Vehicle control method and device, equipment and storage medium

Abstract

The invention discloses a vehicle control method, device and equipment and a storage medium, and the vehicle control method comprises the following steps: determining a current steering angle constraint by using current driving condition data; wherein the current driving condition data comprises at least one of a curvature characterization value at the current moment and a vehicle speed at the current moment, and the curvature characterization value is used for reflecting the curvature of a future driving road section of the vehicle; using a vehicle state tracking error item and a steering angle control item at the current moment to construct a cost function of model prediction control; determining an optimal steering angle when the steering angle control item accords with the current steering angle constraint and the cost function accords with a preset condition; and the front wheel steering angle of the vehicle at the current moment is controlled to be the optimal steering angle. In this way, the current steering angle constraint can be adaptively adjusted according to the change of the current driving condition data, and stable and comfortable tracking control over the vehicle is achieved.

Description

Technical Field

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

[0002] Existing autonomous driving control algorithms often use PID, LQR (Linear Quadratic Regulator), MPC (Model Predictive Control) and other algorithms. Compared with PID and LQR algorithms, MPC can solve the required steering angle while considering actual physical constraints and state constraints. However, fixed control constraint design makes it difficult to take into account both the followability and comfort of the vehicle during driving. Summary of the invention

[0003] The main technical problem solved by the present application is to provide a vehicle control method, device, equipment and computer-readable storage medium, which can adaptively adjust the current steering angle constraint according to the change of current driving condition data to achieve stable and comfortable tracking control of the vehicle.

[0004] In order to solve the above technical problems, a technical solution adopted in the present application is: to provide a vehicle control method, the method comprising: using current driving condition data to determine the current steering angle constraint; wherein the current driving condition data includes at least one of the curvature characterization value at the current moment and the vehicle speed at the current moment, and the curvature characterization value is used to reflect the curvature of the vehicle's future driving section; using the vehicle state tracking error term and the steering angle control term at the current moment to construct a cost function of model predictive control; determining the optimal steering angle when the steering angle control term meets the current steering angle constraint and the cost function meets the preset conditions; 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: using the current driving condition data to determine the current adjustment coefficient; adjusting the reference steering angle using the current adjustment coefficient to obtain a 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 characterization value at the current moment and the vehicle speed at the current moment; using the current driving condition data, determining the current adjustment coefficient, including: using the curvature characterization value at the current moment to determine a first adjustment value; wherein the first adjustment value is positively correlated with the curvature characterization 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 taking the product of the first adjustment value and the second adjustment value as the current adjustment coefficient.

[0007] Optionally, the first adjustment value is determined using the curvature characterization value at the current moment, including: taking the product of the curvature characterization value at the current moment and the first adjustment weight as the first adjustment value; and / or, the second adjustment value is determined using the vehicle speed at the current moment, including: determining the ratio of the vehicle speed at the current moment to the second adjustment weight; taking the difference between the 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 a current steering angle threshold, including: taking 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 a curvature characterization value at the current moment, and the step of obtaining the curvature characterization value at the current moment includes: obtaining the planned curvatures corresponding to a number of planned trajectory points in the preview time domain; and determining the curvature characterization value at the current moment using at least each planned curvature.

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

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

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

[0013] Optionally, the comprehensive value of the planned curvatures is the maximum value among the planned curvatures.

[0014] Optionally, the step of obtaining the vehicle state tracking error term includes: using the system dynamics model to make predictions based on the vehicle state data at the current moment to obtain vehicle state prediction data at several future moments; obtaining the expected vehicle state data at each future moment; using the vehicle state prediction data at each future moment and the expected vehicle state data at each future moment to generate a vehicle state tracking error term; wherein the vehicle state data at the current moment includes the vehicle's center of mass sideslip angle at the current moment, the vehicle state prediction data at each future moment includes the predicted center of mass sideslip angle of the vehicle at the corresponding future moment, and the expected vehicle state data at each future moment includes the first expected center of mass sideslip angle of the vehicle at the corresponding future moment.

