Vehicle lateral control method, device, storage medium and autonomous driving vehicle

Abstract

The present disclosure provides a lateral control method, device, storage medium and autonomous driving vehicle for a vehicle, which relates to the field of computer technology, and more specifically to the field of artificial intelligence technology such as intelligent transportation and autonomous driving. The specific implementation scheme is as follows: when performing lateral control of the vehicle for autonomous driving, the vehicle's operating state information at the current moment and the expected longitudinal speed at each predicted time point in the predicted time domain after the current moment are combined to determine the predicted lateral vehicle state parameters of the vehicle at each predicted time point, and the lateral control amount at each predicted time point in the predicted time domain is determined based on the predicted lateral vehicle state parameters and the expected lateral vehicle state parameters of the vehicle at each predicted time point, and the vehicle is laterally controlled based on the lateral control amount at the first predicted time point in the predicted time domain. In this way, the accuracy and stability of the vehicle's lateral control are improved, thereby improving the safety of the vehicle's autonomous driving.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, specifically to the field of artificial intelligence technology such as intelligent transportation and autonomous driving, and more particularly to a vehicle lateral control method, device, storage medium, and autonomous driving vehicle. Background Art

[0002] With the development of science and technology, self-driving vehicles have become an important development direction of future automobiles. Self-driving vehicles can not only help improve people's travel convenience and travel experience, but also greatly improve people's travel efficiency.

[0003] During lateral control of an autonomous vehicle, related technologies typically rely on a lateral control variable determined by the autonomous driving system. The accuracy of this lateral control variable is crucial to the stability and accuracy of the vehicle's lateral control. Summary of the Invention

[0004] The present disclosure provides a lateral control method, device, storage medium and autonomous driving vehicle for a vehicle.

[0005] According to one aspect of the present disclosure, a method for lateral control of a vehicle is provided, the method comprising: obtaining an expected longitudinal velocity sequence of the vehicle in a predicted time domain after a current moment, wherein the expected longitudinal velocity sequence comprises: expected longitudinal velocities at each predicted time point; determining predicted lateral vehicle state parameters of the vehicle at each predicted time point based on the operating state information of the vehicle at the current moment and the expected longitudinal velocities at each predicted time point; determining expected lateral vehicle state parameters at each predicted time point in an expected trajectory of the vehicle; determining a lateral control amount of the vehicle at each predicted time point based on the predicted lateral vehicle state parameters and the expected lateral vehicle state parameters; and performing automatic driving lateral control of the vehicle based on the lateral control amount at the first predicted time point in the predicted time domain.

[0006] According to another aspect of the present disclosure, a lateral control device for a vehicle is provided, the device comprising: an acquisition module for acquiring an expected longitudinal speed sequence of the vehicle in a predicted time domain after a current moment, wherein the expected longitudinal speed sequence comprises: an expected longitudinal speed at each predicted time point; a first determination module for determining a predicted lateral vehicle state parameter of the vehicle at each predicted time point based on the operating state information of the vehicle at the current moment and the expected longitudinal speed at each predicted time point; a second determination module for determining the expected lateral vehicle state parameter at each predicted time point in the expected trajectory of the vehicle; a third determination module for determining a lateral control amount of the vehicle at each predicted time point based on the predicted lateral vehicle state parameter and the expected lateral vehicle state parameter; and a control module for performing automatic driving lateral control of the vehicle based on the lateral control amount at the first predicted time point in the predicted time domain.

[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the lateral control method of a vehicle of the present disclosure.

[0008] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the vehicle lateral control method disclosed in an embodiment of the present disclosure.

[0009] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the vehicle lateral control method of the present disclosure is implemented.

[0010] One embodiment of the above application has the following advantages or beneficial effects:

[0011] When performing lateral control of a vehicle for autonomous driving, the vehicle's operating state information at the current moment is combined with the expected longitudinal speed at each predicted time point within a prediction time domain after the current moment to determine the predicted lateral vehicle state parameters for each predicted time point. Furthermore, the predicted lateral vehicle state parameters at each predicted time point and the expected lateral vehicle state parameters are combined to determine the lateral control amount for each predicted time point within the prediction time domain. Lateral control of the vehicle is then performed based on the lateral control amount at the first predicted time point within the prediction time domain. Thus, by combining the expected longitudinal speed at each predicted time point within the prediction time domain, the lateral control amount for lateral control of the vehicle is accurately determined, improving the accuracy and stability of the vehicle's lateral control, and thereby enhancing the safety of autonomous driving.

[0012] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0014] Figure 1 is a schematic diagram according to a first embodiment of the present disclosure;

[0015] Figure 2 is a schematic diagram according to a second embodiment of the present disclosure;

[0016] Figure 3 is a schematic diagram according to a third embodiment of the present disclosure;

[0017] Figure 4 is a schematic diagram according to a fourth embodiment of the present disclosure;

[0018] Figure 5 is a schematic diagram according to a fifth embodiment of the present disclosure;

[0019] Figure 6 is a schematic diagram according to a sixth embodiment of the present disclosure;

[0020] Figure 7 is a schematic diagram according to a seventh embodiment of the present disclosure;

[0021] Figure 8 It is a block diagram of an electronic device used to implement the vehicle lateral control method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0023] The following describes the vehicle lateral control method, device, storage medium and autonomous driving vehicle according to embodiments of the present disclosure with reference to the accompanying drawings.

[0024] Figure 1 is a schematic diagram according to a first embodiment of the present disclosure, which provides a lateral control method for a vehicle.

[0025] like Figure 1 As shown, the lateral control method of the vehicle may include:

[0026] Step 101 : obtaining an expected longitudinal speed sequence of the vehicle in a predicted time domain after the current moment, wherein the expected longitudinal speed sequence includes: expected longitudinal speeds at each predicted time point.

