as a speed threshold used to adjust the impact of the vehicle speed at the current moment. The higher the current vehicle speed and the closer it is to the second adjustment weight, the closer the second adjustment value is to 0; the lower the current vehicle speed, the closer the second adjustment value is to a preset value. The second adjustment weight can be set according to actual needs.

[0083] S403: Use the product of the first adjustment value and the second adjustment value as the current adjustment coefficient.

[0084] Alternatively, in other embodiments, only the first adjustment value or only the second adjustment value may be used as the current adjustment coefficient.

[0085] The current adjustment coefficient determined in steps S401 to S403 is a value between 0 and 1.

[0086] S23: Adjust the reference steering angle using the current adjustment coefficient to obtain the current steering angle threshold.

[0087] The product of the current adjustment factor and the reference steering angle is used as the current steering angle threshold.

[0088] For example, the reference steering angle is the maximum steering angle that the vehicle's steering actuator can achieve.

[0089] For example, the current steering angle threshold can be expressed by the following formula:

[0090]

[0091] in, Indicates the current corner threshold, δ f,max k represents the maximum steering angle that the steering actuator can achieve. max(k) represents the curvature representation value at the current time, v(k) represents the vehicle speed at the current time, w1 represents the first adjustment weight, w2 represents the second adjustment weight, and w1*κ max (k) represents the first adjustment value. This indicates the second adjustment value.

[0092] S24: Generate the current steering angle constraint using the current steering angle threshold.

[0093] For example, the current steering angle constraint is: the absolute value of the steering angle is less than or equal to the current steering angle threshold.

[0094] S25: Construct the cost function of model predictive control using the vehicle state tracking error term and steering angle control term at the current moment.

[0095] Obtaining the vehicle state tracking error term may include the following steps:

[0096] Step 1: Use the system dynamics model to make predictions based on the vehicle state data at the current moment, and obtain vehicle state prediction data for several future moments.

[0097] In this embodiment, the centroid sideslip angle is considered to construct the system dynamics model, and the centroid sideslip angle is extended to the state space of the model predictive control. The process is briefly described below.

[0098] First, construct a system dynamics model that does not consider the sideslip angle of the center of mass, and then linearize it at the reference trajectory point to obtain the following expression:

[0099]

[0100] in, Y and X represent the lateral velocity, longitudinal velocity, yaw angle, yaw rate, lateral position, and longitudinal position of the vehicle in the vehicle coordinate system, respectively. and They are respectively The first derivatives of Y and X, δ f This indicates the steering angle of the vehicle's front wheels.

[0101] Furthermore, the system state variables are selected as follows: Control quantity U = [δ f Discretizing a continuous system yields a state-space equation of the following form:

[0102] ξ(k+1)=Aξ(j)+BU(k)

[0103] in:

[0104]

[0105] Furthermore, considering the β (centroid sideslip angle) constraint, the original system state variables are extended to... and The rate of change of the centroid sideslip angle satisfies the following expression:

[0106]

[0107] The extended state-space equation can then be expressed as:

[0108]

[0109] in:

[0110]

[0111] Furthermore, the extended state-space equations described above can be used based on the vehicle state data at the current moment (i.e., the aforementioned...). The vehicle state prediction data for several future times is obtained by iteratively predicting Y and X.

[0112] Step 2: Obtain the expected vehicle state data for each future time point.

[0113] The expected vehicle state data for each future time step includes the expected lateral velocity, expected longitudinal velocity, expected yaw angle, expected lateral position, expected longitudinal position, first expected centroid sideslip angle, and first expected yaw rate at the planned trajectory point for the corresponding future time step. The expected lateral velocity, expected longitudinal velocity, expected yaw angle, expected lateral position, and expected longitudinal position for each future time step are provided by the trajectory planning module. The first expected centroid sideslip angle and first expected yaw rate for each future time step are calculated based on the relevant expected data provided by the trajectory planning module for each future time step.