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

[0016] Optionally, the vehicle state data at the current moment also includes the yaw velocity of the vehicle at the current moment, the vehicle state prediction data at each future moment also includes the predicted yaw velocity of the vehicle at the corresponding future moment, and the vehicle state expected data at each future moment includes the first expected yaw velocity of the vehicle at the corresponding future moment; for each future moment, the step of obtaining the first expected yaw velocity 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 parameter to determine the second expected yaw velocity at the future moment; using the expected longitudinal velocity at the future moment and the maximum lateral acceleration provided by the road surface to determine the maximum yaw velocity constrained by the maximum lateral acceleration at the future moment; selecting the smaller value from the absolute value of the second expected yaw velocity at the future moment and the absolute value of the maximum yaw velocity at the future moment, and determining the first expected yaw velocity at the future moment based on the smaller value.

[0017] In order to solve the above technical problems, another technical solution adopted in the present application is: to provide a vehicle control device, which includes: a constraint determination module, which is used to determine the current steering angle constraint by using the current driving condition data; wherein the current driving condition data includes at least one of the curvature characterization value at the current moment and the vehicle speed at the current moment, and the curvature characterization value is used to reflect the curvature of the vehicle's future driving section; a construction module, which is used to construct a cost function of model predictive control by using the vehicle state tracking error term and the steering angle control term at the current moment; an optimization module, which 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 the preset conditions; and a control module, which is 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 technical problems, another technical solution adopted in the present application is: to provide an electronic device, comprising a memory and a processor coupled to each other, the memory storing program instructions; the processor is used to execute the program instructions stored in the memory to implement the above vehicle control method.

[0019] In order to solve the above technical problems, another technical solution adopted by the present application is: providing a computer-readable storage medium, which is used to store program instructions, and the program instructions can be executed by a processor to implement the above vehicle control method.

[0020] The above scheme determines the current steering angle constraint by using the current driving condition data; constructs the cost function of the model predictive control by using the vehicle state tracking error term and the 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 the 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 characterization value at the current moment and the vehicle speed at the current moment, and the curvature characterization value is used to reflect the curvature of the future driving section of the vehicle. Since the current driving condition data is constantly changing with the driving of the vehicle, and the current steering angle constraint is determined by using the current driving condition data, in this way, the current steering angle constraint can be adaptively adjusted according to the change of the current driving condition data to achieve stable and comfortable tracking control of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of an embodiment of a vehicle control method provided by the present application;

[0022] Figure 2 is a flow chart of another embodiment of the vehicle control method provided by the present application;

[0023] Figure 3 It is a flowchart of an embodiment of a method for obtaining a curvature characterization value at the current moment provided by the present application;

[0024] Figure 4 It is a flowchart of an embodiment of a method for determining a current adjustment coefficient provided by the present application;

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

[0026] Figure 6 It is a schematic diagram of a framework of an embodiment of a vehicle control device provided by the present application;

[0027] Figure 7 It is a schematic diagram of a framework of an embodiment of an electronic device provided by the present application;

[0028] Figure 8 It is a schematic diagram of a framework of an embodiment of a computer-readable storage medium provided by the present application. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and effect of the present application clearer and more specific, the present application is further described in detail below with reference to the accompanying drawings and examples.

[0030] In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures, interfaces, and technologies are provided to facilitate a thorough understanding of the present application.

[0031] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "many" in this article means two or more than two. In addition, the term "at least one" in this article means any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C, it 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 and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0032] See also Figure 1 , Figure 1 It is a flow chart of an embodiment of the vehicle control method provided by the present application. It should be noted that if there are substantially the same results, the method of the present application is not limited to Figure 1 The process sequence shown is limited. Figure 1 As shown, the method comprises the following steps:

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

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

[0035] The curvature representation value at the current moment is used to reflect the curvature of the road section where the vehicle will travel in the future at the current moment. If the curvature representation value at the current moment is larger, the curvature of the road section where the vehicle will travel in the future at the current moment is larger. If the curvature representation value at the current moment is smaller, the curvature of the road section where the vehicle will travel in the future at the current moment is smaller.