[0027] Among them, the executor of the vehicle lateral control method implemented in this embodiment is the vehicle's lateral control device, which can be implemented by software and / or hardware. The vehicle's lateral control device can be an electronic device, or can be configured in an electronic device to achieve automatic driving control of the vehicle.

[0028] In some exemplary implementations, the electronic device may be a terminal device or a server, etc., which is not specifically limited in this embodiment.

[0029] In some exemplary embodiments, the terminal device may be a device that can be installed in any vehicle, such as an onboard controller or a vehicle-mounted computer. In some examples, the vehicle's lateral control device may be an onboard computer. It should be noted that the onboard computer in this example has an autonomous driving function, capable of path planning and autonomous driving control of the vehicle. In other examples, the vehicle's lateral control device may be a server that can communicate with the vehicle and perform autonomous driving control of the vehicle.

[0030] The prediction time domain refers to a period of time after the current moment, which may include N prediction time points, and there is a preset time interval between two adjacent prediction time points.

[0031] The preset time interval is pre-set and can be set based on actual needs. For example, the prediction time domain may include 25 prediction time points, and the preset time interval between two adjacent prediction time points may be 1 second. For another example, the prediction time domain may include 10 prediction time points, and the preset time interval between two adjacent prediction time points may be 2 seconds.

[0032] In some exemplary embodiments, in order to accurately control the vehicle laterally, the current speed of the vehicle at the current moment may be combined to determine the length of the prediction time domain and the value of the time interval between the prediction time points.

[0033] The expected longitudinal velocity sequence refers to a sequence obtained by sorting the expected longitudinal velocities at each prediction time point in the prediction time domain in chronological order.

[0034] The expected longitudinal speed at each prediction time point within the prediction time domain refers to the acceleration that the vehicle is expected to achieve at each prediction time point within the prediction time domain when planning the vehicle based on the actual situation of the vehicle at the current moment.

[0035] Step 102 : Determine predicted lateral vehicle state parameters of the vehicle at each predicted time point based on the vehicle's current operating state information and the expected longitudinal speed at each predicted time point.

[0036] The operating status information may include but is not limited to lateral vehicle status parameters and lateral control quantities, and may also include other information, such as longitudinal vehicle status parameters and longitudinal control quantities, which are not specifically limited in this embodiment.

[0037] The lateral vehicle state parameters may include: lateral displacement, lateral velocity, heading angle, yaw rate, and actual front wheel turning angle.

[0038] Among them, the predicted lateral vehicle state parameters refer to the vehicle state parameters obtained by predicting the lateral vehicle state parameters of the vehicle at each predicted time point in combination with the vehicle's current operating state information and the expected longitudinal speed at each predicted time point.

[0039] Step 103 : Determine the expected lateral vehicle state parameters at each predicted time point in the expected trajectory of the vehicle.

[0040] The expected trajectory refers to the trajectory that the vehicle is expected to achieve in the predicted time domain when planning the vehicle.

[0041] The expected lateral vehicle state parameter refers to the lateral vehicle state parameter that the vehicle is expected to achieve when planning the vehicle.

[0042] In some exemplary embodiments, the expected trajectory may include expected vehicle state parameters at multiple time points, wherein the expected vehicle state parameters may include expected lateral vehicle state parameters. In some examples, the expected vehicle state parameters may also include expected longitudinal vehicle state parameters.

[0043] The desired lateral vehicle state parameters may include, but are not limited to: desired lateral displacement, desired lateral velocity, desired heading angle, desired yaw rate, and desired front wheel turning angle.

[0044] Step 104 : determining the lateral control amount of the vehicle at each predicted time point based on the predicted lateral vehicle state parameter and the expected lateral vehicle state parameter.

[0045] Among them, the lateral control amount may include the issued front wheel angle.

[0046] In some exemplary embodiments of the present disclosure, a model predictive controller may be used to determine the lateral control amount of the vehicle at each predicted time point based on the predicted lateral vehicle state parameters and the expected vehicle state parameters.

[0047] Among them, the Model Predictive Control (MPC) is used to predict the lateral control amount at each prediction time point in the prediction time domain based on the predicted lateral vehicle state parameters and the expected vehicle state parameters, so as to perform automatic driving control processing on the vehicle based on the lateral control amount at the first prediction time point in the prediction time domain.

[0048] Specifically, the exemplary process of the model predictive controller determining the lateral control amount at each predicted time point is as follows: the model predictive controller determines the objective function used when performing model predictive control on the lateral model based on the predicted lateral vehicle state parameters and the expected vehicle state parameters, and solves the optimal solution of the objective function through rolling optimization to obtain the lateral control amount at each predicted time point.

[0049] Step 105 , performing automatic driving lateral control on the vehicle based on the lateral control amount at the first prediction time point within the prediction time domain.

[0050] In an exemplary embodiment of the present disclosure, the predicted time points within the prediction time domain can be sorted in chronological order to obtain a sorted result, wherein the first predicted time point is the time point ranked first in the sorted result. For example, the sorted result is t1, t2, t3, t4, t5, where t1 is the first predicted time point.

[0051] In some exemplary embodiments of the present disclosure, since the time interval between the current moment and the next moment is the same as the time interval between adjacent predicted time points, after determining the lateral control amount of the vehicle at each predicted time point, the lateral control amount at the first predicted time point in the predicted time domain can be used as the lateral control amount of the vehicle at the next moment, and based on this lateral control amount, the vehicle can be laterally controlled at the next moment.