[0114] Please see Figure 5 , Figure 5 This is a schematic diagram of the two-degree-of-freedom vehicle model provided in this application. Figure 5 middle, Indicates the vehicle's yaw angle. This represents the lateral velocity in the vehicle's coordinate system. α represents the longitudinal velocity in the vehicle's coordinate system. f α represents the front wheel slip angle. r β represents the rear wheel slip angle, β represents the center of gravity slip angle, and δ represents the rear wheel slip angle. f The front wheel steering angle of the vehicle is represented by , a and b represent the front and rear wheelbases of the vehicle, and C represents the steering angle of the front wheels. f and C r I represents the lateral stiffness of the front and rear wheels. z The value m represents the vehicle's moment of inertia, and m represents the vehicle's mass.

[0115] In one embodiment, obtaining the first expected centroid sideslip angle for each future time point may further include the following sub-steps:

[0116] Sub-step one: using the expected longitudinal velocity at the future time, the expected front wheel steering angle at the future time, and the first vehicle mechanical parameters, determine the second expected centroid sideslip angle at the future time.

[0117] Specifically, the second expected centroid sideslip angle at future moments can be calculated using the following formula:

[0118]

[0119] In this formula, β r0 This represents the second expected centroid sideslip angle at a future time. and δ f Let b and m represent the expected longitudinal velocity and expected front wheel steering angle at future moments, respectively. The first vehicle mechanical parameters include the rear wheelbase b, the vehicle mass m, and the rear wheel lateral stiffness C. r Wheelbase L and stability coefficient k

[0120]

[0121] Sub-step two: using the expected longitudinal velocity at the future moment, the maximum lateral acceleration provided by the road surface, and the second vehicle mechanical parameters, determine the maximum centroid sideslip angle constrained by the maximum lateral acceleration at the future moment.

[0122] The desired sideslip angle will also be affected by the maximum lateral acceleration α that the ground can provide. y limit:

[0123] |a y |≤μg

[0124] Where μ represents the road surface adhesion coefficient and g represents the gravitational acceleration.

[0125] Specifically, the maximum centroid sideslip angle constrained by the maximum lateral acceleration at a future moment can be expressed as: The second vehicle mechanical parameters include the rear wheelbase b, the vehicle mass m, and the rear wheel lateral stiffness C. r and wheelbase L.

[0126] Sub-step three: Based on the smaller of the absolute value of the second expected centroid sideslip angle at the future time and the absolute value of the maximum centroid sideslip angle at the future time, determine the first expected centroid sideslip angle at the future time.

[0127] Specifically, the first expected centroid sideslip angle at a future time can be calculated using the following formula:

[0128]

[0129] Where, β r For the first expected centroid sideslip angle at a future moment, |β r0 | represents the absolute value of the second expected centroid sideslip angle at a future time. The absolute value of the maximum centroid sideslip angle at future moments is given by the sign function sgn, used to take δ. f The direction to determine β r The positive and negative directions.

[0130] In one embodiment, obtaining the first desired yaw rate for each future time moment may further include the following sub-steps:

[0131] Sub-step one: using the expected longitudinal velocity at the future time, the expected front wheel steering angle at the future time, and the third vehicle mechanical parameters, determine the second expected yaw rate at the future time.

[0132] Specifically, the second expected yaw rate at a future time can be calculated using the following formula:

[0133]

[0134] In this formula, The second expected yaw rate represents the future moment, and the second vehicle mechanical parameters include the wheelbase L and the stability coefficient k.

[0135] Sub-step two: using the expected longitudinal velocity at future time and the maximum lateral acceleration provided by the road surface, determine the maximum yaw rate constrained by the maximum lateral acceleration at future time.

[0136] The desired yaw rate will also be affected by the maximum lateral acceleration a that the ground can provide. y Constraints. Specifically, the maximum yaw rate constrained by the maximum lateral acceleration at a future time can be expressed as:

[0137] Sub-step three: Select the smaller value from the absolute value of the second expected yaw rate at the future time and the absolute value of the maximum yaw rate at the future time, and determine the first expected yaw rate at the future time based on the smaller value.

