Automobile train pose determination method and device, electronic equipment and storage medium

Abstract

Embodiments of the present disclosure disclose a pose determination method and device for a vehicle train, electronic equipment and a storage medium. The method comprises: obtaining a model-derived pose of each axle center at a next time point through a kinematic model, a control quantity of a towing vehicle at a current time point, and initial poses of each axle center at the current time point; determining an actual detected pose of each axle center at the next time point according to effective sensor data of the towing vehicle; constructing model constraint residuals, geometric constraint residuals and detection residuals according to the model-derived pose, a target estimated pose and the actual detected pose at the next time point; obtaining cumulative residuals; and obtaining the target estimated pose of each axle center at the next time point by solving the cumulative residuals with the minimum cumulative residuals as the target, and taking the target estimated pose as the initial pose at the next time point. The method optimizes the model-derived pose by constructing three kinds of residuals, reduces the cumulative error in pose estimation, and reduces the pose estimation cost.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology, and in particular to a method, apparatus, electronic device, and storage medium for determining the pose of a vehicle train. Background Technology

[0002] With the development of vehicle control technology, intelligent driving systems integrating functions such as autonomous driving and assisted driving have gradually matured and been put into practical applications in various vehicle trains and trailers / trailers. During the operation of such vehicles, the pose (position and heading angle) of the trailer is often used to assess the collision risk of the vehicle. Therefore, improving the estimation accuracy of the trailer pose is important for safe vehicle operation.

[0003] In existing technologies, the position and heading angle of each trailer are typically measured by installing sensors on each trailer in a truck train, or by predicting the position and heading angle using kinematic models. However, trailers require frequent loading and unloading during cargo transportation, and the presence of sensors is not conducive to large-scale deployment and maintenance. Furthermore, sensors increase the cost of system integration. In addition, model prediction methods are prone to cumulative errors, resulting in low pose accuracy. Summary of the Invention

[0004] To address or at least partially address the aforementioned technical problems, this disclosure provides a method, apparatus, electronic device, and storage medium for determining the pose of a vehicle train. This optimizes the deduced pose of the model, reduces accumulated errors, improves pose accuracy, and eliminates the need to install sensors on each vehicle, thus reducing costs.

[0005] In a first aspect, embodiments of this disclosure provide a method for determining the pose of a vehicle train, the method comprising:

[0006] Based on the kinematic model corresponding to the target vehicle train, the control quantity of the tractor in the target vehicle train at the current moment, and the initial pose of each axle in the target vehicle train at the current moment, the model derivation pose of each axle in the next moment is determined.

[0007] The actual detection pose of each axle at the next moment is determined based on the effective sensor data of the tractor.

[0008] Based on the model-derived pose, target-estimated pose, and actual detection pose of each axis at the next moment, model constraint residuals, geometric constraint residuals, and detection residuals are constructed, and the cumulative residuals are determined based on the model constraint residuals, the geometric constraint residuals, and the detection residuals.

[0009] The goal is to minimize the cumulative residual and solve for the objective to obtain the target estimated pose of each axis at the next time step. The target estimated pose is then used as the initial pose at the next time step.

[0010] In a second aspect, the embodiments of the present disclosure further provide a device for determining a pose of a train of vehicles, which comprises:

[0011] a model derivation module, configured to determine a model-derived pose of each axle center at a next time based on a kinematic model corresponding to a target train of vehicles, a control variable of a tractor in the target train of vehicles at a current time, and an initial pose of each axle center in the target train of vehicles at the current time;

[0012] a sensor detection module, configured to determine an actual detected pose of each axle center at the next time based on effective data of a sensor of the tractor;

[0013] a residual construction module, configured to construct a model constraint residual, a geometric constraint residual and a detection residual based on the model-derived pose of each axle center at the next time, a target estimated pose and the actual detected pose of each axle center at the next time, and to determine an accumulated residual according to the model constraint residual, the geometric constraint residual and the detection residual;

[0014] a target solving module, configured to solve the target based on a minimization of the accumulated residual, and to obtain the target estimated pose of each axle center at the next time, and to take the target estimated pose as the initial pose at the next time.

[0015] In a third aspect, the embodiments of the present disclosure further provide an electronic device, which comprises one or more processors, a storage device configured to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method for determining a pose of a train of vehicles.

[0016] In a fourth aspect, the embodiments of the present disclosure further provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for determining a pose of a train of vehicles is implemented.

[0017] The method for determining the pose of the automobile train provided in the embodiments of the present disclosure obtains the model-derived pose of each axle center at the next time point through the kinematic model, the control quantity of the towing vehicle at the current time point and the initial pose of each axle center at the current time point, and determines the actual detected pose of each axle center at the next time point according to the effective data of the sensor of the towing vehicle, so as to construct the model constraint residual error, the geometric constraint residual error and the detection residual error according to the model-derived pose, the target estimated pose and the actual detected pose at the next time point, and then obtain the cumulative residual error, and solve the target estimated pose of each axle center at the next time point by taking the minimization of the cumulative residual error as the target, and take the target estimated pose as the initial pose at the next time point. The method combines the pose detected by the sensor to construct three kinds of residual errors, optimizes the model-derived pose, reduces the cumulative error in the pose estimation, and does not need to install a sensor on each vehicle, thereby reducing the cost of pose estimation. BRIEF DESCRIPTION OF DRAWINGS

[0018] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent by describing in detail the embodiments thereof with reference to the attached drawings in which:

[0019] Figure 1 A flowchart of the method for determining the pose of the automobile train in the embodiments of the present disclosure is shown in FIG. 1;

[0020] Figure 2 A simplified schematic diagram of the turning motion of the trailer in the embodiments of the present disclosure is shown in FIG. 2;

[0021] Figure 3 A schematic diagram of the trailer pose detection of the target automobile train under the turning motion in the embodiments of the present disclosure is shown in FIG. 3;

[0022] Figure 4 A structural schematic diagram of the pose determination device of the automobile train in the embodiments of the present disclosure is shown in FIG. 4;

[0023] Figure 5 A structural schematic diagram of the electronic device in the embodiments of the present disclosure is shown in FIG. 5. DETAILED DESCRIPTION

[0024] Embodiments of the present disclosure will be described in more detail by referring to the attached drawings. Although certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein, but rather, these embodiments are provided to make the present disclosure more thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes, and are not intended to limit the scope of protection of the present disclosure.