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

[0037] The current steering angle constraint changes adaptively with the change of current driving condition data.

[0038] In one embodiment, the current steering angle constraint is determined by only considering the influencing factor of the curvature characterization value at the current moment. When the curvature characterization value at the current moment is large, a larger steering angle is required to cope with a larger curvature change, and the range of the current steering angle constraint can be increased accordingly to maintain the stability and comfort of the vehicle. When the curvature characterization value at the current moment is small, the range of the current steering angle constraint can be reduced accordingly, so that the vehicle can travel at a smaller steering angle when traveling on a slightly curved curve, thereby maintaining the stability and comfort of the vehicle.

[0039] In another embodiment, only the vehicle speed at the current moment is considered as an influencing factor to determine the current steering angle constraint. The smaller the vehicle speed at the current moment, the smaller the impact on the current steering angle constraint; when the vehicle speed at the current moment is higher, the current steering angle constraint range can be reduced accordingly to ensure the safety and stability of vehicle driving.

[0040] In yet another embodiment, the curvature representation value at the current moment and the vehicle speed at the current moment may be considered simultaneously as two influencing factors to determine the current steering angle constraint.

[0041] S12: Using the vehicle state tracking error term and the steering angle control term at the current moment, a cost function of the model predictive control is constructed.

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

[0043] In step S12, the vehicle state tracking error term is used to measure the difference between the predicted state and the expected state, and the vehicle state tracking error term is obtained based on the vehicle state prediction data at several future moments and the vehicle state expected data at several future moments. 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 item meets the current steering angle constraint and the cost function meets the preset conditions.

[0045] In step S13, an optimal steering angle control sequence can be obtained when the steering angle control item meets the current steering angle constraint and the cost function meets the preset conditions, and the first value in the optimal steering angle control sequence is used 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] Further, after the optimal steering angle at the current moment is obtained, 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 by using the current driving condition data; the cost function of the model predictive control is constructed by using the vehicle state tracking error term and the 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 the preset conditions; and the front wheel steering angle of the vehicle at the current moment is controlled to be the optimal steering angle. The current driving condition data includes at least one of the curvature characterization value at the current moment and the vehicle speed at the current moment, and the curvature characterization value is used to reflect the curvature of the future driving section of the vehicle. Since the current driving condition data is constantly changing with the driving of the vehicle, and the current steering angle constraint is determined by using the current driving condition data, in this way, the current steering angle constraint can be adaptively adjusted according to the change of the current driving condition data to achieve stable and comfortable tracking control of the vehicle.

[0049] See also Figure 2 , Figure 2 It is a flow chart of another embodiment of the vehicle control method provided by the present application. It should be noted that if there is substantially the same result, the method of the present application is not limited to Figure 2 The process sequence shown is limited. Figure 2 As shown, the method comprises the following steps:

[0050] S21: Acquire current driving condition data.

[0051] The current driving condition data includes at least one of the curvature characterization value at the current moment and the vehicle speed at the current moment, and the curvature characterization value at the current moment is used to reflect the curvature of the future driving section of the vehicle at the current moment. Exemplarily, the current driving condition data may include both the curvature characterization value at the current moment and the vehicle speed at the current moment.

[0052] See also Figure 3 , Figure 3 FIG. 1 is a flow chart of an embodiment of a method for obtaining a curvature characterization value at the current moment provided by the present application. Figure 3 As shown, the method comprises the following steps:

[0053] S301: Determine the preview time domain.

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

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

[0056] Among them, v(k) represents the vehicle speed at the current time k, t pre It represents the preview time, and the symbol [·] represents the rounding operation.

[0057] S302: Obtain planning curvatures corresponding to a plurality of planning trajectory points in the preview time domain.