[0138] Specifically, the first expected yaw rate at a future moment can be calculated using the following formula:

[0139]

[0140] in, The first expected yaw rate at a future moment. Let be the absolute value of the second expected yaw rate at a future moment. The absolute value of the maximum yaw rate at a future time is given by the sign function sgn, which is used to take the value of δ. f To determine the direction The positive and negative directions.

[0141] Step 3: Using the vehicle state prediction data and the vehicle state expectation data at each future time, generate the vehicle state tracking error term.

[0142] Furthermore, in step S25, the cost function of the constructed model predictive control can be expressed as follows:

[0143]

[0144]

[0145] Where i|k represents the i-th predicted state obtained from the system dynamics model at the current time k. The cost function consists of a vehicle state tracking error term and a steering angle control term, with the weight matrices Q and R corresponding to the vehicle state tracking error term and steering angle control term, respectively, being positive semi-definite matrices. The equality constraints include the designed extended centroid sideslip angle constraint. The predicted initial state... Get the current state value Control Constraints The system's actuators meet physical limitations and adaptively adjust to changes in trajectory curvature. It should be noted that... That is, in step S23

[0146] S26: Determine the optimal steering angle when the steering angle control term meets the current steering angle constraint and the cost function meets the preset conditions.

[0147] S27: Control the front wheel steering angle of the vehicle at the current moment to be the optimal steering angle.

[0148] In this embodiment, the MPC algorithm is used to obtain the optimal or locally optimal solution under control constraints, achieving stable following during the vehicle's autonomous driving process, such as stable following during high-curvature cornering and extreme obstacle avoidance in memory parking cruise. An adaptive current steering angle constraint is designed using current driving condition data, enabling pre-adjustment of the current steering angle constraint in scenarios with large and rapid changes in curvature, achieving comfortable control under extreme conditions. By extending the center-of-gravity sideslip angle to the state space of the MPC algorithm and combining it with the maximum lateral acceleration provided by the road surface to calculate the first desired center-of-gravity sideslip angle and the first desired yaw rate, the vehicle's center-of-gravity sideslip angle and yaw rate can be guaranteed not to exceed the stable range in some extreme steering scenarios, especially when driving on roads with low adhesion coefficients, without reducing trajectory tracking accuracy.

[0149] Please see Figure 6 , Figure 6 This is a schematic diagram of a framework of an embodiment of the vehicle control device provided in this application. In this embodiment, the vehicle control device 60 includes a constraint determination module 61, a construction module 62, an optimization module 63, and a control module 64.

[0150] The constraint determination module 61 is used to determine the current steering angle constraint using current driving condition data; the current driving condition data includes at least one of the curvature representation value and the vehicle speed at the current moment, whereby the curvature representation value reflects the curvature of the future driving segment. The construction module 62 is used to construct the cost function of model predictive control using the vehicle state tracking error term and steering angle control term at the current moment. The optimization module 63 is used to determine the optimal steering angle when the steering angle control term meets the current steering angle constraint and the cost function meets preset conditions. The control module 64 is used to control the front wheel steering angle of the vehicle at the current moment to be the optimal steering angle.

[0151] Optionally, the constraint determination module 61 is used to determine the current adjustment coefficient using the current driving condition data; adjust the reference steering angle using the current adjustment coefficient to obtain the current steering angle threshold; and generate the current steering angle constraint using the current steering angle threshold.

[0152] Optionally, the current driving condition data includes the curvature representation value at the current moment and the vehicle speed at the current moment. The constraint determination module 61 is used to determine a first adjustment value using the curvature representation value at the current moment; wherein the first adjustment value is positively correlated with the curvature representation value; and to determine a second adjustment value using the vehicle speed at the current moment; wherein the second adjustment value is negatively correlated with the vehicle speed at the current moment; and to use the product of the first adjustment value and the second adjustment value as the current adjustment coefficient.

[0153] Optionally, the constraint determination module 61 is used to multiply the curvature representation value at the current moment by the first adjustment weight as the first adjustment value; and / or, the constraint determination module 61 is used to determine the ratio of the vehicle speed at the current moment to the second adjustment weight; and to use the difference between the preset value and the ratio as the second adjustment value.