[0025] It should be noted that the terms "first", "second", and the like in the present disclosure are only used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0026] The names of the messages or information exchanged between the plurality of devices in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0027] Before the method provided by the embodiments of the present disclosure is described in detail, the technical problems solved by the method will be described. In the prior art, by measuring the position and heading angle of the trailer through sensors (such as global positioning system, inertial measurement unit, angle sensor, etc.), the following problems exist: 1. The position and heading of the trailer are difficult to measure. Although the global positioning system can obtain the position and heading of the trailer, it has strict requirements on the use scene, and is often unable to be used in indoor scenes; 2. There is inconvenience in installing sensors on the trailer. On the one hand, the trailer needs to be frequently loaded and unloaded during cargo transportation, and the existence of sensors is not conducive to large-scale deployment and maintenance. On the other hand, the power supply and signal of the sensor are difficult to realize; 3. The sensor increases the cost of system integration. In order to ensure the accuracy of the trailer pose, the sensor needs to ensure a certain detection accuracy and anti-interference ability, which greatly increases the system cost.

[0028] However, the existing method of installing sensors only on the tractor, such as patent 1 (CN115096289A, full trailer train, trailer pose determination method, device, equipment and medium), installs sensors only on the tractor and then derives the pose of each section of the trailer by combining the detected pose of the tractor with the kinematic model. However, this method is prone to cumulative error, resulting in inaccurate pose.

[0029] Therefore, in view of the above problems, the embodiments of the present disclosure provide a trailer pose determination method. The model constraint residual error, the geometric constraint residual error and the detection residual error are considered to construct an optimized target to maximize the posterior probability distribution, thereby obtaining the optimal trailer pose. The actual detection pose detected by the sensor is used to estimate the target estimated pose, and the trailer pose is corrected, thereby improving the accuracy of the trailer pose and reducing the cumulative error.

[0030] Figure 1 A flowchart of a trailer pose determination method in the embodiments of the present disclosure. The method can be applied to determine the initial pose at the next time according to the initial pose at the current time, so as to facilitate trajectory planning and decision making according to the initial pose at the next time. The method can be executed by a trailer pose determination device of the automobile train. The device can be realized by software and / or hardware, and the device can be configured in an electronic device. As shown in FIG. 1, the method includes the following steps.Figure 1 As shown, the method can specifically include the following steps:

[0031] S110, based on the kinematic model corresponding to the target automobile train, the control amount of the tractor in the target automobile train at the current time, and the initial pose of each axle center in the target automobile train at the current time, determining the model-derived pose of each axle center at the next time.

[0032] The target automobile train can include a tractor and at least one trailer, and the trailer can be a full hitch type or a semi hitch type. In this embodiment, the kinematic model corresponding to the target automobile train can be obtained by modeling the motion of each axle center of the trailer, and the full hitch type trailer can be regarded as two connected semi hitch type trailers.

[0033] As an example, the kinematic model can be modeled for the axle motion of the full hitch type trailer, and the kinematic model can also be compatible with the semi hitch type trailer. Figure 2 Fig. 1 is a simplified schematic diagram of a turning motion of a trailer in the embodiment of the present disclosure. As shown in the figure, Figure 2 For the tractor, (x0, y0) in the figure is the global coordinates of the rear axle center of the tractor, θ0 is the heading angle of the tractor, L0 is the wheelbase of the tractor, β0 is the front wheel steering angle of the tractor, v0 is the speed of the rear axle center of the tractor, and ω0 is the rate of change of the heading angle of the tractor. Point H1 represents the position of the hitch between the tractor and the trailer, which represents the distance between the hitch pin and the rear axle center of the tractor. The rear axle center of the tractor in the target automobile train can be the 0th axle center of the target automobile train.

[0034] For the ith (i∈[1, 2N]) axle center of the trailer in the target automobile train, (x i , y i ) is the global coordinates of the front axle center or the rear axle center of the trailer, θ i is the heading angle of the front axle center or the rear axle center of the trailer, v i is the speed of the front axle center or the rear axle center of the trailer, and ω i is the rate of change of the heading angle of the front axle center or the rear axle center of the trailer. The hinge angle between the ith axle center and the (i-1)th axle center of the trailer can be represented as β i = θ i - θ i-1 . If the ith axle center is the front axle center of the full hitch type trailer or the rear axle center of the semi hitch type trailer, L i is the distance from the front axle center of the trailer to the hitch pin of the previous section of the trailer, otherwise L i is the wheelbase of the trailer.

[0035] In the embodiment, the control quantity of the tractor can be composed of the rate of change of the heading angle of the tractor and the speed, for example, the control quantity of the tractor is defined as u0=[ω0, v0].

[0036] Specifically, the model-derived pose of each axle center at the next time can be determined according to the control quantity of the tractor at the current time and the initial pose of each axle center (for a full hitch type trailer, one trailer includes two axle centers, and for a semi hitch type trailer, one trailer includes one axle center) at the current time, in combination with the kinematic model. The initial pose of each axle center at the current time can be the target estimated pose at the current time derived by the method provided in the embodiment at the last time.