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

[0059] For example, the planning curvatures corresponding to several planning trajectory points in the preview time domain can be expressed as {κ0(k),κ1(k),...,κ N (k)}.

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

[0061] In one implementation, each planned curvature in the preview time domain is integrated to obtain a planned curvature integrated value; the planned curvature integrated value is used as the curvature representation value at the current moment.

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

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

[0064] For another example, in order to further improve the depiction effect of the curvature of the future driving section 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 planned curvature integrated value by integrating the planned curvatures in the preview time domain, the curvature characterization value at the previous moment is further combined to select the curvature characterization value at the current moment from the planned curvature integrated value and the curvature characterization value at the previous moment.

[0066] Specifically, in this implementation, the maximum value of the planned curvature comprehensive value and the curvature representation value at the previous moment is used as the curvature representation value at the current moment.

[0067] In this implementation, the curvature characterization value of the previous moment is further introduced to determine the curvature characterization value of the current moment. On the one hand, if the curvature characterization value of the previous moment is large, and the planned curvature comprehensive value in the preview time domain suddenly becomes small, then under the effect of the curvature characterization value of the previous moment, the curvature characterization value of the current moment will not immediately drop to a very low level, so that the curvature can be smoothly transitioned and the sharp change of curvature can be avoided. On the other hand, if the curvature characterization value of the previous moment is small, and the planned curvature comprehensive value in the preview time domain suddenly becomes large, the curvature change can also be sensed in advance, and then the current steering angle constraint can be adjusted in advance in the subsequent process 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] Among them, 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 a current adjustment coefficient using current driving condition data.

[0072] See also Figure 4 , Figure 4 FIG. 1 is a flow chart of an embodiment of a method for determining a current adjustment coefficient provided by the present application. Figure 4 As shown, the method comprises the following steps:

[0073] S401: Determine a 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 may be regarded as a gain factor of the curvature characterization value at the current moment, and is used to amplify or reduce the influence of the curvature characterization value at the current moment.

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

[0078] In another example, a first mapping relationship may be pre-constructed, the first mapping relationship including preset adjustment weights corresponding to different road curvatures, and the corresponding first adjustment weights are obtained by querying the first mapping relationship using the actual road curvature. In the first mapping relationship, the greater the road curvature, the greater the corresponding preset adjustment weight; the smaller the road curvature, the smaller the corresponding preset adjustment weight.

[0079] S402: Determine a 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 vehicle speed at the current moment to the second adjustment weight; and taking the difference between the preset value and the ratio as the second adjustment value. For example, the preset value is 1.

[0082] The second adjustment weight can be regarded as a speed threshold, which is used to adjust the impact of the current vehicle speed. When the current vehicle speed is greater and closer to the second adjustment weight, the second adjustment value approaches 0; when the current vehicle speed is smaller, the second adjustment value approaches the preset value. The second adjustment weight can be set according to actual needs.

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

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

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

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

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

[0088] Exemplarily, the reference steering angle is a maximum steering angle that can be achieved by a steering actuator of the vehicle.

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

[0090]

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

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

[0093] Exemplarily, 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: Using the vehicle state tracking error term and the steering angle control term at the current moment, a cost function of the model predictive control is constructed.

[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 to obtain vehicle state prediction data at several future moments.

[0097] In this embodiment, the system dynamics model is constructed by considering the sideslip angle of the center of mass, and the sideslip angle of the center of mass is expanded to the state space of the model predictive control. The process is briefly described below.

[0098] First, we construct a system dynamics model without considering the sideslip angle of the center of mass, and 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 angular velocity, lateral position and longitudinal position of the vehicle in the vehicle coordinate system, respectively. and They are The first-order derivatives of Y and X, δ f Indicates the vehicle's front wheel steering angle.