[0052] The lateral control method for a vehicle of an embodiment of the present disclosure, when performing lateral control of the vehicle for autonomous driving, combines the vehicle's operating state information at the current moment and the expected longitudinal speed at each predicted time point in the predicted time domain after the current moment to determine the predicted lateral vehicle state parameters of the vehicle at each predicted time point, and combines the predicted lateral vehicle state parameters of the vehicle at each predicted time point with the expected lateral vehicle state parameters to determine the lateral control amount at each predicted time point in the predicted time domain, and performs lateral control of the vehicle based on the lateral control amount at the first predicted time point in the predicted time domain. Thus, the lateral control amount for lateral control of the vehicle is accurately determined in combination with the expected longitudinal speed at each predicted time point in the predicted time domain, thereby improving the accuracy and stability of the vehicle's lateral control, and thereby improving the safety of the vehicle's autonomous driving.

[0053] In one embodiment of the present disclosure, in order to accurately determine the predicted lateral vehicle state parameters of the vehicle at each predicted time point, a possible implementation of the above step 102 is as follows: Figure 2 As shown, this may include:

[0054] Step 201, for the i-th prediction time point, the maximum value of the current longitudinal velocity of the vehicle at the current moment and the minimum longitudinal velocity that can be processed by the lateral model is used as the first longitudinal velocity, and the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point are determined based on the first longitudinal velocity, the lateral model, and the operating state information, where the initial value of i is 1.

[0055] In one embodiment of the present disclosure, in order to accurately determine the predicted lateral vehicle state parameters at the i-th prediction time point, a possible implementation method for determining the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point based on the first longitudinal velocity, the lateral model and the operating status information is as follows: determining the first control parameter information used by the lateral model at the i-th prediction time point based on the first longitudinal velocity; adjusting the control parameter information of the lateral model to the first control parameter information; and inputting the lateral vehicle state parameters and the lateral control amount in the operating status information into the lateral model to obtain the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point.

[0056] The lateral model, also known as a lateral state equation model, is used to predict the vehicle's lateral vehicle state parameters. Specifically, after a first lateral vehicle state parameter and a corresponding lateral control variable are input into the lateral model, the lateral model can predict a second lateral vehicle state parameter that the vehicle will enter after applying the lateral control variable under the first lateral vehicle state parameter.

[0057] The first control parameter information may include: first weight information for applying to the vehicle lateral state parameter in the operating state information and second weight information for applying to the lateral control amount in the operating state information.

[0058] Wherein, when i is equal to 1, after the control parameter information of the lateral model is adjusted to the first control parameter information, the exemplary form of the state equation of the lateral model is as follows:

[0059] x1=A d x cur +B d u cur

[0060] Among them, x1 in the formula represents the predicted lateral vehicle state parameter at the first prediction time point, and A in the formula d Indicates the first weight information, B in the formula d Represents the second weight information, x cur Represents the lateral vehicle state parameter of the vehicle at the current moment, u cur Indicates the amount of lateral control applied to the vehicle at the current moment.

[0061] In the case of bilinear discretization, the above A d and B d An example representation of is:

[0062] A d =(I-0.5At) -1 (I-0.5At)

[0063] Among them, B d =(I-0.5At) -1 Bt

[0064] Wherein, B represents the preset weight information, t represents the time interval between adjacent prediction time points, and I represents the identity matrix. It should be noted that the time interval between the current moment and the first prediction time point in the prediction time domain is also t.

[0065] Wherein, A represents a weight matrix determined based on the first longitudinal velocity.

[0066] in, Among them, c f is the front axle cornering stiffness, c r is the rear axle lateral stiffness; l f is the distance from the front axle to the center of mass of the vehicle, l r is the distance from the rear axle to the center of mass of the vehicle; I Z is the moment of inertia at the center of mass of the vehicle, m is the mass of the vehicle, v cur is the current longitudinal speed of the vehicle at the current moment, where τ is a preset delay time coefficient.

[0067] Step 202: add 1 to i.

[0068] Step 203, when i is less than or equal to N, the maximum value of the expected longitudinal velocity at the (i-1)th prediction time point and the minimum longitudinal velocity that can be processed by the lateral model is used as the second longitudinal velocity, and the predicted lateral vehicle state parameters of the vehicle at the (i-1)th prediction time point are determined based on the second longitudinal velocity, the lateral model, the initial lateral control amount of the vehicle at the (i-1)th prediction time point, and the predicted lateral vehicle state parameters, and the process then jumps to step 202.

[0069] Wherein, N is the total number of prediction time points in the prediction time domain.

[0070] In some embodiments of the present disclosure, when i is greater than N, the process ends directly.

[0071] In some embodiments of the present disclosure, in order to accurately determine the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point, a possible implementation method for determining the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point based on the second longitudinal speed, the lateral model, the initial lateral control amount and the predicted lateral vehicle state parameters of the vehicle at the i-1 prediction time point is: determining the second control parameter information used by the lateral model at the i-th prediction time point based on the second longitudinal speed; adjusting the control parameter information of the lateral model to the second control parameter information; and inputting the initial lateral control amount and the predicted lateral vehicle state parameters of the vehicle at the i-1 prediction time point into the lateral model to obtain the predicted lateral vehicle state parameters of the vehicle at the i-1 prediction time point.

[0072] The second control parameter information may include: first weight information applied to the predicted lateral vehicle state parameter at the i-1th prediction time point, and second weight information applied to the initial control amount at the i-1th prediction time point.

[0073] Wherein, when i is greater than or equal to 2 and less than or equal to N, the exemplary form of the state equation in the lateral model is:

[0074] x i+1 =A di x i +B di u i

[0075] Among them, x in the formula i+1 represents the predicted lateral vehicle state parameter at the i+1th prediction time point, x i Represents the predicted lateral vehicle state parameter at the i-th prediction time point, and A in the formula di Indicates the first control parameter information used by the horizontal model at the i-th prediction time. B in the formula di Indicates the second control parameter information used by the horizontal model at the i-th prediction time, u i represents the initial longitudinal control amount applied to the vehicle at the i-th prediction time point. Where N represents the total number of prediction time points in the prediction time domain.

