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

Abstract

The disclosure provides a lateral control method and device of a vehicle, a storage medium and an automatic driving vehicle, relates to the technical field of computers, specifically relates to the field of intelligent transportation and the field of automatic driving and the like artificial intelligence technical field. The specific implementation scheme is: when performing lateral control on the vehicle in automatic driving, the running state information of the vehicle at the current time and the expected longitudinal speed of the vehicle at each prediction time point in the prediction time domain after the current time are combined to determine the predicted lateral vehicle state parameters of the vehicle at each prediction time point, and the predicted lateral vehicle state parameters and the expected lateral vehicle state parameters of the vehicle at each prediction time point are combined to determine the lateral control amount at each prediction time point in the prediction time domain, and the vehicle is controlled laterally based on the lateral control amount at the first prediction time point in the prediction time domain. Therefore, the accuracy and stability of the lateral control of the vehicle are improved, and the safety of the automatic driving of the vehicle is improved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, specifically to the fields of intelligent transportation and autonomous driving, and other artificial intelligence technologies, particularly to lateral control methods, devices, storage media, and autonomous vehicles. Background Technology

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

[0003] In the process of lateral control of autonomous vehicles, related technologies typically rely on lateral control variables determined within the autonomous driving system. The accuracy of these lateral control variables is crucial to the stability and accuracy of the vehicle's lateral control. Summary of the Invention

[0004] This disclosure provides a method, apparatus, storage medium, and autonomous vehicle for lateral control of a vehicle.

[0005] According to one aspect of this disclosure, a lateral control method for a vehicle is provided. The method includes: acquiring a desired longitudinal velocity sequence of the vehicle in a predicted time domain after a current moment, wherein the desired longitudinal velocity sequence includes: desired longitudinal velocities at each predicted time point; determining predicted lateral vehicle state parameters of the vehicle at each predicted time point based on the vehicle's operating state information at the current moment and the desired longitudinal velocities at each predicted time point; determining the desired lateral vehicle state parameters at each predicted time point in the vehicle's desired trajectory; determining a lateral control quantity of the vehicle at each predicted time point based on the predicted lateral vehicle state parameters and the desired lateral vehicle state parameters; and performing lateral control for autonomous driving of the vehicle based on the lateral control quantity at the first predicted time point in the predicted time domain.

[0006] According to another aspect of this disclosure, a lateral control device for a vehicle is provided. The device includes: an acquisition module for acquiring a sequence of expected longitudinal speeds of the vehicle in a predicted time domain after a current moment, wherein the expected longitudinal speed sequence includes expected longitudinal speeds at each predicted time point; a first determination module for determining predicted lateral vehicle state parameters of the vehicle at each predicted time point based on the vehicle's operating state information at the current moment and the expected longitudinal speeds at each predicted time point; a second determination module for determining the expected lateral vehicle state parameters at each predicted time point in the vehicle's expected trajectory; a third determination module for determining a lateral control quantity of the vehicle at each predicted time point based on the predicted lateral vehicle state parameters and the expected lateral vehicle state parameters; and a control module for performing lateral control of the vehicle for autonomous driving based on the lateral control quantity at the first predicted time point in the predicted time domain.

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

[0008] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing the computer to perform a lateral control method for a vehicle disclosed in embodiments of this disclosure.

[0009] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the lateral control method for a vehicle of this disclosure.

