Driving track control method of vehicle in S-shaped curve, vehicle and storage medium

Abstract

The invention provides a driving track control method of a vehicle in an S-shaped curve, the vehicle and a storage medium, and relates to the technical field of intelligent driving. And the discrete point set is fitted into the Nth-degree polynomial target curve, and N is greater than or equal to 5, so that the form of the real lane boundary can be represented more accurately and completely, and the reliability is high. As the form of the real lane boundary can be represented more accurately and completely, whether the target curve is the S-shaped curve can be accurately identified. Under the condition that the target curve is identified as the S-shaped curve, a discrete point set of the S-shaped curve and the current motion state data of the vehicle are input into a pre-configured model prediction control algorithm to output a target driving track enabling the sum of steering angles of the vehicle to be minimum when the vehicle runs in a lane of the S-shaped curve, so that the transverse swing of the vehicle can be relatively small, and the driving stability of the vehicle is improved. The driving stability is high, and the driving safety and the driving experience feeling of the user are further improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a method for controlling a driving trajectory of a vehicle in an S-shaped curve, a vehicle, and a storage medium. Background Art

[0002] Intelligent driving is a technology that combines advanced sensors, control systems, artificial intelligence, and machine learning technologies to allow vehicles to drive safely with little or no human intervention. In intelligent driving technology based on scene recognition, the vehicle can collect a set of discrete points on the lane boundary, and then obtain a lane curve based on a trinomial fitting. Since the trinomial cannot accurately express the shape of the lane line, in order to avoid the vehicle running out of the lane and causing a safety accident, it is necessary to control the vehicle to drive along the center line of the trinomial lane curve. However, the safety of intelligent driving of the vehicle is still relatively low, and when the vehicle is driving along the center line of the trinomial lane curve, the sum of the vehicle's turning angles is large, resulting in a large lateral swing of the vehicle and low driving stability, which further leads to low driving safety and a poor driving experience. Summary of the invention

[0003] The present application provides a method for controlling the driving trajectory of a vehicle in an S-shaped curve, a vehicle and a storage medium, which are used to solve the problems of large lateral swing of the vehicle, low driving stability, low driving safety and poor driving experience in the prior art.

[0004] In a first aspect, the present application provides a method for controlling a driving trajectory of a vehicle in an S-shaped curve, which is applied to a vehicle. The method provided by the present application includes:

[0005] Get a set of discrete points of the lane boundary in front of the vehicle;

[0006] Fit the discrete point set into a target curve, where the target curve is an N-order polynomial, N≥5;

[0007] Identify whether the target curve is an S-shaped curve;

[0008] When the target curve is identified as an S-shaped curve, a discrete point set of the S-shaped curve and the current motion state data of the vehicle are input into a preconfigured model predictive control algorithm to output a target driving trajectory that minimizes the sum of the steering angles of the vehicle when driving in the lane of the S-shaped curve, wherein the constraints of the model predictive control algorithm include that a single steering angle of the vehicle driving in the lane of the S-shaped curve is lower than a set angle threshold, and the driving trajectory of the vehicle cannot overlap with the lane boundary;

[0009] When the vehicle reaches the start point of the lane of the S-curve, the vehicle is controlled to travel in the lane of the S-curve based on the target travel trajectory.

[0010] In some implementations, obtaining a set of discrete points of a lane boundary in front of the vehicle includes:

[0011] Determine whether the vehicle has loaded a high-precision map of the preset distance ahead;

[0012] If a high-precision map is loaded, a set of coordinate points of the lane boundary in front of the vehicle is extracted from the high-precision map as a set of discrete points.

[0013] In some implementations, obtaining a set of discrete points of a lane boundary in front of the vehicle includes:

[0014] Determine whether the vehicle has loaded a high-precision map of the preset distance ahead;

[0015] If no high-precision map is loaded, the road image ahead is collected;

[0016] A lane image is segmented from the front road image, and a set of pixel points at the lane boundary is extracted from the lane image as a discrete point set.