[0037] The kinematic model is described below. For the tractor, the kinematic equation can be expressed as:

[0038]

[0039] For the first trailer, the speed and the rate of change of the heading angle of the front axle center can be expressed as:

[0040]

[0041] The speed and the rate of change of the heading angle of the rear axle center can be expressed as:

[0042]

[0043] In summary, for the ith (i∈[1, 2N]) axle center, the speed and the rate of change of the heading angle can be expressed as:

[0044]

[0045] It should be noted that when the axle center is the rear axle center of the full hitch type trailer, The above formula can be written in matrix form as:

[0046]

[0047] which can be further simplified as:

[0048] u i =J(β i )u i-1 (6);

[0049] For the hinge angle between the front axle and the tractor, the rate of change is:

[0050]

[0051] For the hinge angle between the rear axle and the front axle, the rate of change is:

[0052]

[0053] In summary, for the i-th (i∈[1, 2N]) axle center, the change rate of the articulation angle with the i-1-th axle center can be expressed as:

[0054]

[0055] It should be noted that when the axle center is the rear axle center of the full-hanging type trailer, The above formula is written in matrix form as:

[0056]

[0057] Wherein,

[0058] Therefore, for the target vehicle train hanging with multiple trailers, the change rate of the articulation angle, the speed and the change rate of the heading angle of each axle center of each trailer can be recursively obtained according to the control amount of the towing vehicle at the current time, the vehicle body and the trailer parameters, combined with the above kinematic model, and on this basis, the pose [x i , y i , θ i ] of the i-th axle center is obtained by integration:

[0059]

[0060] The above formula (11) can be expanded as follows:

[0061]

[0062] In a specific example, based on the kinematic model corresponding to the target vehicle train, the control amount of the towing vehicle in the target vehicle train at the current time, and the initial pose of each axle center in the target vehicle train at the current time, the model-derived pose of each axle center at the next time is determined, including the following steps:

[0063] Step 11, obtaining the articulation angle of each axle center of each trailer in the target vehicle train at the last time and the control amount at the last time;

[0064] Step 12, based on the articulation angle of each axle center of each trailer at the last time, the control amount at the last time and the kinematic model, determining the change rate of the articulation angle of each axle center of each trailer at the last time, and determining the articulation angle of each axle center of each trailer at the current time according to the change rate of the articulation angle of each axle center of each trailer at the last time;

[0065] Step 13, obtaining the control amount of the tractor at the current time, determining the control amount of each axis of each drag bucket at the current time based on the control amount of the tractor at the current time, the hinge angles of each axis of each drag bucket at the current time, and the kinematic model;

[0066] Step 14, obtaining the initial pose of each axis at the current time, determining the model-derived pose of each axis at the next time based on the initial pose of each axis at the current time, the control amount of each axis at the current time, and the kinematic model.

[0067] Specifically, the hinge angles of each axis of each drag bucket at the previous time and the control amount of each axis of each drag bucket at the previous time can be obtained, and then substituted into the kinematic model (such as formula 10 above) to obtain the hinge angle change rate of each axis of each drag bucket at the previous time. For each axis of each drag bucket, the hinge angle change rate of the axis at the previous time can be calculated based on the hinge angle of the axis at the previous time and the control amount of the previous axis of the axis at the previous time. In this way, the hinge angle change rate of each axis of each drag bucket at the previous time is obtained in turn.

[0068] Further, the hinge angle change rate of each axis at the previous time can be integrated to obtain the hinge angle of each axis at the current time. The control amount of the tractor at the current time and the hinge angle of each axis of each drag bucket at the current time are substituted into the kinematic model (such as formula 5 above) to obtain the control amount of each axis of each drag bucket at the current time. Starting from the first axis of the first drag bucket, the control amount of the axis at the current time can be derived based on the hinge angle of the axis at the current time and the control amount of the tractor at the current time. The control amount of the second axis at the current time can be obtained based on the hinge angle of the second axis at the current time and the control amount of the first axis at the current time. In this way, the control amount of each axis of each drag bucket at the current time is recursively derived.

[0069] It should be noted that for a two-wheel differential control tractor, the heading angle change rate ω0 and the speed v0 at the center of the rear axle can be directly obtained. For a front-wheel steering rear-wheel drive tractor, the heading angle change rate cannot be directly obtained, and can be calculated by the wheelbase L0 of the tractor, the speed v0 of the tractor, and the front-wheel steering angle β0 of the tractor, combined with the Ackerman model.

[0070] Regarding step 13 above, optionally, obtaining the control quantity of the tractor at the current moment includes: obtaining the wheelbase of the tractor, the speed of the tractor at the current moment, and the front wheel deflection angle of the tractor at the current moment; determining the rate of change of the heading angle of the tractor at the current moment based on the wheelbase of the tractor, the speed of the tractor at the current moment, and the front wheel deflection angle of the tractor at the current moment; and determining the control quantity of the tractor at the current moment based on the speed of the tractor at the current moment and the rate of change of the heading angle of the tractor at the current moment.

[0071] The rate of change of the tractor's heading angle at the current moment is determined based on the tractor's wheelbase, its current speed, and its current front wheel deflection angle, and can satisfy the following formula:

[0072]

[0073] The meanings of the symbols in the formula can be found in the above explanation and will not be repeated here. After obtaining the rate of change of the tractor's heading angle at the current moment, it can be combined with the tractor's speed at the current moment to use the rate of change of the heading angle and speed as the control variables for the tractor at the current moment. Through this method, the accurate determination of the control variables for the tractor at the current moment is achieved, thereby ensuring the accuracy of the pose derivation from the model.

[0074] In this embodiment, after obtaining the control values ​​of each axis of each tow bucket at the current moment, the initial pose of each axis at the current moment can be combined with the kinematic model (as shown in Formula 12 above) to obtain the model-derived pose of each axis at the next moment. The pose can be composed of the global coordinates and heading angle of the axis. For each axis, the initial pose (x, y, y) of the axis at the current moment can be used as a reference. k y k θ k ), combined with the control quantity (ω) of the axis at the current moment. k v k The model derivation pose (x) of the axis at the next time step is calculated using the time interval T between the two time steps. k+1 y k+1 θ k+1 ).