[0076] In the case of bilinear discretization, the above A di and B di An example representation of is:

[0077] A di =(I-0.5A i t) -1 (I-0.5A i t)

[0078] B di =(I-0.5A i t) -1 Bt

[0079] Wherein, B represents the preset weight information, and t represents the time interval between adjacent prediction time points. It should be noted that the time interval between the current moment and the first prediction time point in the prediction time domain is also t.

[0080] Among them, A i represents weight information determined based on the second longitudinal speed corresponding to the i-th prediction time point.

[0081] in,

[0082] Among them, v i represents the second longitudinal velocity corresponding to the i-th prediction time point, v i =max(v min ,v ri )

[0083] Among them, v min represents the minimum longitudinal velocity that the lateral model can handle, vri Represents the expected longitudinal velocity corresponding to the i-th prediction time point.

[0084] The specific method of calculating the second longitudinal velocity corresponding to the i-th prediction time point is as follows: the expected longitudinal velocity corresponding to the i-th prediction time point and the minimum longitudinal velocity that can be processed by the lateral model are used as the second longitudinal velocity corresponding to the i-th prediction time point.

[0085] In some exemplary embodiments, in order to accurately determine the expected lateral vehicle state parameters of the vehicle at each predicted time point, a possible implementation of step 103 is as follows: Figure 3 As shown, this may include:

[0086] Step 301 : Determine the predicted longitudinal position of the vehicle at each predicted time point.

[0087] In one embodiment of the present disclosure, a predicted longitudinal speed of the vehicle at each predicted time point may be obtained, and a predicted longitudinal position of the vehicle at each predicted time point may be determined based on the predicted longitudinal speed at each predicted time point and the current position of the vehicle at the current moment.

[0088] Step 302: For each predicted time point, determine a target trajectory point at the predicted time point from multiple trajectory points in the expected trajectory based on the predicted longitudinal position at the predicted time point, wherein the distance between the longitudinal position of the target trajectory point and the predicted longitudinal position is the smallest, and the time point corresponding to the target trajectory point is later than the predicted time point.

[0089] Step 303: Using the expected lateral vehicle state parameter at the target trajectory point as the expected lateral vehicle state parameter at the prediction time point.

[0090] In one embodiment of the present disclosure, in order to accurately determine the predicted longitudinal speed of the vehicle at each predicted time point, a possible implementation method for determining the predicted longitudinal position of the vehicle at each predicted time point is as follows: Figure 4 As shown, this may include:

[0091] Step 401: Obtain the current longitudinal position and current longitudinal speed of the vehicle at the current moment.

[0092] Step 402 : obtaining an expected longitudinal speed sequence of the vehicle in the prediction time domain, wherein the expected longitudinal speed sequence includes the expected longitudinal speed at each prediction time point.

[0093] Step 403 : Determine the predicted longitudinal position of the vehicle at each predicted time point based on the current longitudinal speed, the current longitudinal position, and the expected longitudinal speed sequence.

[0094] In one embodiment of the present disclosure, a possible implementation method for determining the predicted longitudinal position of the vehicle at each prediction time point based on the current longitudinal speed, the current longitudinal position, and the expected longitudinal speed sequence is as follows: for the i-th prediction time point, taking the maximum value of the expected longitudinal speed at the i-th prediction time point and the minimum longitudinal speed that can be processed by the lateral model as the first longitudinal speed, and determining the predicted longitudinal position at the i-th prediction time point based on the first longitudinal speed and the current longitudinal position, where the initial value of i is 1; adding 1 to i; when i is less than or equal to N, taking the maximum value of the expected longitudinal speed at the i-th prediction time point and the minimum longitudinal speed that can be processed by the lateral model as the second longitudinal speed, determining the predicted longitudinal position at the i-th prediction time point based on the second longitudinal speed and the predicted longitudinal position at the i-1-th prediction time point, and then proceeding to the step of adding 1 to i, where N is the total number of prediction time points in the prediction time domain.

[0095] In one embodiment of the present disclosure, when i is equal to 1, an exemplary formula for determining the predicted longitudinal position at the first predicted time point is as follows:

[0096] S1=v1*t+S cur

[0097] v1=max(v cur ,v min )

[0098] Among them, S1 in the formula represents the predicted longitudinal position at the first prediction time point in the prediction time domain; v1 in the formula represents the first longitudinal velocity corresponding to the first prediction time point, and v in the formula is cur Indicates the current longitudinal speed of the vehicle at the current moment; v min Indicates the minimum longitudinal velocity that the lateral model can handle. T represents the time interval between the current moment and the first predicted time point.

[0099] In one embodiment of the present disclosure, when i is greater than or equal to 2 and less than or equal to N, the formula for determining the predicted longitudinal position at the i-th prediction time point is:

[0100]

[0101] v i =max(v ri ,v min )

[0102] Among them, S in the formula i Indicates the predicted longitudinal position at the i-th prediction time point in the prediction time domain; v in the formula iIndicates the second longitudinal velocity corresponding to the i-th prediction time point, v in the formula ri represents the expected longitudinal velocity corresponding to the i-th prediction time point; v min represents the minimum longitudinal velocity that the lateral model can handle. t represents the time interval between two adjacent prediction time points.

[0103] In an embodiment of the present disclosure, the predicted longitudinal position at the first prediction time point in the prediction time domain is accurately determined by combining the current longitudinal speed, current longitudinal position, and the minimum longitudinal speed that can be processed by the lateral model at the current moment of the vehicle. For the ith prediction time point, the predicted longitudinal position at the ith prediction time point is determined by combining the predicted longitudinal position, the expected longitudinal speed, and the minimum longitudinal speed that can be processed by the lateral model at the i-1th prediction time point, where the value of i is any integer from 2 to N, and N represents the total number of prediction time points in the prediction time domain.