[0017] In some implementations, identifying whether the target curve is an S-shaped curve includes:

[0018] Detect the curvature of each discrete point of the target curve;

[0019] When the number of times the curvature reverses its sign is greater than or equal to 2 times, and the distance extended by each group of discrete points of the curvature with the same sign is greater than a set distance threshold, the target curve is determined to be an S-shaped curve.

[0020] In some implementations, identifying whether the target curve is an S-shaped curve includes:

[0021] The target curve is input into a pre-trained S-shaped curve recognition model to identify whether the target curve is an S-shaped curve, wherein the S-shaped curve recognition model is trained by inputting multiple training samples into a neural network, each training sample includes a historical target curve, and the historical target curve is marked with a label of whether it is an S-shaped curve.

[0022] In some embodiments, when N=5, the expression of the N-order polynomial is y(x)=a5x 5 +a4x 4 +a3x 3 +a2x 2 +a1x, and satisfies y(x0) ’ =0,y(x n ) ’ =0, where y(x0) ’ is the slope of the starting point of the target curve, y(x n ) ’is the slope of the end point of the target curve, [x, y(x)] is the coordinate of the discrete point, and a1, a2, a3, a4, and a5 are different fitting coefficients.

[0023] In some implementations, the constraints of the model predictive control algorithm further include:

[0024] The speed of the vehicle is within a preset speed threshold range, and the acceleration of the vehicle is within a preset acceleration threshold range.

[0025] In a second aspect, the present application provides a vehicle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the vehicle executes the method provided in the first aspect of the present application.

[0026] In a third aspect, the present application further provides a storage medium storing a computer program. When the computer program is executed by a processor, the vehicle executes the method provided in the first aspect of the present application.

[0027] In a fourth aspect, the present application also provides a computer program product, including a computer program, which, when executed, enables a vehicle to execute the method provided in the first aspect of the present application.

[0028] The present application provides a method for controlling the driving trajectory of a vehicle in an S-shaped curve, a vehicle and a storage medium, which fits a discrete point set into an N-order polynomial target curve. Since N≥5, the shape of the real lane boundary can be more accurately and completely characterized, and the reliability is high. Since the shape of the real lane boundary can be more accurately and completely characterized, it is possible to accurately identify whether the target curve is an S-shaped curve.

[0029] When the target curve is identified as an S-shaped curve, the discrete point set of the S-shaped curve and the current motion state data of the vehicle are input into the preconfigured model predictive control algorithm to output a target driving trajectory that minimizes the sum of the steering angles of the vehicle when driving in the lane of the S-shaped curve, wherein the constraints of the model predictive control algorithm include that the single steering angle of the vehicle in the lane of the S-shaped curve is lower than the set angle threshold, and the driving trajectory of the vehicle cannot coincide with the lane boundary. When the vehicle reaches the starting point of the lane of the S-shaped curve, the vehicle is controlled to drive in the lane of the S-shaped curve based on the target driving trajectory; since the sum of the turning angles of the vehicle driving along the target driving trajectory is small, the lateral swing of the vehicle can be small, and the driving stability is high, which further improves the driving safety and the driving experience of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0031] Figure 1 A flow chart of a method for controlling a driving trajectory of a vehicle in an S-shaped curve provided in an embodiment of the present application;

[0032] Figure 2 A schematic diagram of a vehicle driving in an S-curve lane provided in an embodiment of the present application;

[0033] Figure 3 A block diagram of the functional modules of a device for controlling the driving trajectory of a vehicle in an S-shaped curve provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0035] Various structural schematic diagrams according to embodiments of the present disclosure are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may further design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0036] In the context of the present disclosure, when a layer / element is referred to as being "on" another layer / element, the layer / element may be directly on the other layer / element or an intervening layer / element may exist therebetween. In addition, if a layer / element is "on" another layer / element in one orientation, the layer / element may be "below" the other layer / element when the orientation is reversed.