[0075] By using steps 11-14 above, the hinge angle of each bucket at the current moment is determined by combining the kinematic model, and the control quantity of each axis at the current moment is deduced, thus realizing the accurate determination of the model derivation pose of each axis at the next moment.

[0076] S120. Determine the actual detection pose of each axle at the next moment based on the effective sensor data of the tractor.

[0077] In this embodiment, sensors are installed only on the tractor to detect the position and orientation of the trailer behind. These sensors can be lidar, cameras, or the like. Figure 3 This is a schematic diagram illustrating the trailer pose detection of a target vehicle train during turning motion, as shown in an embodiment of this disclosure. Figure 3 As shown, during the turning motion, the poses of the 1st, nth (n∈(1,N)), and Nth trailer (trailer) sections detected by the sensors on the tractor are valid, meaning that the rear axle center pose of the corresponding trailer can be obtained. Therefore, this valid pose information can be used to correct the poses of trailers where pose detection is invalid between sections 1-n and between sections n-N.

[0078] In this embodiment, assuming that the bucket motion is a rigid body motion (rotation and translation) on a two-dimensional plane, the pose of the i-th axis center can be represented by a two-dimensional Euclidean group (SE(2)):

[0079]

[0080] Assuming that the pose detection of the nth bucket section is valid at some point during the motion, the pose of the rear axle center of the nth bucket section (i.e., the 2nth axle center) can be represented as:

[0081]

[0082] Specifically, in this embodiment, the effective detection pose of a portion of the trailer can be obtained from the effective sensor data detected by the sensors installed on the tractor. The effective detection pose is used as the actual detection pose of that portion of the trailer, and then the actual detection pose of other shafts is derived based on the effective detection pose of that portion of the trailer, thereby obtaining the actual detection pose of each shaft at the next moment.

[0083] S130. Based on the model derivation pose, target estimation pose, and actual detection pose of each axis at the next moment, construct model constraint residuals, geometric constraint residuals, and detection residuals. Determine the cumulative residuals based on the model constraint residuals, geometric constraint residuals, and detection residuals.

[0084] Among them, model constraint residuals can be used to describe the difference between the model-derived pose and the target estimated pose, geometric constraint residuals can be used to describe the geometric residuals between adjacent vehicles under the target estimated pose, and detection residuals are used to describe the difference between the actual detected pose and the target estimated pose.

[0085] In one specific implementation, the model constraint residual, geometric constraint residual, and detection residual are constructed based on the model-derived pose, target-estimated pose, and actual detection pose of each axis at the next time step, including the following steps:

[0086] Step 21: Based on the difference between the model-derived pose of each axis at the next moment and the target estimated pose, construct the model constraint residual;

[0087] Step 22: Based on the x-coordinate, y-coordinate, and heading angle of the target estimated pose of the adjacent axes at the next moment, as well as the traction pin distance of the previous axis and the wheelbase of the next axis among the adjacent axes, construct the geometric constraint residual;

[0088] Step 23: Construct the detection residual based on the difference between the actual detection pose of each axis and the estimated pose of the target at the next moment.

[0089] Among them, the model constraint residual can be understood as the difference between the model-derived pose and the target-estimated pose at the same time.

[0090] Regarding step 21 above, optionally, based on the difference between the model-derived pose of each axis at the next time step and the target estimated pose, a model constraint residual is constructed, satisfying the following formula:

[0091]

[0092] in, Represents the model constraint residuals. Let represent the model-derived pose of the i-th axle center in the target vehicle train at the current moment. This represents the estimated pose of the i-th axle in the target vehicle train at the current moment.

[0093] In Equation 16 above, the two-dimensional Euclidean group describing the pose is logarithmically mapped to obtain the Lie algebra, which can be used to represent infinitesimal transformations of the Lie group, i.e., minute changes in pose. The Lie algebra can approximate pose differences without requiring complex matrix multiplication, thus improving the computational efficiency of model constraint residuals. By constructing model constraint residuals and combining them to solve for the estimated target pose, the gap between the estimated target pose and the model-derived pose can be minimized, thereby ensuring the accuracy of the estimated target pose.

[0094] The geometric constraint residual can be understood as the geometric residual between adjacent vehicles at the same moment under the target estimated pose of each axle center. Since the axles of the trailer can be regarded as rigidly connected, and the mechanical structure of the trailer itself remains unchanged, there is a geometric connection constraint between its front axle center and rear axle center. In this embodiment, G can be used. i-i+1 This represents the connection constraint between the i-th axis and the (i+1)-th axis of the bucket, such as G. 01 G represents the connection constraint between the rear axle center of the tractor and the front axle center of the first trailer. 12Let represent the connection constraint between the front axle center and the rear axle center of the first trailer section. Then, the geometric constraint residual connecting the tractor to the front axle center of the first trailer section can be expressed as:

[0095]

[0096] The geometric constraint residual connecting the front and rear axle centers of the first section of the trailer can be expressed as:

[0097]

[0098] Regarding step 22 above, optionally, based on the x-coordinate, y-coordinate, and heading angle of the target estimated pose of adjacent axes at the next moment, as well as the traction pin distance of the previous axis and the wheelbase of the next axis among adjacent axes, a geometric constraint residual is constructed, satisfying the following formula:

[0099] If the center of the preceding axle is located at the center of the rear axle of the vehicle, then the corresponding geometric constraint residual is:

[0100]

[0101] If the previous axle center is located at the center of the vehicle's front axle among adjacent axle centers, then the corresponding geometric constraint residual is:

[0102]

[0103] Among them, G i-i+1 This represents the connection constraint between the i-th axle center and the (i+1)-th axle center in the target vehicle train. X represents the geometric constraint residual between the i-th axis and the (i+1)-th axis. i X i+1 Let y and y represent the x-coordinates of the i-th axis and the (i+1)-th axis in the estimated pose of the target at the current time, respectively. i y i+1 Let θ represent the ordinates of the i-th axis and the (i+1)-th axis in the estimated pose of the target at the current time. i θ i+1 Let L represent the heading angles of the i-th axis and the (i+1)-th axis in the estimated pose of the target at the current time. i+1 This represents the axis distance of the (i+1)th axis. L1 represents the distance of the traction pin at the i-th axis, and L1 is the wheelbase at the 1-th axis.