[0104] In one embodiment of the present disclosure, in order to accurately determine the lateral control amount of the vehicle at each predicted time point, a possible implementation of the above step 104 is as follows: Figure 5 As shown, this may include:

[0105] Step 501 : constructing an objective function of a model predictive controller for model control prediction of a longitudinal model based on the predicted lateral vehicle state parameters at each prediction time point and the expected lateral vehicle state parameters at each prediction time point in the expected trajectory.

[0106] The formula of the objective function J is as follows:

[0107]

[0108] Among them, Y in the formula i represents the predicted lateral vehicle state parameter at the i-th prediction time point; Y in the formula ri represents the expected lateral vehicle state parameter at the i-th prediction time point; Q represents the first penalty weight corresponding to the state quantity error at the i-th prediction time point; U i represents the initial lateral control amount at the i-th prediction time point; ΔU i Represents the control amount increment at the i-th prediction time point; R1 represents the second penalty weight; R2 represents the third penalty weight; T represents transpose; N represents the total number of prediction time points in the prediction domain.

[0109] Step 502 : Solve the objective function to obtain the lateral control amount of the vehicle at each predicted actual point.

[0110] Among them, the implementation method of solving the objective function to obtain the lateral control amount of the vehicle at each predicted actual point can be referred to the relevant technology. An exemplary implementation method can be: the optimal solution of the objective function can be solved by rolling optimization to obtain the longitudinal control amount at each predicted time point.

[0111] In order to implement the above embodiment, the embodiment of the present disclosure further provides a lateral control device for a vehicle.

[0112] Figure 6 is a schematic diagram of a sixth embodiment of the present disclosure, which provides a lateral control device for a vehicle.

[0113] like Figure 6 As shown, the vehicle lateral control device 60 may include an acquisition module 61, a first determination module 62, a second determination module 63, a third determination module 64 and a control module 65, wherein:

[0114] The acquisition module 61 is configured to acquire an expected longitudinal velocity sequence of the vehicle in a predicted time domain after a current moment, wherein the expected longitudinal velocity sequence includes expected longitudinal velocities at each predicted time point.

[0115] The first determination module 62 is configured to determine predicted lateral vehicle state parameters of the vehicle at each predicted time point based on the vehicle's current operating state information and the expected longitudinal speed at each predicted time point.

[0116] The second determination module 63 is configured to determine expected lateral vehicle state parameters at each predicted time point in the expected trajectory of the vehicle.

[0117] The third determination module 64 is configured to determine the lateral control amount of the vehicle at each predicted time point according to the predicted lateral vehicle state parameter and the expected lateral vehicle state parameter.

[0118] The control module 65 is used to perform automatic driving lateral control of the vehicle according to the lateral control amount at the first prediction time point in the prediction time domain.

[0119] The lateral control device of the vehicle of the embodiment of the present disclosure, when performing lateral control of the vehicle for automatic driving, combines the vehicle's operating state information at the current moment and the expected longitudinal speed at each predicted time point in the predicted time domain after the current moment to determine the predicted lateral vehicle state parameters of the vehicle at each predicted time point, and combines the predicted lateral vehicle state parameters of the vehicle at each predicted time point with the expected lateral vehicle state parameters to determine the lateral control amount at each predicted time point in the predicted time domain, and performs lateral control of the vehicle based on the lateral control amount at the first predicted time point in the predicted time domain. Thus, the lateral control amount for lateral control of the vehicle is accurately determined in combination with the expected longitudinal speed at each predicted time point in the predicted time domain, thereby improving the accuracy and stability of the vehicle's lateral control, and thereby improving the safety of the vehicle's automatic driving.

[0120] In one embodiment of the present disclosure, Figure 7 As shown, the lateral control device 70 of the vehicle may include: an acquisition module 71, a first determination module 72, a second determination module 73, a third determination module 74 and a control module 75, wherein the first determination module 72 may include: a first determination unit 721, a processing unit 722 and a second determination unit 723; the second determination module 73 may include: a third determination unit 731, a fourth determination unit 732 and a fifth determination unit 733; the third determination unit 731 may include: a first acquisition subunit 7311, a second acquisition unit 7312, and a determination subunit 7313.

[0121] It should be noted that the detailed description of the acquisition module 71 and the control module 75 can be found in the above Figure 6 The description of the acquisition module 61 and the control module 65 in the embodiment will not be described again here.

[0122] In one embodiment of the present disclosure, the first determining module 72 includes:

[0123] a first determining unit 721 for determining, for the i-th prediction time point, a predicted lateral vehicle state parameter of the vehicle at the i-th prediction time point based on the first longitudinal speed, the lateral model, and the operating state information, using the maximum value of the current longitudinal speed of the vehicle at the current moment and the minimum longitudinal speed that can be processed by the lateral model as a first longitudinal speed, where the initial value of i is 1;

[0124] A processing unit 722 is configured to add 1 to i;

[0125] The second determination unit 723 is configured to, when i is less than or equal to N, use the maximum value of the expected longitudinal velocity at the (i-1)th prediction time point and the minimum longitudinal velocity that can be processed by the lateral model as the second longitudinal velocity, determine the predicted lateral vehicle state parameter of the vehicle at the (i-1)th prediction time point based on the second longitudinal velocity, the lateral model, the initial lateral control amount of the vehicle at the (i-1)th prediction time point, and the predicted lateral vehicle state parameter, and proceed to the step of adding 1 to i, where N is the total number of prediction time points in the prediction time domain.