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

[0011] When performing lateral control for autonomous driving, the predicted lateral vehicle state parameters at each predicted time point are determined by combining the vehicle's current operating state information with the expected longitudinal velocity at each predicted time point within the prediction time domain after the current moment. Then, by combining these predicted and expected lateral vehicle state parameters, the lateral control variables at each predicted time point within the prediction time domain are determined. Based on the lateral control variable at the first predicted time point within the prediction time domain, lateral control of the vehicle is performed. Thus, by combining the expected longitudinal velocity at each predicted time point within the prediction time domain, the lateral control variables for lateral control of the vehicle are accurately determined, improving the accuracy and stability of lateral control and consequently enhancing the safety of autonomous driving.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0014] Figure 1 This is a schematic diagram based on the first embodiment of the present disclosure;

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

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

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

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

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

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

[0021] Figure 8 This is a block diagram of an electronic device used to implement the lateral control method for a vehicle according to embodiments of the present disclosure. Detailed Implementation

[0022] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

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

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

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

[0026] Step 101: Obtain the expected longitudinal velocity sequence of the vehicle in the prediction time domain after the current time, wherein the expected longitudinal velocity sequence includes the expected longitudinal velocity at each prediction time point.

[0027] In this embodiment, the vehicle lateral control method is executed by a vehicle lateral control device, which can be implemented by software and / or hardware. The vehicle 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 embodiments, the above-mentioned electronic device may be a terminal device or a server, etc., and this embodiment does not specifically limit it.

[0029] In some exemplary embodiments, the aforementioned terminal device can be a device that can be installed in any vehicle, such as an onboard controller or an onboard computer. In some examples, the lateral control device of the vehicle can be an onboard computer. It should be noted that the onboard computer in this example has autonomous driving capabilities, can plan routes for the vehicle, and can perform autonomous driving control of the vehicle. In other examples, the lateral control device of the vehicle can 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, with a preset time interval between two adjacent prediction time points.

[0031] The aforementioned preset time interval is pre-set and its value can be adjusted according to actual needs. For example, the prediction time domain can include 25 prediction time points, and the preset time interval between two adjacent prediction time points can be 1 second. Alternatively, the prediction time domain can include 10 prediction time points, and the preset time interval between two adjacent prediction time points can 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 can be combined to determine the length of the prediction time domain and the value of the time interval between prediction time points.

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

[0034] The expected longitudinal velocity 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 its actual situation at the current moment.

[0035] Step 102: Based on the vehicle's current operating status information and the expected longitudinal speed at each predicted time point, determine the predicted lateral vehicle state parameters 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. This embodiment does not specifically limit this.

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

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

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

[0040] The desired trajectory refers to the trajectory that the vehicle is expected to reach in the prediction time domain when planning the vehicle.

[0041] Among them, the desired lateral vehicle state parameters refer to the lateral vehicle state parameters that the vehicle is expected to achieve when planning the vehicle.

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

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

[0045] The lateral control quantity may include the issued front wheel steering angle.

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

[0047] The Model Predictive Control (MPC) is used to predict the lateral control quantities at each prediction time point in the prediction time domain based on the predicted lateral vehicle state parameters and the desired vehicle state parameters, so as to perform autonomous driving control processing on the vehicle based on the lateral control quantity at the first prediction time point in the prediction time domain.

[0048] Specifically, an exemplary process by which the model predictive controller determines the lateral control quantity at each prediction time point is as follows: the model predictive controller determines the objective function used for model predictive control of the lateral model based on the predicted lateral vehicle state parameters and the desired vehicle state parameters, and obtains the lateral control quantity at each prediction time point by solving the optimal solution of the objective function through rolling optimization.

[0049] Step 105: Perform lateral control of the vehicle for autonomous driving based on the lateral control quantity at the first predicted time point in the prediction time domain.

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

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

[0052] The lateral control method for a vehicle according to embodiments of this disclosure, when performing lateral control for autonomous driving, combines the vehicle's current operating state information and the expected longitudinal velocity at each predicted time point within the prediction time domain after the current time to determine the predicted lateral vehicle state parameters at each predicted time point. Then, combining the predicted lateral vehicle state parameters and the expected lateral vehicle state parameters at each predicted time point, it determines the lateral control quantity at each predicted time point within the prediction time domain. Based on the lateral control quantity at the first predicted time point within the prediction time domain, it performs lateral control on the vehicle. Therefore, by combining the expected longitudinal velocity at each predicted time point within the prediction time domain, the lateral control quantity for lateral control of the vehicle is accurately determined, improving the accuracy and stability of lateral control and thus enhancing the safety of autonomous driving.