[0104] That is, when calculating the geometric constraint residual connecting the i-th axle center and the (i+1)-th axle center, if the i-th axle center is the rear axle center of the vehicle, then the geometric constraint residual is constructed based on the above formula 19; if the i-th axle center is the front axle center of the vehicle, then the geometric constraint residual is constructed based on the above formula 20.

[0105] In the above optional implementation, geometric constraint residuals are constructed by connecting the axes, and then the target estimated pose is solved by combining the geometric constraint residuals. This ensures that the calculated target estimated pose conforms to the geometric connection constraints between the axes, thus guaranteeing the accuracy of the target estimated pose.

[0106] The detection residual can be understood as the difference between the estimated pose and the actual detected pose of the target at the same moment. For the tractor unit, since the initial value of the kinematic model integral of the target vehicle train comes from the sensor detection value, the detection residual of the tractor unit can be the same as the model constraint residual, and can be expressed as:

[0107]

[0108] For step 23 above, optionally, based on the difference between the actual detected pose of each axis at the next moment and the estimated pose of the target, a detection residual is constructed, satisfying the following formula:

[0109]

[0110] in, This represents the detection residual corresponding to the actual detection pose of the m-th axis relative to the tractor vehicle. This indicates the actual detected pose of the m-th axis relative to the tractor at the current moment. This indicates the estimated pose of the tractor unit at the current moment. This represents the estimated pose of the target at the current time for the m-th axis. This represents the relative pose of the target at the current moment after the estimated pose of the m-th axis is transformed to the tractor coordinate system; when the trailer in the target vehicle is a full trailer, m = 2n, and when the trailer in the target vehicle is a semi-trailer, m = n, where n is the index of the trailer.

[0111] That is, the detection residual of the rear axle center of each trailer relative to the actual detected pose of the tractor at the next moment can be calculated using the above formula 22. It should be noted that the actual detected pose of the axle center is the pose of the axle center relative to the tractor detected by the sensor, that is, the pose of the axle center in the tractor coordinate system, while the target estimated pose and the model derived pose are the poses in the world coordinate system. Therefore, the above formula 22 first transforms the target estimated pose from the world coordinate system to the tractor coordinate system, and then uses the difference between the estimated pose and the actual detected pose as the detection residual.

[0112] In the above optional implementation, by constructing the detection residual through the target estimated pose and detection pose of each axis, the difference between the solved target estimated pose and the detection pose can be minimized, thus ensuring the accuracy of the target estimated pose.

[0113] In this embodiment, in order to improve the accuracy of the target estimated pose for measuring the bucket, the above-mentioned multiple residuals are used to solve the target estimated pose.

[0114] Specifically, the model constraint residuals of all axes can be squared and summed, the geometric constraint residuals of all axes can be squared and summed, the target estimated poses of all axes can be squared and summed, and then all sums can be accumulated to construct the cumulative residual.

[0115] S140. With the goal of minimizing the cumulative residual, solve for the objective to obtain the target estimated pose of each axis at the next time step, and use the target estimated pose as the initial pose at the next time step.

[0116] With the goal of minimizing the cumulative residual, the following formula can be satisfied:

[0117]

[0118] Where, p ★ Let be the set of estimated target poses, including the estimated target poses of each axis at the next time step; let r be the set of various residuals; and let C(r) be the objective function, i.e., the cumulative residual. The cumulative residual is defined as follows:

[0119]

[0120] In the formula, ∑ M ∑ represents the sum of squares of the model constraint residuals. G Let ∑ represent the sum of squares of the geometric constraint residuals. F Let represent the sum of squares of the detection residuals, where i (i∈[0, 2N]) is the index of the axle of the trailer and the tractor, and n (n∈(1, N)) is the index of the trailer whose pose is valid as detected by the sensor. It should be noted that Formula 24 defines the cumulative residuals using a full-trailer type trailer as an example. For a semi-trailer type trailer, i.e., a semi-trailer truck train, the residuals in Formula 24 can be... Replace with m = n, where n is the index of the bucket.

[0121] Specifically, the above objectives can be solved to obtain the maximum a posteriori probability distribution estimate, yielding the estimated target pose for each axis at the next time step. This estimated pose can then be used as the initial pose for the next time step, facilitating the continued solution of the estimated target poses for subsequent time steps. The estimated target pose can be sent to the prediction module for collision detection or trajectory planning, or to the decision model for trajectory decision-making.

[0122] In this embodiment, in addition to considering the model constraint residual and geometric constraint residual, the above-mentioned objective also takes into account the effective pose of the detected bucket, and minimizes the difference between the detected pose and the estimated pose of the target, thereby correcting the estimated pose of the target.

[0123] The vehicle train pose determination method provided in this embodiment is highly versatile and compatible with semi-trailer and full-trailer types of trailers. Furthermore, it comprehensively considers the model constraint residuals, the geometric constraint residuals corresponding to the mechanical connection constraints, and the detection residuals of trailer detection. By optimizing the solution to maximize the posterior probability distribution and minimize the sum of squared residuals, the optimal pose estimation is obtained, thus obtaining the target estimated pose. This can reduce the cumulative error caused by using only model-derived pose. Compared with the method of using trailer pose detection, in addition to correcting the pose of the trailer within the visible range, it can also simultaneously correct the pose within the blind zone.