[0037] The following is a detailed description of the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0038] The present application embodiment provides a method for controlling the driving trajectory of a vehicle in an S-shaped curve, which is applied to a vehicle. Figure 1 As shown, the method provided in the embodiment of the present application includes:

[0039] S101: Obtain a discrete point set of the lane boundary in front of the vehicle.

[0040] The specific implementation of S101 includes but is not limited to the following two methods:

[0041] The first method: determine whether the vehicle is loaded with a high-precision map (i.e., HD map) of a preset distance ahead. If a high-precision map is loaded, extract the coordinate point set of the lane boundary in front of the vehicle from the high-precision map as a discrete point set. It can be understood that the vehicle can load a high-precision map of a preset distance ahead (such as 2km) from the cloud at preset intervals (such as 1min) or every time it travels a certain distance (such as 20m). If the network signal is good, the high-precision map of the preset distance ahead can be loaded successfully. If the network signal is poor, the loading of the high-precision map of the preset distance ahead fails. Therefore, it is necessary to determine whether the vehicle is loaded with a high-precision map of the preset distance ahead. Since the coordinate point set of the lane boundary of the high-precision map has a high accuracy, the coordinate point set of the lane boundary of the high-precision map is preferentially used as a discrete point set.

[0042] The second method is to determine whether the vehicle is loaded with a high-precision map of the preset distance ahead; if the high-precision map is not loaded, the road image ahead is collected; for example, the road image ahead can be collected by a vehicle-mounted front-view camera. Then, a lane image is segmented from the road image ahead, and a set of pixel points at the lane boundary is extracted from the lane image as a set of discrete points.

[0043] S102: Fitting the discrete point set into a target curve, wherein the target curve is an N-order polynomial, where N≥5.

[0044] For example, when N=5, the expression of the N-order polynomial is y(x)=a5x 5 +a4x 4 +a3x 3 +a2x 2 +a1x, and satisfies y(x0) ’ =0,y(x n ) ’ =0, where y(x0) ’ is the slope of the starting point of the target curve, i.e. y(x0) ’ is the first-order derivative of y(x0); y(x n ) ’ is the slope of the end point of the target curve, that is, y(x n ) ’ is y(xn ), [x, y(x)] is the coordinate of the discrete point, a1, a2, a3, a4, a5 are different fitting coefficients. It should be noted that the coordinate of each discrete point in the discrete point set satisfies the above fifth-order polynomial. It can be understood that [x0, y(x0)] is the starting point of the target curve, [x n , y(x n )] is the end point of the target curve. In addition, it can also satisfy y(x0)"=0, y(x n )"=0, and y(x0)" is the second-order derivative of y(x0), y(x n )" is y(x n ), [x, y(x)] is the coordinate of the discrete point, a1, a2, a3, a4, a5 are different fitting coefficients.

[0045] In addition, the principle of the polynomial when N=6 or N=7 is the same as that of the polynomial when N=5, which will not be described in detail. It can be understood that since N≥5, the shape of the actual lane boundary can be more accurately and completely represented with high reliability.

[0046] S103: Identify whether the target curve is an S-shaped curve, if yes, execute S104.

[0047] For example, the specific implementation of S103 includes but is not limited to the following two methods:

[0048] The first method: detect the curvature of each discrete point of the target curve; when the number of times the curvature reverses its sign is greater than or equal to 2 times, and the distance extended by each group of discrete points of the curvature with the same sign is greater than the set distance threshold, determine that the target curve is an S-shaped curve. It can be understood that the curvature of a curve refers to the rotation rate of the tangent direction angle to the arc length at a certain point on the curve. Generally, the curvature of an S-curve is characterized by changing from a positive value (left bend) to a negative value (right bend), and then from a negative value (right bend) to a positive value (left bend), or from a negative value (right bend) to a positive value (left bend), and then from a positive value (left bend) to a negative value (right bend). In this way, when the number of times the curvature reverses its sign (such as 2 times, 3 times, or 4 times, etc.) is greater than or equal to 2 times, it means that the curvature of the target curve has reversed twice; in addition, in order to ensure reliability, it is also necessary to determine that when the distance extended by discrete points of the curvature with the same sign in each group is greater than a set distance threshold (such as 30m or 50m), the target curve is determined to be an S-shaped curve.