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

[0054] Step 201: For the i-th prediction time point, take the maximum value between the current longitudinal speed of the vehicle at the current time and the minimum longitudinal speed that the lateral model can handle as the first longitudinal speed, and determine the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point based on the first longitudinal speed, the lateral model and the running state information, where the initial value of i is 1.

[0055] In one embodiment of this disclosure, in order to accurately determine the predicted lateral vehicle state parameters at the i-th prediction time point, one possible implementation of determining the predicted lateral vehicle state parameters at the i-th prediction time point based on the first longitudinal speed, the lateral model, and the operating state information is as follows: based on the first longitudinal speed, determine the first control parameter information used by the lateral model at the i-th prediction time point; adjust the control parameter information of the lateral model to the first control parameter information; input the lateral vehicle state parameters and lateral control quantity from the operating state information into the lateral model to obtain the predicted lateral vehicle state parameters at the i-th prediction time point.

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

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

[0058] Where i equals 1, after adjusting the control parameter information of the lateral model 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] In the formula, x1 represents the predicted lateral vehicle state parameter at the first prediction time point, and A in the formula... d This represents the first weight information, B in the formula. d This represents the second weighting information, x. cur u represents the lateral vehicle state parameter of the vehicle at the current moment. cur This indicates the amount of lateral control applied to the vehicle at the current moment.

[0061] Taking bilinear discretization as an example, the above A d and B d An example representation is as follows:

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

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

[0064] Where B represents the pre-set 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] Where A represents the weight matrix determined based on the first longitudinal velocity.

[0066] in, In the formula, c f For the front axle lateral stiffness, c r Rear axle lateral stiffness; l f l is the distance from the front axle to the vehicle's center of gravity. r I is the distance from the rear axle to the vehicle's center of gravity. Z Let m be the moment of inertia at the vehicle's center of gravity, and v be the total vehicle mass. cur Let τ be the vehicle's current longitudinal speed at the current moment, where τ is a pre-set delay time coefficient.

[0067] Step 202: Increment i by 1.

[0068] Step 203: If i is less than or equal to N, take the maximum value of the expected longitudinal velocity and the minimum longitudinal velocity that the lateral model can handle at the (i-1)th prediction time point as the second longitudinal velocity. Based on the second longitudinal velocity, the lateral model, the initial lateral control quantity of the vehicle at the (i-1)th prediction time point, and the predicted lateral vehicle state parameters, determine the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point, and jump to step 202.

[0069] Where N is the total number of prediction time points within the prediction time domain.

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

[0071] In some embodiments of this disclosure, in order to accurately determine the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point, one possible implementation of 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 quantity of the vehicle at the (i-1)-th prediction time point, and the predicted lateral vehicle state parameters is as follows: Based on the second longitudinal speed, determine the second control parameter information used by the lateral model at the i-th prediction time point; adjust the control parameter information of the lateral model to the second control parameter information; input the initial lateral control quantity of the vehicle at the (i-1)-th prediction time point and the predicted lateral vehicle state parameters into the lateral model to obtain the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point.

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

[0073] Where 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 as follows:

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

[0075] Where x in the formula i+1 Let x represent the predicted lateral vehicle state parameters at the (i+1)th prediction time point. i A represents the predicted lateral vehicle state parameters at the i-th prediction time point. di This represents the first control parameter information used by the lateral model at the i-th prediction time, where B in the formula... di This represents the second control parameter information used by the lateral model at the i-th prediction time, u i This represents the initial longitudinal control quantity applied to the vehicle at the i-th prediction time point. Here, N represents the total number of prediction time points within the prediction time domain.