[0154] Optionally, the reference steering angle is the maximum steering angle of the vehicle's steering actuator; and / or, the constraint determination module 61 is used to multiply the current adjustment coefficient by the reference steering angle as the current steering angle threshold.

[0155] Optionally, the current driving condition data includes the curvature representation value at the current moment. The constraint determination module 61 is used to obtain the planned curvature corresponding to several planned trajectory points in the preview time domain; and to determine the curvature representation value at the current moment using at least each planned curvature.

[0156] Optionally, the constraint determination module 61 is used to synthesize the various planned curvatures to obtain a comprehensive value of the planned curvature; and to use the comprehensive value of the planned curvature as the curvature representation value at the current moment.

[0157] Optionally, the constraint determination module 61 is used to synthesize the various planned curvatures to obtain a comprehensive planned curvature value; and to select the curvature characterization value at the current moment from the comprehensive planned curvature value and the curvature characterization value at the previous moment.

[0158] Optionally, the constraint determination module 61 is used to take the maximum value between the planned curvature composite value and the curvature characterization value at the previous moment as the curvature characterization value at the current moment.

[0159] Optionally, the composite value of the planned curvature is the maximum value among all planned curvatures.

[0160] Optionally, the construction module 62 is used to make predictions based on the vehicle state data at the current moment using the system dynamics model to obtain vehicle state prediction data at several future moments; obtain vehicle state expectation data at each future moment; and generate a vehicle state tracking error term using the vehicle state prediction data and the vehicle state expectation data at each future moment; wherein the vehicle state data at the current moment includes the vehicle's centroid sideslip angle at the current moment, the vehicle state prediction data at each future moment includes the predicted centroid sideslip angle of the vehicle at the corresponding future moment, and the vehicle state expectation data at each future moment includes the first expected centroid sideslip angle of the vehicle at the corresponding future moment.

[0161] Optionally, for each future time moment, the construction module 62 is used to determine the second expected centroid sideslip angle at the future time moment using the expected longitudinal velocity at the future time moment, the expected front wheel steering angle at the future time moment, and the first vehicle mechanical parameters; to determine the maximum centroid sideslip angle at the future time moment constrained by the maximum lateral acceleration using the expected longitudinal velocity at the future time moment, the maximum lateral acceleration provided by the road surface, and the second vehicle mechanical parameters; and to determine the first expected centroid sideslip angle at the future time moment based on the smaller of the absolute value of the second expected centroid sideslip angle at the future time moment and the absolute value of the maximum centroid sideslip angle at the future time moment.

[0162] Optionally, the vehicle state data at the current moment also includes the vehicle's yaw rate at the current moment, the vehicle state prediction data at each future moment also includes the predicted yaw rate of the vehicle at the corresponding future moment, and the vehicle state expectation data at each future moment includes the vehicle's first expected yaw rate at the corresponding future moment.

[0163] For each future moment, the construction module 62 is used to determine the second expected yaw rate at the future moment using the expected longitudinal velocity at the future moment, the expected front wheel steering angle at the future moment, and the third vehicle mechanical parameters; to determine the maximum yaw rate at the future moment constrained by the maximum lateral acceleration using the expected longitudinal velocity at the future moment and the maximum lateral acceleration provided by the road surface; and to select the smaller value from the absolute value of the second expected yaw rate at the future moment and the absolute value of the maximum yaw rate at the future moment, and to determine the first expected yaw rate at the future moment based on the smaller value.

[0164] It should be noted that the apparatus of this embodiment can perform the steps in the above method. For detailed descriptions of the relevant content, please refer to the method section above, which will not be repeated here.

[0165] Please see Figure 7 , Figure 7 This is a schematic diagram of a framework of an embodiment of the electronic device provided in this application. In this embodiment, the electronic device 70 includes a memory 71 and a processor 72.

[0166] Processor 72 can also be referred to as CPU (Central Processing Unit). Processor 72 may be an integrated circuit chip with signal processing capabilities. Processor 72 can also be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. A general-purpose processor can be a microprocessor, or processor 72 can be any conventional processor 72, etc.

[0167] The memory 71 in the electronic device 70 is used to store the program instructions required for the processor 72 to run.