[0126] In one embodiment of the present disclosure, the above-mentioned first determination unit 721 is specifically used to: determine the first control parameter information used by the lateral model at the i-th prediction time point based on the first longitudinal speed; adjust the control parameter information of the lateral model to the first control parameter information; input the lateral vehicle state parameters and lateral control amount in the operating state information into the lateral model to obtain the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point.

[0127] In one embodiment of the present disclosure, the above-mentioned second determination unit 722 is specifically used to: determine the second control parameter information used by the lateral model at the i-th prediction time point based on the second longitudinal speed; adjust the control parameter information of the lateral model to the second control parameter information; input the initial lateral control amount and predicted lateral vehicle state parameters of the vehicle at the i-1-th prediction time point into the lateral model to obtain the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point.

[0128] In one embodiment of the present disclosure, the second determining module 73 includes:

[0129] The third determining unit 731 is configured to determine the predicted longitudinal position of the vehicle at each predicted time point.

[0130] The fourth determination unit 732 is used to determine, for each predicted time point, a target trajectory point at the predicted time point from multiple trajectory points in the expected trajectory according to the predicted longitudinal position at the predicted time point, wherein the distance between the longitudinal position of the target trajectory point and the predicted longitudinal position is the smallest, and the time point corresponding to the target trajectory point is later than the predicted time point.

[0131] The fifth determining unit 733 is configured to use the expected lateral vehicle state parameter at the target trajectory point as the expected lateral vehicle state parameter at the prediction time point.

[0132] In one embodiment of the present disclosure, the third determining unit 731 may include:

[0133] The first acquisition subunit 7311 is used to acquire the current longitudinal position and current longitudinal speed of the vehicle at the current moment;

[0134] A second acquiring unit 7312 is configured to acquire an expected longitudinal velocity sequence of the vehicle in the prediction time domain, wherein the expected longitudinal velocity sequence includes the expected longitudinal velocity at each prediction time point;

[0135] The determination subunit 7313 is configured to determine the predicted longitudinal position of the vehicle at each predicted time point according to the current longitudinal speed, the current longitudinal position and the expected longitudinal speed sequence.

[0136] In one embodiment of the present disclosure, the above-mentioned determination subunit 7313 is specifically used to: for the i-th prediction time point, use the maximum value of the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that can be processed by the lateral model as the first longitudinal velocity, and determine the predicted longitudinal position at the i-th prediction time point based on the first longitudinal velocity and the current longitudinal position, where the initial value of i is 1; add 1 to i; when i is less than or equal to N, use the maximum value of the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that can be processed by the lateral model as the second longitudinal velocity, determine the predicted longitudinal position at the i-th prediction time point based on the second longitudinal velocity and the predicted longitudinal position at the i-1-th prediction time point, and go to the step of adding 1 to i, where N is the total number of prediction time points in the prediction time domain.

[0137] In one embodiment of the present disclosure, the third determination module 74 is specifically used to: construct an objective function for the model predictive controller to perform model control prediction on the longitudinal model based on the predicted lateral vehicle state parameters at each predicted time point and the expected lateral vehicle state parameters at each predicted time point in the expected trajectory; and solve the objective function to obtain the lateral control amount of the vehicle at each actual predicted point.

[0138] It should be noted that the above explanation of the vehicle lateral control method is also applicable to the vehicle lateral control device in this embodiment, and this embodiment will not elaborate on this.

[0139] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0140] Figure 8A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0141] like Figure 8 As shown, the electronic device 800 may include a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the device 800 may also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0142] Various components in device 800 are connected to I / O interface 805, including an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0143] The computing unit 801 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the vehicle lateral control method. For example, in some embodiments, the vehicle lateral control method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the vehicle lateral control method described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the vehicle lateral control method by any other suitable means (e.g., via firmware).

[0144] Various embodiments of the devices and techniques described above herein can be implemented in digital electronic circuit devices, integrated circuit devices, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), devices on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable device that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage device, at least one input device, and at least one output device, and transmit data and instructions to the storage device, the at least one input device, and the at least one output device.

[0145] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0146] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution device, device or equipment or used in combination with an instruction execution device, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor devices, devices or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0147] To provide interaction with a user, the devices and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0148] The apparatus and techniques described herein can be implemented in a computing device that includes backend components (e.g., as a data server), or a computing device that includes middleware components (e.g., an application server), or a computing device that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the apparatus and techniques described herein), or a computing device that includes any combination of such backend components, middleware components, or front-end components. The components of the apparatus can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0149] A computer device may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. The client-server relationship is established by computer programs running on the respective computers and establishing a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and VPS services ("Virtual Private Servers" or "VPS"). The server may be a cloud server, a distributed device server, or a server integrated with blockchain.

[0150] In one embodiment of the present disclosure, the present disclosure also provides an autonomous driving vehicle, comprising Figure 8 The exemplary electronic device.

[0151] It should be noted that the electronic device is used to execute the vehicle lateral control method of the embodiment of the present disclosure.

[0152] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0153] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for lateral control of a vehicle, comprising: Obtaining an expected longitudinal speed sequence of the vehicle in a predicted time domain after a current moment, wherein the expected longitudinal speed sequence includes: an expected longitudinal speed at each predicted time point; determining a predicted lateral vehicle state parameter of the vehicle at each of the predicted time points based on the operating state information of the vehicle at the current moment and the expected longitudinal speed at each of the predicted time points; determining expected lateral vehicle state parameters at each of the predicted time points in the expected trajectory of the vehicle; determining a lateral control amount of the vehicle at each of the predicted time points based on the predicted lateral vehicle state parameter and the expected lateral vehicle state parameter; performing automatic driving lateral control of the vehicle according to the lateral control amount at a first prediction time point within the prediction time domain; Determining the lateral control amount of the vehicle at each of the predicted time points based on the predicted lateral vehicle state parameter and the expected lateral vehicle state parameter includes: constructing an objective function for a model predictive controller to perform model control prediction on the longitudinal model based on the predicted lateral vehicle state parameters at each of the predicted time points and the expected lateral vehicle state parameters at each of the predicted time points in the expected trajectory; The objective function is solved to obtain the lateral control amount of the vehicle at each of the predicted actual points.