[0076] Taking bilinear discretization as an example, the above A di and B di An example representation is as follows:

[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] Where B represents the pre-set weight information, and t represents the time interval between adjacent prediction time points. It should be noted that the time interval between the current time and the first prediction time point in the prediction time domain is also t.

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

[0081] in,

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

[0083] Among them, v minv represents the minimum longitudinal velocity that the lateral model can handle. ri This represents the expected longitudinal velocity at the i-th prediction time point.

[0084] Specifically, the second longitudinal velocity corresponding to the i-th prediction time point is determined by taking the expected longitudinal velocity and the minimum longitudinal velocity that the lateral model can handle at the i-th prediction time point as the second longitudinal velocity corresponding to the i-th prediction time point.

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

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

[0087] In one embodiment of this disclosure, the predicted longitudinal velocity of the vehicle at each predicted time point can be obtained, and the predicted longitudinal position of the vehicle at each predicted time point can be determined based on the predicted longitudinal velocity at each predicted time point and the current position of the vehicle at the current moment.

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

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

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

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

[0092] Step 402: Obtain the 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.

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

[0094] In one embodiment of this disclosure, a possible implementation of determining the predicted longitudinal position of the vehicle at each predicted time point based on the current longitudinal speed, current longitudinal position, and expected longitudinal speed sequence is as follows: For the i-th predicted time point, the maximum value between the expected longitudinal speed at the i-th predicted time point and the minimum longitudinal speed that the lateral model can handle is taken as the first longitudinal speed. Based on the first longitudinal speed and the current longitudinal position, the predicted longitudinal position at the i-th predicted time point is determined, where the initial value of i is 1; i is incremented by 1; if i is less than or equal to N, the maximum value between the expected longitudinal speed at the i-th predicted time point and the minimum longitudinal speed that the lateral model can handle is taken as the second longitudinal speed. Based on the second longitudinal speed and the predicted longitudinal position at the (i-1)-th predicted time point, the predicted longitudinal position at the i-th predicted time point is determined, and the process proceeds to the step of incrementing i by 1, where N is the total number of predicted time points in the prediction time domain.

[0095] In one embodiment of this disclosure, when i equals 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] In the formula, S1 represents the predicted longitudinal position at the first prediction time point within the prediction time domain; v1 represents the first longitudinal velocity corresponding to the first prediction time point; and v... cur This represents the vehicle's current longitudinal velocity at the current moment; v min This represents the minimum longitudinal velocity that the lateral model can handle. T represents the time interval between the current time and the first prediction time point.

[0099] In one embodiment of this 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] Wherein, S in the formula i This represents the predicted longitudinal position at the i-th prediction time point within the prediction time domain; v in the formula iThis represents the second longitudinal velocity at the i-th prediction time point, where v in the formula... ri v represents the expected longitudinal velocity at the i-th prediction time point; min This represents the minimum longitudinal velocity that the lateral model can handle. t represents the time interval between two adjacent prediction time points.

[0103] In the embodiments of this disclosure, the predicted longitudinal position at the first prediction time point in the prediction time domain is accurately determined by combining the vehicle's current longitudinal speed, current longitudinal position, and the minimum longitudinal speed that the lateral model can handle at the current moment. For the i-th prediction time point, the predicted longitudinal position at the i-th prediction time point is determined by combining the predicted longitudinal position at the (i-1)-th prediction time point, the expected longitudinal speed, and the minimum longitudinal speed that the lateral model can handle, where i takes the value of 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 this disclosure, in order to accurately determine the lateral control amount of the vehicle at each predicted time point, one possible implementation of step 104 is as follows: Figure 5 As shown, it may include:

[0105] Step 501: 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, construct the objective function for the model predictive controller to perform model control prediction on the longitudinal model.