[0124] Furthermore, this method eliminates the need for additional sensors on the trailer, utilizing only sensors installed on the tractor to obtain the tractor speed, rate of change of heading angle, and trailer position within the rear visible range, thereby reducing system integration costs.

[0125] The vehicle train pose determination method provided in this embodiment obtains the model-derived pose of each axle at the next moment by using a kinematic model, the control quantity of the tractor at the current moment, and the initial pose of each axle at the current moment. Based on the effective sensor data of the tractor, the actual detected pose of each axle at the next moment is determined. Then, model constraint residuals, geometric constraint residuals, and detection residuals are constructed based on the model-derived pose, target estimated pose, and actual detected pose at the next moment. The cumulative residual is then obtained, and the solution is performed with the goal of minimizing the cumulative residual to obtain the target estimated pose of each axle at the next moment, which is then used as the initial pose at the next moment. This method optimizes the model-derived pose by combining the pose detected by sensors to construct three types of residuals, reducing the cumulative error in pose estimation. Furthermore, it eliminates the need to install sensors on each vehicle, thus reducing the cost of pose estimation.

[0126] Figure 4 This is a schematic diagram of the structure of a vehicle train posture determination device according to an embodiment of this disclosure. Figure 4As shown: The device includes: a model derivation module 410, a sensor detection module 420, a residual construction module 430, and an objective solution module 440, wherein:

[0127] The model derivation module 410 is used to determine the model derivation pose of each axle in the next moment based on the kinematic model corresponding to the target vehicle train, the control quantity of the tractor in the target vehicle train at the current moment, and the initial pose of each axle in the target vehicle train at the current moment.

[0128] The sensor detection module 420 is used to determine the actual detection pose of each axle at the next moment based on the effective sensor data of the tractor.

[0129] The residual construction module 430 is used to construct model constraint residuals, geometric constraint residuals, and detection residuals based on the model derivation pose, target estimated pose, and actual detection pose of each axis at the next time step, and to determine the cumulative residuals based on the model constraint residuals, the geometric constraint residuals, and the detection residuals.

[0130] The objective solving module 440 is used to solve the objective by minimizing the cumulative residual, to obtain the target estimated pose of each axis center at the next time moment, and to use the target estimated pose as the initial pose at the next time moment.

[0131] The vehicle pose determination device provided in this embodiment can execute the steps in the vehicle pose determination method provided in this embodiment, and has the execution steps and beneficial effects, which will not be repeated here.

[0132] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this disclosure. See below for details. Figure 5 It shows a schematic diagram of a structure suitable for implementing the electronic device 500 in the embodiments of this disclosure. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0133] like Figure 5 As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 501, which can perform various appropriate actions and processes to implement the methods of the embodiments described herein, based on a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device 500. The processing device 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0134] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts, thereby implementing the pose determination method for a vehicle train as described above. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0135] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0136] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the following steps:

[0137] Based on the kinematic model corresponding to the target vehicle train, the control quantity of the tractor in the target vehicle train at the current moment, and the initial pose of each axle in the target vehicle train at the current moment, the model derivation pose of each axle in the next moment is determined.

[0138] The actual detection pose of each axle at the next moment is determined based on the sensors on the tractor.

[0139] Based on the model-derived pose, target-estimated pose, and actual detection pose of each axis at the next moment, model constraint residuals, geometric constraint residuals, and detection residuals are constructed, and the cumulative residuals are determined based on the model constraint residuals, the geometric constraint residuals, and the detection residuals.

[0140] The goal is to minimize the cumulative residual and solve for the objective to obtain the target estimated pose of each axis at the next time step. The target estimated pose is then used as the initial pose at the next time step.

[0141] Optionally, when one or more of the above-described procedures are executed by the electronic device, the electronic device may also perform other steps described in the above embodiments.

[0142] 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 system, apparatus, 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, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, 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.

[0143] Option 1: A method for determining the pose of a car train, the method comprising:

[0144] Based on the kinematic model corresponding to the target vehicle train, the control quantity of the tractor in the target vehicle train at the current moment, and the initial pose of each axle in the target vehicle train at the current moment, the model derivation pose of each axle in the next moment is determined.

[0145] The actual detection pose of each axle at the next moment is determined based on the effective sensor data of the tractor.

[0146] Based on the model-derived pose, target-estimated pose, and actual detection pose of each axis at the next moment, model constraint residuals, geometric constraint residuals, and detection residuals are constructed, and the cumulative residuals are determined based on the model constraint residuals, the geometric constraint residuals, and the detection residuals.

[0147] The goal is to minimize the cumulative residual and solve for the objective to obtain the target estimated pose of each axis at the next time step. The target estimated pose is then used as the initial pose at the next time step.

[0148] Option 2, according to the method described in Option 1, the step of determining the model-derived pose of each axle in the next moment based on the kinematic model corresponding to the target vehicle train, the control quantity of the tractor in the target vehicle train at the current moment, and the initial pose of each axle in the target vehicle train at the current moment includes:

[0149] Obtain the hinge angle of each axle of each trailer in the target vehicle train at the previous moment and the control quantity at the previous moment;

[0150] Based on the hinge angle of each axis of each bucket at the previous moment, the control quantity at the previous moment, and the kinematic model, the hinge angle change rate of each axis of each bucket at the previous moment is determined, and the hinge angle of each axis of each bucket at the current moment is determined according to the hinge angle change rate of each axis of each bucket at the previous moment.