[0101] Furthermore, the system state quantity is selected as Control quantity U=[δ f ], after discretizing the continuous system, the state space equation can be obtained in the following form:

[0102] ξ(k+1)=Aξ(j)+BU(k)

[0103] in:

[0104]

[0105] Furthermore, considering the β (center of mass sideslip angle) constraint, the original system state quantity is expanded to and (The rate of change of the sideslip angle at the center of mass) satisfies the following expression:

[0106]

[0107] Then the extended state space equation can be expressed as:

[0108]

[0109] in:

[0110]

[0111] Furthermore, the above-mentioned extended state space equation can be used based on the vehicle state data at the current moment (i.e., the aforementioned Y and X) to perform iterative prediction to obtain vehicle state prediction data at several future moments.

[0112] Step 2: Obtain the expected vehicle status data at each future moment.

[0113] The expected data of the vehicle state at each future moment include the expected lateral speed, expected longitudinal speed, expected yaw angle, expected lateral position, expected longitudinal position, first expected center of mass sideslip angle and first expected yaw rate at the planned trajectory point at the corresponding future moment. Among them, the expected lateral speed, expected longitudinal speed, expected yaw angle, expected lateral position and expected longitudinal position at each future moment are given by the trajectory planning module. The first expected center of mass sideslip angle and the first expected yaw rate at each future moment are calculated based on the relevant expected data at each future moment given by the trajectory planning module.

[0114] See also Figure 5 , Figure 5 It is a schematic diagram of the two-degree-of-freedom vehicle model provided in this application. Figure 5 middle, represents the vehicle’s yaw angle, represents the lateral velocity in the vehicle coordinate system, represents the longitudinal velocity in the vehicle coordinate system, α f represents the front wheel slip angle, α r represents the rear wheel slip angle, β represents the center of mass slip angle, δ f represents the front wheel steering angle of the vehicle, a and b represent the front and rear wheelbase of the vehicle, C f and C r Indicates the cornering stiffness of the front and rear wheels, I z represents the moment of inertia of the vehicle, and m represents the mass of the vehicle.

[0115] In one embodiment, for each future moment, obtaining the first expected center of mass sideslip angle at the future moment may further include the following sub-steps:

[0116] Sub-step one, using the expected longitudinal speed at the future time, the expected front wheel steering angle at the future time and the first vehicle mechanical parameters, to determine the second expected center of mass sideslip angle at the future time.

[0117] Specifically, the second expected center of mass sideslip angle at the future moment can be calculated by the following formula:

[0118]

[0119] In this formula, β r0 represents the second expected sideslip angle of the center of mass at the future moment, and δ f They represent the expected longitudinal speed and the expected front wheel steering angle at the future moment respectively. The first vehicle mechanical parameters include the rear wheelbase b, the vehicle mass m, and the rear wheel cornering stiffness C. r , wheelbase L and stability coefficient k,

[0120]

[0121] Sub-step two, using the expected longitudinal speed at the future moment, the maximum lateral acceleration provided by the road surface and the second vehicle mechanical parameter, determine the maximum center of mass sideslip angle constrained by the maximum lateral acceleration at the future moment.

[0122] The desired slip angle is also affected by the maximum lateral acceleration a that the ground can provide. y limit:

[0123] |a y |≤μg

[0124] Among them, μ represents the road adhesion coefficient and g represents the acceleration due to gravity.

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

[0126] Sub-step three, determining a first expected center of mass sideslip angle at a future time based on the smaller value of the absolute value of the second expected center of mass sideslip angle at a future time and the absolute value of the maximum center of mass sideslip angle at a future time.

[0127] Specifically, the first expected center of mass sideslip angle at the future moment can be calculated by the following formula:

[0128]

[0129] Among them, β r is the first expected center of mass sideslip angle at the future moment, |β r0 | is the absolute value of the second expected center of mass sideslip angle at the future moment, is the absolute value of the maximum center of mass sideslip angle at the future moment, and the sign function sgn is used to take δ f to determine the direction of β r The positive and negative direction of .

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

[0131] Sub-step 1: Determine a second expected yaw rate at a future time by using an expected longitudinal speed at a future time, an expected front wheel steering angle at a future time, and a third vehicle mechanical parameter.