[0106] The formula for the objective function J is shown below:

[0107]

[0108] Where, Y in the formula i Y represents the predicted lateral vehicle state parameters at the i-th prediction time point; ri U represents the expected lateral vehicle state parameters 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 represents the expected lateral vehicle state parameters at the i-th prediction time point. i ΔU represents the initial lateral control quantity at the i-th prediction time point; i R1 represents the control increment at the i-th prediction time point; R2 represents the second penalty weight; R3 represents the third penalty weight; T represents the transpose; N represents the total number of prediction time points in the prediction time domain.

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

[0110] The method of solving the objective function to obtain the lateral control quantity of the vehicle at each predicted actual point can be found in related technologies. An exemplary implementation method is to obtain the longitudinal control quantity at each predicted time point by solving the optimal solution of the objective function through rolling optimization.

[0111] To implement the above embodiments, this disclosure also provides a lateral control device for a vehicle.

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

[0113] like Figure 6 As shown, the lateral control device 60 of the vehicle 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 used to acquire the expected longitudinal velocity sequence of the vehicle in the prediction time domain after the current time, wherein the expected longitudinal velocity sequence includes the expected longitudinal velocity at each prediction time point.

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

[0116] The second determining module 63 is used to determine the expected lateral vehicle state parameters at each predicted time point in the vehicle's expected trajectory.

[0117] The third determining module 64 is used to determine the lateral control quantity of the vehicle at each predicted time point based on the predicted lateral vehicle state parameters and the expected lateral vehicle state parameters.

[0118] The control module 65 is used to perform lateral control of the vehicle for autonomous driving based on the lateral control quantity at the first prediction time point in the prediction time domain.

[0119] The lateral control device for a vehicle according to this embodiment, when performing lateral control for autonomous driving, combines the vehicle's current operating state information with the expected longitudinal velocity at each predicted time point in the prediction time domain after the current time to determine the predicted lateral vehicle state parameters at each predicted time point. Then, combining the predicted lateral vehicle state parameters and the expected lateral vehicle state parameters at each predicted time point, it determines the lateral control quantity at each predicted time point in the prediction time domain. Based on the lateral control quantity at the first predicted time point in the prediction time domain, it performs lateral control on the vehicle. Therefore, by combining the expected longitudinal velocity at each predicted time point in the prediction time domain, the lateral control quantity for lateral control of the vehicle is accurately determined, improving the accuracy and stability of the vehicle's lateral control, thereby enhancing the safety of autonomous driving.

[0120] In one embodiment of this disclosure, such as 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. 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 detailed descriptions of the acquisition module 71 and the control module 75 can be found above. Figure 6 The descriptions of the acquisition module 61 and the control module 65 are not provided here.

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

[0123] The first determining unit 721 is used to, for the i-th prediction time point, take the maximum value of the current longitudinal speed of the vehicle at the current moment and the minimum longitudinal speed that the lateral model can handle as the first longitudinal speed, and determine the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point based on the first longitudinal speed, the lateral model and the running state information, where the initial value of i is 1.

[0124] Processing unit 722 is used to increment i by 1;

[0125] The second determining unit 723 is used to, when i is less than or equal to N, take the maximum value of the expected longitudinal speed at the (i-1)th prediction time point and the minimum longitudinal speed that the lateral model can handle as the second longitudinal speed, and determine 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 quantity of the vehicle at the (i-1)th prediction time point and the predicted lateral vehicle state parameters, and then proceed to the step of incrementing i by 1, where N is the total number of prediction time points in the prediction time domain.

[0126] In one embodiment of this disclosure, the first determining 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 velocity; adjust the control parameter information of the lateral model to the first control parameter information; and input the lateral vehicle state parameters and lateral control quantities in the running 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 this disclosure, the second determining 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; and input the initial lateral control quantity and the 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 this disclosure, the second determining module 73 includes:

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

[0130] The fourth determining unit 732 is used to determine, for each predicted time point, the 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.

[0131] The fifth determining unit 733 is used to take the expected lateral vehicle state parameters at the target trajectory point as the expected lateral vehicle state parameters at the prediction time point.

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

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

[0134] The second acquisition unit 7312 is used to acquire the 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 used to 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 desired longitudinal speed sequence.