[0151] The control quantity of the tractor at the current moment is obtained, and based on the control quantity of the tractor at the current moment, the hinge angle of each axis of each trailer at the current moment, and the kinematic model, the control quantity of each axis of each trailer at the current moment is determined.

[0152] Obtain the initial pose of each axis at the current moment. Based on the initial pose of each axis at the current moment, the control quantity of each axis at the current moment, and the kinematic model, determine the model-derived pose of each axis at the next moment.

[0153] Option 3: According to the method described in Option 2, obtaining the control quantity of the tractor at the current moment includes:

[0154] Obtain the wheelbase of the tractor, the speed of the tractor at the current moment, and the front wheel deflection angle of the tractor at the current moment;

[0155] Based on the wheelbase of the tractor, the speed of the tractor at the current moment, and the front wheel deflection angle of the tractor at the current moment, determine the rate of change of the heading angle of the tractor at the current moment;

[0156] The control quantity of the tractor at the current moment is determined based on the tractor's speed at the current moment and the rate of change of its heading angle at the current moment.

[0157] Option 4: According to the method described in Option 1, the construction of model constraint residuals, geometric constraint residuals, and detection residuals based on the model derivation pose, target estimated pose, and actual detection pose of each axis at the next moment includes:

[0158] Based on the difference between the model-derived pose of each axis and the target-estimated pose at the next moment, model constraint residuals are constructed.

[0159] Based on the x-coordinate, y-coordinate, and heading angle of the target estimated pose of the adjacent axes at the next moment, as well as the traction pin distance of the previous axis and the wheelbase of the next axis among the adjacent axes, a geometrically constrained residual is constructed.

[0160] Based on the difference between the actual detection pose of each axis at the next moment and the estimated pose of the target, a detection residual is constructed.

[0161] Option 5: According to the method described in Option 4, the model constraint residual is constructed based on the difference between the model-derived pose of each axis center at the next moment and the target estimated pose, satisfying the following formula:

[0162]

[0163] in, Represents the model constraint residuals. Let represent the model-derived pose of the i-th axle center in the target vehicle train at the current moment. This represents the estimated pose of the i-th axle in the target vehicle train at the current moment.

[0164] Option 6: According to the method described in Option 4, the geometric constraint residual is constructed based on the x-coordinate, y-coordinate, and heading angle of the target estimated pose at the next moment of the adjacent axes, as well as the traction pin distance of the previous axis and the wheelbase of the next axis among the adjacent axes, satisfying the following formula:

[0165] If the center of the preceding axle is located at the center of the rear axle of the vehicle, then the corresponding geometric constraint residual is:

[0166]

[0167] If the previous axle center is located at the center of the vehicle's front axle among adjacent axle centers, then the corresponding geometric constraint residual is:

[0168]

[0169] Among them, G i-i+1 This represents the connection constraint between the i-th axle center and the (i+1)-th axle center in the target vehicle train. X represents the geometric constraint residual between the i-th axis and the (i+1)-th axis. i X i+1 Let y and y represent the x-coordinates of the i-th axis and the (i+1)-th axis in the estimated pose of the target at the current time, respectively. i y i+1 Let θ represent the ordinates of the i-th axis and the (i+1)-th axis in the estimated pose of the target at the current time. i θ i+1 Let L represent the heading angles of the i-th axis and the (i+1)-th axis in the estimated pose of the target at the current time. i+1 This represents the axis distance of the (i+1)th axis. L1 represents the distance of the traction pin at the i-th axis, and L1 is the wheelbase at the 1-th axis.

[0170] Option 7: According to the method described in Option 4, the detection residual is constructed based on the difference between the actual detected pose of each axis center at the next moment and the estimated pose of the target, satisfying the following formula:

[0171]

[0172] in, This represents the detection residual corresponding to the actual detection pose of the m-th axis relative to the tractor vehicle. This indicates the actual detected pose of the m-th axis relative to the tractor at the current moment. This indicates the estimated pose of the tractor unit at the current moment. This represents the estimated pose of the target at the current time for the m-th axis. This represents the relative pose of the target estimated pose at the current moment after transforming the m-th axis center to the tractor coordinate system; when the trailer in the target vehicle is a full trailer type, m = 2n, and when the trailer in the target vehicle is a semi trailer type, m = n, where n is the index of the trailer.

[0173] Option 8: A vehicle train position determination device, comprising:

[0174] The model derivation module is used to determine the model derivation pose of each axle in the next moment based on the kinematic model corresponding to the target vehicle train, the control quantity of the tractor in the target vehicle train at the current moment, and the initial pose of each axle in the target vehicle train at the current moment.

[0175] The sensor detection module is used to determine the actual detection pose of each axle at the next moment based on the effective sensor data of the tractor.

[0176] The residual construction module is used to construct model constraint residuals, geometric constraint residuals, and detection residuals based on the model derivation pose, target estimated pose, and actual detection pose of each axis at the next time step, and to determine the cumulative residuals based on the model constraint residuals, the geometric constraint residuals, and the detection residuals.

[0177] The objective solving module is used to minimize the cumulative residual and solve for the objective to obtain the target estimated pose of each axis center at the next time step, and use the target estimated pose as the initial pose at the next time step.

[0178] Option 9: An electronic device, the electronic device comprising:

[0179] One or more processors;

[0180] Storage device for storing one or more programs;

[0181] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of schemes 1-7.

[0182] Option 10: A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of Options 1-7.