[0168] The processor 72 is used to execute program instructions to implement the vehicle control method of this application.

[0169] Please see Figure 8 , Figure 8This is a schematic diagram of a framework of an embodiment of the computer-readable storage medium provided in this application. The computer-readable storage medium 80 of this application embodiment stores program instructions 81, which, when executed, implement the vehicle control method provided in this application. The program instructions 81 can be formed into a program file and stored in the aforementioned computer-readable storage medium 80 in the form of a software product, so that a computer device (which may be a personal computer, server, or network device, etc.) can execute all or part of the steps of the methods of various embodiments of this application. The aforementioned computer-readable storage medium 80 includes various media capable of storing program code, such as a USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or terminal devices such as computers, servers, mobile phones, and tablets.

[0170] The above scheme determines the current steering angle constraint using current driving condition data; constructs the cost function of model predictive control using the vehicle state tracking error term and steering angle control term at the current moment; determines the optimal steering angle when the steering angle control term meets the current steering angle constraint and the cost function meets preset conditions; and controls the front wheel steering angle of the vehicle at the current moment to be the optimal steering angle. The current driving condition data includes at least one of the curvature representation value and the vehicle speed at the current moment, with the curvature representation value reflecting the curvature of the future driving segment. Since the current driving condition data is constantly changing as the vehicle moves, and the current steering angle constraint is determined using this data, this method allows for adaptive adjustment of the current steering angle constraint based on changes in the current driving condition data, achieving stable and comfortable tracking control of the vehicle.

[0171] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0172] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatuses, and systems can be implemented in other ways. For example, the apparatus implementations described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or units may be electrical, mechanical, or other forms.

[0174] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0175] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0176] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0177] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A vehicle control method, characterized in that, The method includes: A first adjustment value is determined using the curvature representation value at the current moment; wherein the curvature representation value is used to reflect the curvature of the future driving segment of the vehicle, and the first adjustment value is positively correlated with the curvature representation value; A second adjustment value is determined using the vehicle speed at the current moment; wherein the second adjustment value is negatively correlated with the vehicle speed at the current moment. The product of the first adjustment value and the second adjustment value is used as the current adjustment coefficient; The reference steering angle is adjusted using the current adjustment coefficient to obtain the current steering angle threshold; Using the current steering angle threshold, generate the current steering angle constraint; The cost function of model predictive control is constructed using the vehicle state tracking error term and steering angle control term at the current moment. Determine the optimal steering angle when the steering angle control term meets the current steering angle constraint and the cost function meets the preset conditions; The front wheel steering angle of the vehicle at the current moment is controlled to be the optimal steering angle.

2. The method according to claim 1, characterized in that, The step of determining the first adjustment value using the curvature representation value at the current moment includes: The product of the curvature representation value at the current moment and the first adjustment weight is used as the first adjustment value; And / or, determining the second adjustment value using the vehicle speed at the current moment includes: Determine the ratio of the current vehicle speed to the second adjustment weight; The difference between the preset value and the ratio is used as the second adjustment value.

3. The method according to claim 1, characterized in that, The reference steering angle is the maximum steering angle of the vehicle's steering actuator; And / or, adjusting the reference steering angle using the current adjustment coefficient to obtain the current steering angle threshold includes: The product of the current adjustment coefficient and the reference steering angle is used as the current steering angle threshold.

4. The method according to claim 1, characterized in that, The steps for obtaining the curvature representation value at the current moment include: Obtain the planning curvature corresponding to several planned trajectory points within the pre-aiming time domain; At least using each of the planned curvatures, determine the curvature representation value at the current moment.

5. The method according to claim 4, characterized in that, The step of determining the curvature representation value at the current moment by utilizing at least each of the planned curvatures includes: By combining the various planned curvatures, a comprehensive value for the planned curvature is obtained; The comprehensive curvature value of the plan is used as the curvature representation value at the current moment.

6. The method according to claim 4, characterized in that, The step of determining the curvature representation value at the current moment by utilizing at least each of the planned curvatures includes: By combining the various planned curvatures, a comprehensive value for the planned curvature is obtained; The curvature characterization value at the current moment is selected from the planned curvature composite value and the curvature characterization value at the previous moment.