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

[0133]

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

[0135] Sub-step 2, using the expected longitudinal velocity at the future time and the maximum lateral acceleration provided by the road surface, determine the maximum yaw angular velocity constrained by the maximum lateral acceleration at the future time.

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

[0137] Sub-step three: selecting a smaller value 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 determining the first desired yaw rate at the future time based on the smaller value.

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

[0139]

[0140] in, is the first expected yaw rate at the future moment, is the absolute value of the second expected yaw rate at the future moment, is the absolute value of the maximum yaw rate at the future moment, and the sign function sgn is used to take δ f direction to determine The positive and negative direction of .

[0141] Step three, using the vehicle state prediction data at each future moment and the vehicle state expected data at each future moment, generate a 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] Among them, i|k represents the i-th step predicted state obtained based on the system dynamics model at the current time k. The cost function consists of the vehicle state tracking error term and the steering angle control term. The weight matrices corresponding to the vehicle state tracking error term and the steering angle control term are semi-positive matrices Q and R respectively. The equality constraint includes the designed extended center of mass sideslip angle constraint. The predicted initial state Get the current state value Control quantity constraint It meets the physical limitations of the system actuator and adjusts adaptively as the trajectory curvature changes. That is,

[0146] S26: Determine the optimal steering angle when the steering angle control item 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 local optimal solution under the control constraints to achieve stable following during the vehicle's automatic driving process, such as stable following during large curvature cornering and extreme obstacle avoidance during memory parking cruise. The adaptive current steering angle constraint is designed based on the current driving condition data, and the current steering angle constraint can be pre-adjusted in scenarios where the curvature changes greatly and rapidly, so as to achieve comfortable control under extreme conditions. By expanding the center of mass slip angle to the state space of the MPC algorithm and calculating the first expected center of mass slip angle and the first expected yaw rate in combination with the maximum lateral acceleration that the road surface can provide, the center of mass slip angle and yaw rate of the vehicle can be guaranteed not to exceed the stable range in some extreme steering scenarios, especially when driving on some roads with low adhesion coefficients, and the trajectory tracking accuracy will not be reduced.

[0149] See also Figure 6 , Figure 6 Schematic diagram of a framework of an embodiment of a vehicle control device provided by the present 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] Among them, the constraint determination module 61 is used to determine the current steering angle constraint using the current driving condition data; wherein the current driving condition data includes at least one of the curvature characterization value at the current moment and the vehicle speed at the current moment, and the curvature characterization value is used to reflect the curvature of the future driving section of the vehicle. The construction module 62 is used to construct the cost function of the model predictive control using the vehicle state tracking error term and the 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 the 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 a current adjustment coefficient using current driving condition data; adjust the reference steering angle using the current adjustment coefficient to obtain a current steering angle threshold; and generate a current steering angle constraint using the current steering angle threshold.

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

[0154] Optionally, the reference steering angle is a maximum steering angle of a steering actuator of the vehicle; and / or, the constraint determination module 61 is configured to take the product of the current adjustment coefficient and the reference steering angle as the current steering angle threshold.

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

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

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

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

[0159] Optionally, the comprehensive value of the planned curvatures is the maximum value among the planned curvatures.

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

[0161] Optionally, for each future moment, the construction module 62 is used to determine a second expected center of mass sideslip angle at the future moment using the expected longitudinal speed at the future moment, the expected front wheel steering angle at the future moment, and the first vehicle mechanical parameter; determine a maximum center of mass sideslip angle constrained by the maximum lateral acceleration at the future moment using the expected longitudinal speed at the future moment, the maximum lateral acceleration provided by the road surface, and the second vehicle mechanical parameter; and determine a first expected center of mass sideslip angle at the future moment based on the smaller value of the absolute value of the second expected center of mass sideslip angle at the future moment and the absolute value of the maximum center of mass sideslip angle at the future 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 expected data at each future moment includes the first expected yaw rate of the vehicle at the corresponding future moment.