2. The method according to claim 1, wherein Determining the predicted lateral vehicle state parameter of the vehicle at each of the predicted time points based on the operating state information of the vehicle at the current moment and the expected longitudinal speed at each of the predicted time points includes: For the i-th prediction time point, taking the maximum value of the current longitudinal velocity of the vehicle at the current moment and the minimum longitudinal velocity that can be processed by the lateral model as a first longitudinal velocity, and determining a predicted lateral vehicle state parameter of the vehicle at the i-th prediction time point based on the first longitudinal velocity, the lateral model, and the operating state information, where the initial value of i is 1; Add 1 to the i; when i is less than or equal to N, taking the maximum of the expected longitudinal velocity at the (i-1)th prediction time point and the minimum longitudinal velocity processable by the lateral model as a second longitudinal velocity, and determining a predicted lateral vehicle state parameter of the vehicle at the (i-1)th prediction time point based on the second longitudinal velocity, the lateral model, an initial lateral control amount of the vehicle at the (i-1)th prediction time point, and the predicted lateral vehicle state parameter; Going to the step of adding 1 to the value i, repeatedly performing the steps of, when i is less than or equal to N, taking the maximum value of the expected longitudinal velocity at the (i-1)th prediction time point and the minimum longitudinal velocity processable by the lateral model as the second longitudinal velocity, and determining the predicted lateral vehicle state parameter of the vehicle at the (i-1)th prediction time point based on the second longitudinal velocity, the lateral model, the initial lateral control amount of the vehicle at the (i-1)th prediction time point, and the predicted lateral vehicle state parameter, until i is greater than N, where N is the total number of prediction time points in the prediction time domain.

3. The method according to claim 2, wherein: The determining, based on the first longitudinal speed, the lateral model, and the operating state information, a predicted lateral vehicle state parameter of the vehicle at the i-th prediction time point includes: determining, according to the first longitudinal speed, first control parameter information used by the transverse model at the i-th prediction time point; Adjusting the control parameter information of the horizontal model to the first control parameter information; The lateral vehicle state parameters and lateral control variables in the operating state information are input into the lateral model to obtain predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point.

4. The method according to claim 2, wherein: Determining the predicted lateral vehicle state parameter of the vehicle at the i-th prediction time point based on the second longitudinal speed, the lateral model, the initial lateral control amount of the vehicle at the i-1-th prediction time point, and the predicted lateral vehicle state parameter includes: determining, according to the second longitudinal speed, second control parameter information used by the transverse model at the i-th prediction time point; Adjusting the control parameter information of the horizontal model to the second control parameter information; The initial lateral control amount and the predicted lateral vehicle state parameter of the vehicle at the i-1th prediction time point are input into the lateral model to obtain the predicted lateral vehicle state parameter of the vehicle at the i-th prediction time point.

5. The method according to claim 1, wherein Determining the expected lateral vehicle state parameter at each of the predicted time points in the expected trajectory of the vehicle includes: determining a predicted longitudinal position of the vehicle at each of the predicted time points; For each of the predicted time points, determining a target trajectory point at the predicted time point from a plurality of trajectory points in the desired trajectory based on the predicted longitudinal position at the predicted time point, wherein the longitudinal position of the target trajectory point has a minimum distance from the predicted longitudinal position, and the time point corresponding to the target trajectory point is later than the predicted time point; The expected lateral vehicle state parameter at the target trajectory point is used as the expected lateral vehicle state parameter at the prediction time point.

6. The method according to claim 5, wherein: Determining the predicted longitudinal position of the vehicle at each of the predicted time points includes: Obtaining a current longitudinal position and a current longitudinal speed of the vehicle at the current moment; Acquiring an expected longitudinal speed sequence of the vehicle in a prediction time domain, wherein the expected longitudinal speed sequence includes the expected longitudinal speed at each of the prediction time points; A predicted longitudinal position of the vehicle at each predicted time point is determined based on the current longitudinal speed, the current longitudinal position and the expected longitudinal speed sequence.

7. The method according to claim 6, wherein: Determining the predicted longitudinal position of the vehicle at each predicted time point based on the current longitudinal speed, the current longitudinal position, and the expected longitudinal speed sequence includes: For the i-th prediction time point, taking the maximum value of the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that can be processed by the lateral model as a first longitudinal velocity, and determining the predicted longitudinal position at the i-th prediction time point based on the first longitudinal velocity and the current longitudinal position, where the initial value of i is 1; Add 1 to the i; When i is less than or equal to N, taking the maximum of the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that can be processed by the lateral model as the second longitudinal velocity, and determining the predicted longitudinal position at the i-th prediction time point based on the second longitudinal velocity and the predicted longitudinal position at the (i-1)-th prediction time point; Going to the step of adding 1 to the number i, repeatedly performing the step of, when i is less than or equal to N, taking the maximum value of the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that can be processed by the lateral model as the second longitudinal velocity, and determining the predicted longitudinal position at the i-th prediction time point based on the second longitudinal velocity and the predicted longitudinal position at the i-1-th prediction time point, until i is greater than N, where N is the total number of prediction time points in the prediction time domain.