7. The method according to claim 6, characterized in that, The step of selecting the curvature characterization value at the current moment from the planned curvature composite value and the curvature characterization value at the previous moment includes: The maximum value between the planned curvature composite value and the curvature characterization value at the previous moment is used as the curvature characterization value at the current moment.

8. The method according to claim 5 or 6, characterized in that, The comprehensive value of the planned curvature is the maximum value among all the planned curvatures.

9. The method according to claim 1, characterized in that, The steps for obtaining the vehicle state tracking error term include: By using a system dynamics model to predict vehicle state data at the current moment, several future moment prediction data are obtained. Obtain the expected vehicle state data for each of the aforementioned future time moments; The vehicle state tracking error term is generated using the vehicle state prediction data and the vehicle state expectation data at each of the future times. The vehicle status data at the current moment includes the vehicle's sideslip angle at the current moment, the vehicle status prediction data at each of the future moments includes the predicted sideslip angle at the corresponding future moment, and the vehicle status expectation data at each of the future moments includes the first expected sideslip angle at the corresponding future moment.

10. The method according to claim 9, characterized in that, For each of the future moments, the step of obtaining the first expected centroid sideslip angle at the future moment includes: Using the expected longitudinal velocity at the future moment, the expected front wheel steering angle at the future moment, and the first vehicle mechanical parameters, the second expected center of gravity sideslip angle at the future moment is determined; Using the expected longitudinal velocity at the future moment, the maximum lateral acceleration provided by the road surface, and the second vehicle mechanical parameters, the maximum centroid sideslip angle constrained by the maximum lateral acceleration at the future moment is determined; The first expected centroid sideslip angle at the future time is determined based on the smaller of the absolute value of the second expected centroid sideslip angle at the future time and the absolute value of the maximum centroid sideslip angle at the future time.

11. The method according to claim 9, characterized in that, The vehicle status data at the current moment also includes the vehicle's yaw rate at the current moment, and the vehicle status prediction data at each of the future moments also includes the predicted yaw rate of the vehicle at the corresponding future moment, and the vehicle status expectation data at each of the future moments includes the first expected yaw rate of the vehicle at the corresponding future moment. For each of the future moments, the step of obtaining the first expected yaw rate at the future moment includes: Using the expected longitudinal velocity at the future moment, the expected front wheel steering angle at the future moment, and the third vehicle mechanical parameters, the second expected yaw rate at the future moment is determined; Using the expected longitudinal velocity at the future moment and the maximum lateral acceleration provided by the road surface, determine the maximum yaw rate constrained by the maximum lateral acceleration at the future moment; The smaller value is selected from the absolute value of the second desired yaw rate at the future time and the absolute value of the maximum yaw rate at the future time, and the first desired yaw rate at the future time is determined based on the smaller value.

12. A vehicle control device, characterized in that, The device includes: The constraint determination module is used to determine a first adjustment value using the curvature representation value at the current moment, wherein the curvature representation value reflects the curvature of the future driving segment of the vehicle, and the first adjustment value is positively correlated with the curvature representation value; determine a second adjustment value using the vehicle speed at the current moment, wherein the second adjustment value is negatively correlated with the vehicle speed at the current moment; use the product of the first adjustment value and the second adjustment value as the current adjustment coefficient; adjust the reference steering angle using the current adjustment coefficient to obtain the current steering angle threshold; and generate the current steering angle constraint using the current steering angle threshold. The module is used to construct the cost function of model predictive control using the vehicle state tracking error term and steering angle control term at the current moment; The optimization module is used to determine the optimal steering angle when the steering angle control term meets the current steering angle constraint and the cost function meets preset conditions; The control module is used to control the front wheel steering angle of the vehicle at the current moment to be the optimal steering angle.

13. An electronic device, characterized in that, Including interconnected memory and processor, The memory stores program instructions; The processor is used to execute program instructions stored in the memory to implement the method according to any one of claims 1-11.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program instructions that can be executed by a processor to implement the method of any one of claims 1-11.

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