[0183] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

Claims

1. A method for determining the pose of a car train, characterized in that, The method includes: Based on the kinematic model corresponding to the target vehicle train, the control quantity of the tractor in the target vehicle train at the current moment, and the initial pose of each axle in the target vehicle train at the current moment, the model derivation pose of each axle in the next moment is determined. The actual detection pose of each axle at the next moment is determined based on the effective sensor data of the tractor. Based on the difference between the model-derived pose of each axis and the target-estimated pose at the next moment, model constraint residuals are constructed. Based on the x-coordinate, y-coordinate, and heading angle of the target estimated pose of the adjacent axes at the next moment, as well as the traction pin distance of the previous axis and the wheelbase of the next axis among the adjacent axes, a geometrically constrained residual is constructed. Based on the difference between the actual detection pose of each axis in the next moment and the estimated pose of the target, a detection residual is constructed. The cumulative residual is determined based on the model constraint residual, the geometric constraint residual, and the detection residual; The goal is to minimize the cumulative residual and solve for the objective to obtain the target estimated pose of each axis at the next time step. The target estimated pose is then used as the initial pose at the next time step.

2. The method according to claim 1, characterized in that, The process of determining the model-derived pose of each axle in the next moment based on the kinematic model corresponding to the target vehicle train, the control quantity of the tractor in the target vehicle train at the current moment, and the initial pose of each axle in the target vehicle train at the current moment includes: Obtain the hinge angle of each axle of each trailer in the target vehicle train at the previous moment and the control quantity at the previous moment; Based on the hinge angle of each axis of each bucket at the previous moment, the control quantity at the previous moment, and the kinematic model, the hinge angle change rate of each axis of each bucket at the previous moment is determined, and the hinge angle of each axis of each bucket at the current moment is determined according to the hinge angle change rate of each axis of each bucket at the previous moment. The control quantity of the tractor at the current moment is obtained, and based on the control quantity of the tractor at the current moment, the hinge angle of each axis of each trailer at the current moment, and the kinematic model, the control quantity of each axis of each trailer at the current moment is determined. Obtain the initial pose of each axis at the current moment. Based on the initial pose of each axis at the current moment, the control quantity of each axis at the current moment, and the kinematic model, determine the model-derived pose of each axis at the next moment.

3. The method according to claim 2, characterized in that, The acquisition of the control quantity of the tractor at the current moment includes: Obtain the wheelbase of the tractor, the speed of the tractor at the current moment, and the front wheel deflection angle of the tractor at the current moment; Based on the wheelbase of the tractor, the speed of the tractor at the current moment, and the front wheel deflection angle of the tractor at the current moment, determine the rate of change of the heading angle of the tractor at the current moment; The control quantity of the tractor at the current moment is determined based on the tractor's speed at the current moment and the rate of change of its heading angle at the current moment.

4. The method according to claim 1, characterized in that, The model constraint residuals are constructed based on the difference between the model-derived pose of each axis center at the next time step and the target estimated pose, satisfying the following formula: ； in, Represents the model constraint residuals. Let represent the model-derived pose of the i-th axle center in the target vehicle train at the current moment. This represents the estimated pose of the i-th axle in the target vehicle train at the current moment.

5. The method according to claim 1, characterized in that, The geometrically constrained residuals are constructed based on the x-coordinate, y-coordinate, and heading angle of the target estimated pose at the next moment of the adjacent axes, as well as the traction pin distance of the previous axis and the wheelbase of the next axis among the adjacent axes, satisfying the following formula: If the center of the preceding axle is located at the center of the rear axle of the vehicle, then the corresponding geometric constraint residual is: ； If the previous axle center is located at the center of the vehicle's front axle among adjacent axle centers, then the corresponding geometric constraint residual is: ； in, This represents the connection constraint between the i-th axle center and the (i+1)-th axle center in the target vehicle train. This represents the geometric constraint residual between the i-th axis and the (i+1)-th axis. , Let x and y represent the x-coordinates of the i-th axis and the (i+1)-th axis in the estimated pose of the target at the current time, respectively. , Let represent the ordinates of the i-th axis and the (i+1)-th axis in the estimated pose of the target at the current time, respectively. , Let represent the heading angles of the i-th axis and the (i+1)-th axis in the estimated pose of the target at the current time, respectively. This represents the axis distance of the (i+1)th axis. This represents the distance of the traction pin at the i-th axis. The wheelbase is the distance from the first axis.

6. The method according to claim 1, characterized in that, The detection residual is constructed based on the difference between the actual detected pose of each axis center at the next moment and the estimated pose of the target, satisfying the following formula: ； in, This represents the detection residual corresponding to the actual detection pose of the m-th axis relative to the tractor vehicle. This indicates the actual detected pose of the m-th axis relative to the tractor at the current moment. This indicates the estimated pose of the tractor unit at the current moment. This represents the estimated pose of the target at the current time for the m-th axis. This represents the relative pose of the target estimated pose at the current moment after transforming the m-th axis center to the tractor coordinate system; when the trailer in the target vehicle train is a full trailer type, m=2n, and when the trailer in the target vehicle train is a semi-trailer type, m=n, where n is the index of the trailer.

7. A vehicle train position determination device, characterized in that, include: The model derivation module is used to determine the model derivation pose of each axle in the next moment based on the kinematic model corresponding to the target vehicle train, the control quantity of the tractor in the target vehicle train at the current moment, and the initial pose of each axle in the target vehicle train at the current moment. The sensor detection module is used to determine the actual detection pose of each axle at the next moment based on the effective sensor data of the tractor. The residual construction module is used to construct model constraint residuals based on the difference between the model-derived pose and the target estimated pose of each axis at the next time step; construct geometric constraint residuals based on the x-coordinate, y-coordinate, and heading angle of the target estimated pose of adjacent axes at the next time step, as well as the traction pin distance of the previous axis and the wheelbase of the next axis in adjacent axes; construct detection residuals based on the difference between the actual detected pose and the target estimated pose of each axis at the next time step; and determine the cumulative residuals based on the model constraint residuals, the geometric constraint residuals, and the detection residuals. The objective solving module is used to minimize the cumulative residual and solve for the objective to obtain the target estimated pose of each axis center at the next time step, and use the target estimated pose as the initial pose at the next time step.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

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