[0136] In one embodiment of this disclosure, the aforementioned determining subunit 7313 is specifically configured to: for the i-th prediction time point, take the maximum value of the expected longitudinal velocity and the minimum longitudinal velocity that the lateral model can handle at the i-th prediction time point as the first longitudinal velocity; determine the predicted longitudinal position at the i-th prediction time point based on the first longitudinal velocity and the current longitudinal position, wherein the initial value of i is 1; increment i by 1; if i is less than or equal to N, take the maximum value of the expected longitudinal velocity and the minimum longitudinal velocity that the lateral model can handle at the i-th prediction time point 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 proceed to the step of incrementing i by 1, wherein N is the total number of prediction time points in the prediction time domain.

[0137] In one embodiment of this disclosure, the third determining module 74 is specifically used to: construct an objective function for model predictive controller to perform model control prediction on the 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; and solve the objective function to obtain the lateral control quantity of the vehicle at each predicted actual point.

[0138] It should be noted that the above explanation of the lateral control method for the vehicle also applies to the lateral control device of the vehicle in this embodiment, and this embodiment will not repeat the above.

[0139] According to embodiments of this disclosure, this 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 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative 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. The RAM 803 may also store various programs and data required for the operation of the device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

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

[0143] The computing unit 801 can be a variety of general-purpose and / or special-purpose 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 special-purpose 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 a lateral control method for a vehicle. For example, in some embodiments, the lateral control method for a vehicle may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the lateral control method for a vehicle described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the lateral control method for a vehicle by any other suitable means (e.g., by means of firmware).

[0144] Various embodiments of the apparatuses 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), device-on-a-chip (SoC) devices, complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable device including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage device, at least one input device, and at least one output device, and transmitting data and instructions to the storage device, the at least one input device, and the at least one output device.

[0145] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

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

[0147] To provide interaction with a user, the apparatus and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of apparatus can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0148] The apparatus and techniques described herein can be implemented in computing devices that include backend components (e.g., as a data server), or computing devices that include middleware components (e.g., an application server), or computing devices that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the apparatus and techniques described herein), or computing devices that include any combination of such backend, middleware, or frontend components. The components of the apparatus can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0149] Computer devices can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. A server can be a cloud server, a distributed server, or a server incorporating blockchain technology.

[0150] In one embodiment of this disclosure, an autonomous vehicle is also provided, including... Figure 8 The exemplary electronic device.

[0151] It should be noted that this electronic device is used to execute the lateral control method for a vehicle according to embodiments of this disclosure.

[0152] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

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

Claims

1. A method for lateral control of a vehicle, comprising: Obtain the expected longitudinal velocity sequence of the vehicle in the prediction time domain after the current time, wherein the expected longitudinal velocity sequence includes: the expected longitudinal velocity at each prediction time point; Based on the vehicle's operating status information at the current moment and the expected longitudinal speed at each of the predicted time points, the predicted lateral vehicle state parameters of the vehicle at each of the predicted time points are determined. Determine the expected lateral vehicle state parameters at each predicted time point in the expected trajectory of the vehicle; Based on the predicted lateral vehicle state parameters and the desired lateral vehicle state parameters, the lateral control quantity of the vehicle at each of the predicted time points is determined. Based on the lateral control quantity at the first predicted time point within the predicted time domain, the vehicle is subjected to lateral control for autonomous driving. The step of determining the predicted lateral vehicle state parameters of the vehicle at each predicted time point based on the vehicle's operating state information at the current moment and the expected longitudinal velocity at each predicted time point includes: For the i-th prediction time point, the maximum value between the current longitudinal speed of the vehicle at the current time and the minimum longitudinal speed that the lateral model can handle is taken as the first longitudinal speed. Based on the first longitudinal speed, the lateral model and the running state information, the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point are determined, where the initial value of i is 1. Increment i by 1; 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 the lateral model can handle is taken as the second longitudinal velocity. Based on the second longitudinal velocity, the lateral model, the initial lateral control quantity of the vehicle at the (i-1)th prediction time point, and the predicted lateral vehicle state parameters, the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point are determined. Proceed to the step of incrementing i by 1, and repeat the step of taking the maximum value of the expected longitudinal velocity at the (i-1)th prediction time point and the minimum longitudinal velocity that the lateral model can handle as the second longitudinal velocity when i is less than or equal to N, and determining the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point based on the second longitudinal velocity, the lateral model, the initial lateral control quantity of the vehicle at the (i-1)th prediction time point and the predicted lateral vehicle state parameters, until i is greater than N, where N is the total number of prediction time points in the prediction time domain.