[0049] The second method is to input the target curve into a pre-trained S-shaped curve recognition model to identify whether the target curve is an S-shaped curve. The S-shaped curve recognition model is obtained by inputting multiple training samples into a neural network for training, each training sample includes a historical target curve, and the historical target curve is marked with a label of whether it is an S-shaped curve. For example, a label of "1" indicates that the historical target curve is an S-shaped curve; a label of "0" indicates that the historical target curve is not an S-shaped curve.

[0050] It can be understood that since the above quinomial formula can more accurately and completely characterize the shape of the actual lane boundary, it can accurately identify whether the target curve is an S-shaped curve.

[0051] S104: When the target curve is identified as an S-shaped curve, a discrete point set of the S-shaped curve and the current motion state data of the vehicle are input into a preconfigured model predictive control algorithm to output a target driving trajectory that minimizes the sum of the steering angles of the vehicle when driving in the lane of the S-shaped curve.

[0052] Exemplarily, the current motion state data of the vehicle may include but is not limited to the current position and the current speed. Among them, the constraints of the model predictive control algorithm include that the single turning angle of the vehicle in the lane of the S-curve is lower than the set angle threshold, and the vehicle's driving trajectory cannot coincide with the lane boundary. In some embodiments, the constraints of the model predictive control algorithm may also include: the speed of the vehicle is within a preset speed threshold (such as 80km / h), and the acceleration of the vehicle is within a preset acceleration threshold.

[0053] Model Predictive Control (MPC) is an advanced control method that guides current control decisions by predicting the future behavior of the system. It generates control inputs by establishing a mathematical model of a dynamic system, combining the system's operating constraints and performance indicators, and optimizing future states. For example, it can output a target driving trajectory that minimizes the sum of the steering angles of the vehicle when driving in an S-curve lane through a quadratic programming (QP) algorithm or a nonlinear optimization solver (IPOPT) (Interior Point OPTimizer, IPOPT).

[0054] S105: When the vehicle reaches the starting point of the lane of the S-shaped curve, the vehicle is controlled to travel in the lane of the S-shaped curve based on the target driving trajectory.

[0055] For example, the vehicle 101 may be traveling in the S-curved lane 102 as follows: Figure 2As shown, it should be noted that the vehicle 101 exceeds the lane boundary 103 when traveling in the S-curved lane 102 .

[0056] In summary, the embodiment of the present application provides a method for controlling the driving trajectory of a vehicle in an S-shaped curve, which fits a set of discrete points into a target curve of an N-order polynomial. Since N ≥ 5, the shape of the actual lane boundary can be more accurately and completely characterized, and the reliability is high. Since the shape of the actual lane boundary can be more accurately and completely characterized, it is possible to accurately identify whether the target curve is an S-shaped curve.

[0057] When the target curve is identified as an S-shaped curve, the discrete point set of the S-shaped curve and the current motion state data of the vehicle are input into the preconfigured model predictive control algorithm to output a target driving trajectory that minimizes the sum of the steering angles of the vehicle when driving in the lane of the S-shaped curve, wherein the constraints of the model predictive control algorithm include that the single steering angle of the vehicle in the lane of the S-shaped curve is lower than the set angle threshold, and the driving trajectory of the vehicle cannot coincide with the lane boundary. When the vehicle reaches the starting point of the lane of the S-shaped curve, the vehicle is controlled to drive in the lane of the S-shaped curve based on the target driving trajectory; since the sum of the turning angles of the vehicle driving along the target driving trajectory is small, the lateral swing of the vehicle can be small, and the driving stability is high, which further improves the driving safety and the driving experience of the user.