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

[0164] It should be noted that the device of this embodiment can execute the steps in the above method. For detailed description of the relevant content, please refer to the above method part, which will not be repeated here.

[0165] See also Figure 7 , Figure 7 Schematic diagram of the electronic device 70 according to an embodiment of the present invention.

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

[0167] The memory 71 in the electronic device 70 is used to store 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 in the present application.

[0169] See also Figure 8 , Figure 8It is a schematic diagram of the framework of an embodiment of a computer-readable storage medium provided by the present application. The computer-readable storage medium 80 of the embodiment of the present application stores a program instruction 81, and the program instruction 81 implements the vehicle control method provided by the present application when it is executed. Among them, the program instruction 81 can form a program file and be stored in the above-mentioned computer-readable storage medium 80 in the form of a software product, so that a computer device (which can be a personal computer, a server, or a network device, etc.) executes all or part of the steps of each implementation method of the present application. The aforementioned computer-readable storage medium 80 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0170] The above scheme determines the current steering angle constraint by using the current driving condition data; constructs the cost function of the model predictive control by using the vehicle state tracking error term and the 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 the 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 characterization value at the current moment and the vehicle speed at the current moment, and the curvature characterization value is used to reflect the curvature of the future driving section of the vehicle. Since the current driving condition data is constantly changing with the driving of the vehicle, and the current steering angle constraint is determined by using the current driving condition data, in this way, the current steering angle constraint can be adaptively adjusted according to the change of the current driving condition data to achieve stable and comfortable tracking control of the vehicle.

[0171] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0172] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0173] In the several embodiments provided in the present application, it should be understood that the disclosed methods, devices and systems can be implemented in other ways. For example, the device implementation described above is only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

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

[0175] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0176] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. With this understanding, the technical solution of the present application, 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, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of each implementation method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0177] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A vehicle control method, characterized in that: The method comprises: Determine the 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 a current moment and a vehicle speed at a current moment, and the curvature representation value is used to reflect the curvature of a future driving section of the vehicle; The cost function of model predictive control is constructed by using the vehicle state tracking error term and steering angle control term at the current moment; Determining an optimal steering angle when the steering angle control item meets the current steering angle constraint and the cost function meets a preset condition; 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 method of using the current driving condition data to determine the current steering angle constraint includes: Determining a current adjustment coefficient using the current driving condition data; Using the current adjustment coefficient to adjust the reference steering angle to obtain a current steering angle threshold; The current steering angle constraint is generated using the current steering angle threshold.

3. The method according to claim 2, characterized in that The current driving condition data includes the curvature representation value at the current moment and the vehicle speed at the current moment; the current adjustment coefficient is determined by using the current driving condition data, including: Determine a first adjustment value by using the curvature characterization value at the current moment; wherein the first adjustment value is positively correlated with the curvature characterization value; Determine a second adjustment value using the current vehicle speed; wherein the second adjustment value is negatively correlated with the current vehicle speed; The product of the first adjustment value and the second adjustment value is used as the current adjustment coefficient.

4. The method according to claim 3, characterized in that The determining the first adjustment value by using the curvature characterization 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 by using the vehicle speed at the current moment includes: Determining a ratio of the vehicle speed at the current moment to the second adjustment weight; The difference between the preset value and the ratio is used as the second adjustment value.

5. The method according to claim 2, characterized in that: The reference steering angle is the maximum steering angle of the steering actuator of the vehicle; And / or, using the current adjustment coefficient to adjust the reference steering angle to obtain a 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.

6. The method according to claim 1, characterized in that The current driving condition data includes the curvature characterization value at the current moment, and the step of obtaining the curvature characterization value at the current moment includes: Obtain the planning curvatures corresponding to a number of planning trajectory points in the preview time domain; At least using each of the planned curvatures, determine the curvature characterization value at the current moment.