2. The method according to claim 1, wherein, The step of 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 state information includes: Based on the first longitudinal velocity, determine the first control parameter information used by the lateral model at the i-th prediction time point; Adjust the control parameter information of the lateral model to the first control parameter information; The lateral vehicle state parameters and lateral control quantities in the operating status information are input into the lateral model to obtain the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point.

3. The method according to claim 1, wherein, The step of determining the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point based on the second longitudinal velocity, the lateral model, the initial lateral control quantity of the vehicle at the (i-1)-th prediction time point, and the predicted lateral vehicle state parameters includes: Based on the second longitudinal velocity, determine the second control parameter information used by the lateral model at the i-th prediction time point; The control parameter information of the lateral model is adjusted to the second control parameter information; The initial lateral control quantity and predicted lateral vehicle state parameters of the vehicle at the (i-1)th prediction time point are input into the lateral model to obtain the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point.

4. The method according to claim 1, wherein, Determining the expected lateral vehicle state parameters at each predicted time point in the expected trajectory of the vehicle includes: Determine the predicted longitudinal position of the vehicle at each of the predicted time points; For each predicted time point, based on the predicted longitudinal position at the predicted time point, a target trajectory point at the predicted time point is determined from multiple trajectory points in the expected trajectory, 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. The expected lateral vehicle state parameters at the target trajectory point are used as the expected lateral vehicle state parameters at the predicted time point.

5. The method according to claim 4, wherein, Determining the predicted longitudinal position of the vehicle at each of the predicted time points includes: Obtain the current longitudinal position and current longitudinal speed of the vehicle at the current moment; Obtain the 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 of the prediction time points; Based on the current longitudinal speed, the current longitudinal position, and the expected longitudinal speed sequence, the predicted longitudinal position of the vehicle at each predicted time point is determined.

6. The method according to claim 5, 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, the maximum value between the expected longitudinal velocity and the minimum longitudinal velocity that the lateral model can handle at the i-th prediction time point is taken as the first longitudinal velocity. Based on the first longitudinal velocity and the current longitudinal position, the predicted longitudinal position at the i-th prediction time point is determined, where the initial value of i is 1. Increment i by 1; When i is less than or equal to N, the maximum value between the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that the lateral model can handle is taken as the second longitudinal velocity. Based on the second longitudinal velocity and the predicted longitudinal position at the (i-1)-th prediction time point, the predicted longitudinal position at the i-th prediction time point is determined. Proceed to the step of incrementing i by 1, and repeat the step of taking the maximum value of the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that the lateral model can handle as the second longitudinal velocity when i is less than or equal to N, 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.