[0058] See also Figure 3 , the embodiment of the present application also provides a driving trajectory control device for a vehicle in an S-shaped curve, which is configured on a vehicle. It should be noted that the basic principle and technical effects of the driving trajectory control device for a vehicle in an S-shaped curve provided in the embodiment of the present application are the same as those in the above embodiment. For the sake of brief description, for the parts not mentioned in the embodiment of the present application, reference can be made to the corresponding contents in the above embodiment. Figure 3 As shown, the device provided in the embodiment of the present application includes a data acquisition unit, a curve fitting unit, a curve identification unit, and a trajectory output unit, wherein:

[0059] The data acquisition unit is used to acquire a discrete point set of the lane boundary in front of the vehicle.

[0060] The curve fitting unit is used to fit the discrete point set into a target curve, wherein the target curve is an N-order polynomial, wherein N≥5.

[0061] The curve recognition unit is used to recognize whether the target curve is an S-shaped curve.

[0062] The trajectory output unit is used to input the discrete point set of the S-shaped curve and the current motion state data of the vehicle into a preconfigured model predictive control algorithm when the target curve is identified as an S-shaped curve, so as to output a target driving trajectory that minimizes the sum of the steering angles of the vehicle when driving in the lane of the S-shaped curve, wherein the constraints of the model predictive control algorithm include that the single steering angle of the vehicle in the lane of the S-shaped curve is lower than a set angle threshold, and the driving trajectory of the vehicle cannot coincide with the lane boundary.

[0063] A driving control unit is used to control the vehicle to drive in the lane of the S-shaped curve based on a target driving trajectory when the vehicle reaches the starting point of the lane of the S-shaped curve.

[0064] In some embodiments, the data acquisition unit is specifically used to determine whether the vehicle is loaded with a high-precision map of a preset distance ahead; if a high-precision map is loaded, a set of coordinate points of the lane boundary in front of the vehicle is extracted from the high-precision map as a set of discrete points.

[0065] In other embodiments, the data acquisition unit is further specifically used to determine whether the vehicle is loaded with a high-precision map of a preset distance ahead; if the high-precision map is not loaded, then an image of the road ahead is collected; a lane image is segmented from the image of the road ahead, and a set of pixel points of the lane boundary is extracted from the lane image as a set of discrete points.

[0066] In some embodiments, the curve recognition unit is specifically used to detect the curvature of each discrete point of the target curve; when the number of times the curvature reverses its positive and negative signs is greater than or equal to 2 times, and the distance extended by each group of discrete points of the curvature with the same sign is greater than a set distance threshold, the target curve is determined to be an S-shaped curve.

[0067] In some other embodiments, the curve recognition unit is further specifically configured to input the target curve into a pre-trained S-shaped curve recognition model to identify whether the target curve is an S-shaped curve.

[0068] The S-shaped curve recognition model is obtained by inputting multiple training samples into a neural network for training, each training sample includes a historical target curve, and the historical target curve is marked with a label indicating whether it is an S-shaped curve.

[0069] In some embodiments, when N=5, the expression of the N-order polynomial is y(x)=a5x 5 +a4x 4 +a3x 3 +a2x 2 +a1x, and satisfies y(x0) ’ =0,y(x n ) ’ =0, where y(x0)’ is the slope of the starting point of the target curve, y(x n ) ’ is the slope of the end point of the target curve, [x, y(x)] is the coordinate of the discrete point, and a1, a2, a3, a4, and a5 are different fitting coefficients.

[0070] In some implementations, the constraints of the model predictive control algorithm further include:

[0071] The speed of the vehicle is within a preset speed threshold range, and the acceleration of the vehicle is within a preset acceleration threshold range.

[0072] In addition, an embodiment of the present application provides a vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the vehicle executes the method provided in the above embodiment of the present application.

[0073] In addition, an embodiment of the present application further provides a storage medium, which stores a computer program. When the computer program is executed by a processor, the vehicle executes the method provided in the above embodiment of the present application.