7. The method according to claim 6, characterized in that The step of at least using each of the planned curvatures to determine the curvature characterization value of each of the planned curvatures at the current moment includes: The planning curvatures are integrated to obtain a comprehensive planning curvature value; The planned curvature comprehensive value is used as the curvature representation value at the current moment.

8. The method according to claim 6, characterized in that The step of determining the curvature characterization value at the current moment by at least using each of the planned curvatures includes: The planning curvatures are integrated to obtain a comprehensive planning curvature value; The curvature characterization value at the current moment is selected from the planned curvature comprehensive value and the curvature characterization value at the previous moment.

9. The method according to claim 8, characterized in that The step of selecting the curvature characterization value at the current moment from the planned curvature comprehensive value and the curvature characterization value at the previous moment comprises: The maximum value of the planned curvature comprehensive value and the curvature characterization value at the previous moment is used as the curvature characterization value at the current moment.

10. The method according to claim 7 or 8, characterized in that: The comprehensive value of the planned curvatures is the maximum value among the planned curvatures.

11. The method according to claim 1, characterized in that: The step of obtaining the vehicle state tracking error term comprises: The system dynamics model is used to predict the vehicle state data at the current moment, and the vehicle state prediction data at several future moments are obtained; Obtaining expected vehicle status data at each of the future moments; Generate the vehicle state tracking error term using the vehicle state prediction data at each of the future moments and the vehicle state expected data at each of the future moments; Among them, the vehicle state data at the current moment includes the vehicle's center of mass sideslip angle at the current moment, the vehicle state prediction data at each future moment includes the predicted center of mass sideslip angle of the vehicle at the corresponding future moment, and the vehicle state expected data at each future moment includes the first expected center of mass sideslip angle of the vehicle at the corresponding future moment.

12. The method according to claim 11, characterized in that For each of the future moments, the step of obtaining the first expected center of mass sideslip angle at the future moment includes: Determine a second expected center of mass sideslip angle at the future moment by using the expected longitudinal speed at the future moment, the expected front wheel steering angle at the future moment, and the first vehicle mechanical parameter; Determine a maximum center of mass sideslip angle constrained by the maximum lateral acceleration at the future moment by using the expected longitudinal speed at the future moment, the maximum lateral acceleration provided by the road surface, and a second vehicle mechanical parameter; The first expected sideslip angle of the center of mass at the future time is determined based on the smaller value of the absolute value of the second expected sideslip angle of the center of mass at the future time and the absolute value of the maximum sideslip angle of the center of mass at the future time.

13. The method according to claim 11, characterized in that The vehicle state data at the current moment also includes the yaw rate of the vehicle 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 expected data at each future moment 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 angular velocity at the future moment includes: Determine a second expected yaw rate at the future time by using the expected longitudinal speed at the future time, the expected front wheel steering angle at the future time, and a third vehicle mechanical parameter; Determining a maximum yaw rate constrained by the maximum lateral acceleration at the future time by using the expected longitudinal speed at the future time and the maximum lateral acceleration provided by the road surface; A smaller value is selected from an absolute value of the second desired yaw rate at the future time and an 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.

14. A vehicle control device, characterized in that: The device comprises: A constraint determination module, used to determine 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 a current moment and a vehicle speed at a current moment, and the curvature representation value is used to reflect the curvature of a future driving section of the vehicle; A construction module, used to construct a cost function of model predictive control using a vehicle state tracking error term and a steering angle control term at a current moment; an optimization module, used for determining an optimal steering angle when the steering angle control item meets the current steering angle constraint and the cost function meets a preset condition; The control module is used to control the front wheel steering angle of the vehicle at a current moment to be the optimal steering angle.

15. An electronic device, characterized in that: comprising a memory and a processor coupled to each other, The memory stores program instructions; The processor is used to execute the program instructions stored in the memory to implement the method according to any one of claims 1 to 13.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program instructions, and the program instructions can be executed by a processor to implement the method according to any one of claims 1 to 13.

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