8. A lateral control device for a vehicle, comprising: an acquisition module, configured to acquire an expected longitudinal velocity sequence of the vehicle in a predicted time domain after a current moment, wherein the expected longitudinal velocity sequence includes: an expected longitudinal velocity at each predicted time point; a first determining module, configured to determine a predicted lateral vehicle state parameter of the vehicle at each of the predicted time points based on the operating state information of the vehicle at the current moment and the expected longitudinal speed at each of the predicted time points; a second determination module, configured to determine an expected lateral vehicle state parameter at each of the predicted time points in the expected trajectory of the vehicle; a third determining module, configured to determine a lateral control amount of the vehicle at each of the predicted time points based on the predicted lateral vehicle state parameter and the expected lateral vehicle state parameter; a control module, configured to perform automatic driving lateral control of the vehicle based on the lateral control amount at a first predicted time point within the predicted time domain; The third determining module is specifically configured to: constructing an objective function for a model predictive controller to perform model control prediction on the longitudinal model based on the predicted lateral vehicle state parameters at each of the predicted time points and the expected lateral vehicle state parameters at each of the predicted time points in the expected trajectory; The objective function is solved to obtain the lateral control amount of the vehicle at each of the predicted actual points.

9. The device according to claim 8, wherein The first determining module includes: a first determining unit configured to, for an i-th prediction time point, use a maximum value of a current longitudinal velocity of the vehicle at the current moment and a minimum longitudinal velocity processable by a lateral model as a first longitudinal velocity, and determine a predicted lateral vehicle state parameter of the vehicle at the i-th prediction time point based on the first longitudinal velocity, the lateral model, and the operating state information, where an initial value of i is 1; a processing unit, configured to add 1 to the value i; a second determining unit, configured to, when i is less than or equal to N, use a maximum of the expected longitudinal velocity at the (i-1)th prediction time point and a minimum longitudinal velocity processable by the lateral model as a second longitudinal velocity, and determine a predicted lateral vehicle state parameter of the vehicle at the (i-1)th prediction time point based on the second longitudinal velocity, the lateral model, an initial lateral control amount of the vehicle at the (i-1)th prediction time point, and a predicted lateral vehicle state parameter; Going to the step of adding 1 to the value i, repeatedly performing the steps of, when i is less than or equal to N, taking the maximum value of the expected longitudinal velocity at the (i-1)th prediction time point and the minimum longitudinal velocity processable by the lateral model as the second longitudinal velocity, and determining the predicted lateral vehicle state parameter of the vehicle at the (i-1)th prediction time point based on the second longitudinal velocity, the lateral model, the initial lateral control amount of the vehicle at the (i-1)th prediction time point, and the predicted lateral vehicle state parameter, until i is greater than N, where N is the total number of prediction time points in the prediction time domain.

10. The device according to claim 9, wherein The first determining unit is specifically configured to: determining, according to the first longitudinal speed, first control parameter information used by the transverse model at the i-th prediction time point; Adjusting the control parameter information of the horizontal model to the first control parameter information; The lateral vehicle state parameters and lateral control variables in the operating state information are input into the lateral model to obtain predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point.

11. The device according to claim 9, wherein The second determining unit is specifically configured to: determining, according to the second longitudinal speed, second control parameter information used by the transverse model at the i-th prediction time point; Adjusting the control parameter information of the horizontal model to the second control parameter information; The initial lateral control amount and the predicted lateral vehicle state parameter of the vehicle at the i-1th prediction time point are input into the lateral model to obtain the predicted lateral vehicle state parameter of the vehicle at the i-th prediction time point.

12. The device according to claim 8, wherein The second determining module includes: a third determining unit, configured to determine a predicted longitudinal position of the vehicle at each of the predicted time points; a fourth determining unit, configured to determine, for each of the predicted time points, a target trajectory point at the predicted time point from a plurality of trajectory points in the desired trajectory according to the predicted longitudinal position at the predicted time point, wherein the longitudinal position of the target trajectory point has a minimum distance from the predicted longitudinal position, and the time point corresponding to the target trajectory point is later than the predicted time point; A fifth determining unit is configured to use the expected lateral vehicle state parameter at the target trajectory point as the expected lateral vehicle state parameter at the predicted time point.

13. The device according to claim 12, wherein The third determining unit includes: a first acquiring subunit, configured to acquire a current longitudinal position and a current longitudinal speed of the vehicle at the current moment; a second acquiring unit, configured to acquire an expected longitudinal speed sequence of the vehicle in a prediction time domain, wherein the expected longitudinal speed sequence includes an expected longitudinal speed at each of the prediction time points; The determining subunit is configured to determine a predicted longitudinal position of the vehicle at each predicted time point according to the current longitudinal speed, the current longitudinal position and the expected longitudinal speed sequence.

14. The device according to claim 13, wherein The determining subunit is specifically configured to: For the i-th prediction time point, taking the maximum value of the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that can be processed by the lateral model as a first longitudinal velocity, and determining the predicted longitudinal position at the i-th prediction time point based on the first longitudinal velocity and the current longitudinal position, where the initial value of i is 1; Add 1 to the i; When i is less than or equal to N, taking the maximum of the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that can be processed by the lateral model as the second longitudinal velocity, and determining the predicted longitudinal position at the i-th prediction time point based on the second longitudinal velocity and the predicted longitudinal position at the (i-1)-th prediction time point; Going to the step of adding 1 to the number i, repeatedly performing the step of, when i is less than or equal to N, taking the maximum value of the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that can be processed by the lateral model as the second longitudinal velocity, and determining the predicted longitudinal position at the i-th prediction time point based on the second longitudinal velocity and the predicted longitudinal position at the i-1-th prediction time point, until i is greater than N, where N is the total number of prediction time points in the prediction time domain.

15. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

17. A computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

18. An autonomous driving vehicle comprising: The electronic device according to claim 15.

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