7. A lateral control device for a vehicle, comprising: The acquisition module is used to acquire the expected longitudinal velocity sequence of the vehicle in the prediction time domain after the current time, wherein the expected longitudinal velocity sequence includes: the expected longitudinal velocity at each prediction time point; The first determining module is used to determine the predicted lateral vehicle state parameters of the vehicle at each of the predicted time points based on the vehicle's operating state information at the current time and the expected longitudinal speed at each of the predicted time points. The second determining module is used to determine the expected lateral vehicle state parameters at each predicted time point in the expected trajectory of the vehicle. The third determining module is used to determine the lateral control quantity of the vehicle at each of the predicted lateral vehicle state parameters and the expected lateral vehicle state parameters. The control module is used to perform lateral control of the vehicle for autonomous driving based on the lateral control quantity at the first predicted time point in the predicted time domain. The first determining module includes: The first determining unit is configured to, for the i-th prediction time point, take the maximum value of the current longitudinal speed of the vehicle at the current time and the minimum longitudinal speed that the lateral model can handle as the first longitudinal speed, and determine the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point based on the first longitudinal speed, the lateral model and the running state information, wherein the initial value of i is 1. The processing unit is used to increment i by 1; The second determining unit is configured to, when i is less than or equal to N, take the maximum value of the expected longitudinal speed at the (i-1)th prediction time point and the minimum longitudinal speed that the lateral model can handle as the second longitudinal speed, and determine 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 quantity of the vehicle at the (i-1)th prediction time point, and the predicted lateral vehicle state parameters; then proceed to the step of incrementing i by 1, and repeat the step of taking the maximum value of the expected longitudinal speed at the (i-1)th prediction time point and the minimum longitudinal speed that the lateral model can handle as the second longitudinal speed, and 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 quantity of the vehicle at the (i-1)th prediction time point, and the predicted lateral vehicle state parameters of the vehicle at the i-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. The apparatus according to claim 7, wherein, The first determining unit is specifically used for: Based on the first longitudinal velocity, determine the first control parameter information used by the lateral model at the i-th prediction time point; Adjust the control parameter information of the lateral model to the first control parameter information; The lateral vehicle state parameters and lateral control quantities in the operating status information are input into the lateral model to obtain the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point.

9. The apparatus according to claim 7, wherein, The second determining unit is specifically used for: Based on the second longitudinal velocity, determine the second control parameter information used by the lateral model at the i-th prediction time point; The control parameter information of the lateral model is adjusted to the second control parameter information; The initial lateral control quantity and predicted lateral vehicle state parameters of the vehicle at the (i-1)th prediction time point are input into the lateral model to obtain the predicted lateral vehicle state parameters of the vehicle at the i-th prediction time point.

10. The apparatus according to claim 7, wherein, The second determining module includes: The third determining unit is used to determine the predicted longitudinal position of the vehicle at each of the predicted time points; The fourth determining unit is used to determine, for each predicted time point, a target trajectory 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. The fifth determining unit is used to take the expected lateral vehicle state parameters at the target trajectory point as the expected lateral vehicle state parameters at the predicted time point.

11. The apparatus according to claim 10, wherein, The third determining unit includes: The first acquisition subunit is used to acquire the current longitudinal position and current longitudinal speed of the vehicle at the current moment; The second acquisition unit is used to acquire the 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 of the prediction time points; A determination subunit is used to 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.

12. The apparatus according to claim 11, wherein, The determining subunit is specifically used for: For the i-th prediction time point, the maximum value between the expected longitudinal velocity and the minimum longitudinal velocity that the lateral model can handle at the i-th prediction time point is taken as the first longitudinal velocity. Based on the first longitudinal velocity and the current longitudinal position, the predicted longitudinal position at the i-th prediction time point is determined, where the initial value of i is 1. Increment i by 1; When i is less than or equal to N, the maximum value between the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that the lateral model can handle is taken as the second longitudinal velocity. Based on the second longitudinal velocity and the predicted longitudinal position at the (i-1)-th prediction time point, the predicted longitudinal position at the i-th prediction time point is determined. Proceed to the step of incrementing i by 1, and repeat the step of taking the maximum value of the expected longitudinal velocity at the i-th prediction time point and the minimum longitudinal velocity that the lateral model can handle as the second longitudinal velocity when i is less than or equal to N, 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.

13. 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 to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

15. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

16. An autonomous vehicle, comprising: The electronic device as claimed in claim 13.

Images