[0074] In addition, an embodiment of the present application also provides a computer program product, including a computer program, which, when executed, enables a vehicle to execute a method as provided in the above embodiment of the present application.

[0075] In the above description, the technical details such as the patterning of each layer are not described in detail. However, those skilled in the art should understand that various technical means can be used to form layers, regions, etc. of desired shapes. In addition, in order to form the same structure, those skilled in the art can also design methods that are not completely the same as the methods described above. In addition, although the various embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage.

[0076] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0077] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for controlling the driving trajectory of a vehicle in an S-shaped curve, characterized in that: Applied to a vehicle, the method comprises: Obtaining a discrete point set of a lane boundary in front of the vehicle; Fitting the discrete point set into a target curve, wherein the target curve is an N-order polynomial, N≥5; Identify whether the target curve is an S-shaped curve; In the case where the target curve is identified as an S-shaped curve, a discrete point set of the S-shaped curve and the current motion state data of the vehicle are input into a preconfigured model predictive control algorithm to output a target driving trajectory that minimizes the sum of the steering angles of the vehicle when driving in the lane of the S-shaped curve, wherein the constraints of the model predictive control algorithm include that a single steering angle of the vehicle driving in the lane of the S-shaped curve is lower than a set angle threshold, and the driving trajectory of the vehicle cannot overlap with the lane boundary; When the vehicle reaches the start point of the lane of the S-shaped curve, the vehicle is controlled to travel in the lane of the S-shaped curve based on the target travel trajectory.

2. The method according to claim 1, characterized in that The step of obtaining a discrete point set of a lane boundary in front of the vehicle includes: Determining whether the vehicle is loaded with a high-precision map of a preset distance ahead; If the high-precision map is loaded, a set of coordinate points of the lane boundary in front of the vehicle is extracted from the high-precision map as the discrete point set.

3. The method according to claim 1, characterized in that The step of obtaining a discrete point set of a lane boundary in front of the vehicle includes: Determining whether the vehicle is loaded with a high-precision map of a preset distance ahead; If the high-precision map is not loaded, collecting the road image ahead; A lane image is segmented from the front road image, and a set of pixel points at the lane boundary is extracted from the lane image as the discrete point set.

4. The method according to claim 1, characterized in that: The identifying whether the target curve is an S-shaped curve includes: Detecting the curvature of each discrete point of the target curve; When the number of times the curvature reverses its sign is greater than or equal to 2 times, and the distance extended by each group of discrete points of the curvature with the same sign is greater than a set distance threshold, the target curve is determined to be an S-shaped curve.

5. The method according to claim 1, characterized in that The identifying whether the target curve is an S-shaped curve includes: The target curve is input into a pre-trained S-shaped curve recognition model to identify whether the target curve is an S-shaped curve, wherein the S-shaped curve recognition model is trained by inputting multiple training samples into a neural network, each of the training samples includes a historical target curve, and the historical target curve is marked with a label of whether it is an S-shaped curve.

6. The method according to claim 1, characterized in that When N=5, the expression of the Nth-order polynomial is y(x)=a5x 5 +a4x 4 +a3x 3 +a2x 2 +a1x, and satisfies y(x0) ’ =0,y(x n ) ’ =0, where y(x0) ’ is the slope of the starting point of the target curve, y(x n ) ’ is the slope of the end point of the target curve, [x, y(x)] is the coordinate of the discrete point, and a1, a2, a3, a4, and a5 are different fitting coefficients.

7. The method according to any one of claims 1 to 6, characterized in that: The constraints of the model predictive control algorithm also include: The speed of the vehicle is within a preset speed threshold range, and the acceleration of the vehicle is within a preset acceleration threshold range.

8. A vehicle comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the vehicle is caused to perform the method according to any one of claims 1 to 7.

9. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the vehicle executes the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, which, when executed, causes a vehicle to execute the method according to any one of claims 1 to 7.

Images