A vehicle articulation angle measurement method, device, vehicle and medium

Abstract

The embodiment of the application provides a kind of vehicle articulation angle measurement method, equipment vehicle and medium, it is related to vehicle technical field.The method comprises: obtaining the first angle parameter of articulation angle at last time and vehicle configuration parameter, and the original point cloud data is collected by the laser radar of tractor;According to the first angle parameter at last time and vehicle configuration parameter, determine the region of interest, and select target point cloud data from original point cloud data based on the region of interest, obtain the second angle parameter of articulation angle at current time;The final angle parameter of articulation angle at current time is obtained by Kalman filtering algorithm.The point cloud data is collected by laser radar in the application, the angle parameter at last time is involved in the determination of region of interest and measurement angle, and then Kalman filtering algorithm is used to obtain the final angle of articulation angle, so that the measurement mode of articulation angle is more stable, accurate, and easy to implement.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and more specifically to a method, equipment, vehicle, and medium for measuring the articulation angle of a vehicle. Background Technology

[0002] When an articulated vehicle moves forward, an angle is formed between the tractor and semi-trailer due to the articulation. For example, when a heavy truck turns, the tractor turns first, and then the articulation pulls the semi-trailer to turn, thus forming an angle between the tractor and semi-trailer, commonly referred to as the articulation angle. There are three existing methods for measuring the articulation angle: using angle sensors, magnetic sensors, and lidar. Using angle sensors requires installing additional mechanical structures at the drawbar connecting the tractor and semi-trailer, which can affect the existing mechanical structure and pose safety risks to the heavy truck. Using magnetic sensors requires installing magnetic field transmitting and receiving equipment on the heavy truck; however, the magnetic field has poor anti-interference capabilities, leading to inaccuracies. Lidar uses lidar to acquire the relative position data between the tractor and semi-trailer, and calculates the relative angle between them using data analysis techniques as an estimate of the articulation angle. However, due to the large data range of lidar measurements, different vehicle models, and diverse environments, the radar is susceptible to complex influences, resulting in noise in the acquired point cloud data, posing a challenge to the accurate estimation of the articulation angle.

[0003] Based on the aforementioned technical problems, the applicant has proposed the technical solution of this application. Summary of the Invention

[0004] The purpose of this invention is to provide a method, equipment, vehicle, and medium for measuring the articulation angle of a vehicle. It utilizes lidar to collect point cloud data, incorporates the angle parameters from the previous moment into the determination of the region of interest and the measurement angle, and then uses a Kalman filter algorithm to obtain the final articulation angle. This makes the articulation angle measurement method more stable, accurate, and easy to promote and implement.

[0005] To achieve the above objectives, the present invention provides a method for measuring the articulation angle of a vehicle, wherein the vehicle includes a tractor unit and a semi-trailer articulated with the tractor unit, and the articulation angle is the angle between the tractor unit and the semi-trailer; the method includes:

[0006] Obtain the first angle parameter and vehicle configuration parameters of the articulation angle at the previous moment, and collect raw point cloud data through the lidar of the tractor vehicle;

[0007] The region of interest is determined based on the first angle parameter at the previous moment and the vehicle configuration parameters, and target point cloud data is selected from the original point cloud data based on the region of interest.

[0008] Based on the target point cloud data, the second angle parameter of the hinge angle at the current moment is obtained;

[0009] Based on the first angle parameter at the previous moment and the second angle parameter at the current moment, the final angle parameter of the hinge angle at the current moment is obtained by using the Kalman filter algorithm.

[0010] The present invention provides a measuring device for a vehicle hinge angle, the measuring device being used to perform the above-described method for measuring the vehicle hinge angle.

[0011] The present invention provides a vehicle including the above-described vehicle articulation angle measuring device.

[0012] The present invention also provides a non-transitory computer-readable storage medium storing processor-executable instructions configured to perform the above-described method for measuring the vehicle articulation angle.

[0013] In one embodiment, determining the region of interest based on the first angle parameter at the previous moment and the vehicle configuration parameters, and selecting target point cloud data from the raw point cloud data based on the region of interest, includes:

[0014] Using a preset point as the center and a circle with a radius of a set distance from the preset point as the boundary, a coarse screening area for the point cloud is determined, and coarse screening point cloud data falling into the coarse screening area for the point cloud is extracted.

[0015] If the first angle parameter is obtained, the first angle range is determined, and the first angle range is divided to obtain multiple hypothetical angles;

[0016] If the first angle parameter cannot be obtained, the preset second angle range is divided to obtain the plurality of hypothetical angles;

[0017] Multiple regions of interest are determined based on the multiple assumed angles and the vehicle configuration parameters, and the coarse-screened point cloud data falling into each region of interest are counted. The target region of interest is selected based on the counting results.

[0018] The coarse point cloud data that falls within the target region of interest is used as the target point cloud data.

[0019] In one embodiment, determining multiple regions of interest based on the multiple assumed angles and the vehicle configuration parameters, counting the coarse-screened point cloud data falling into each region of interest, and selecting a target region of interest based on the counting results includes:

[0020] For each assumed angle, multiple boundary corner points are determined based on the assumed angle and the vehicle configuration parameters, and the multiple boundary corner points are combined to form a region of interest corresponding to the assumed angle;

[0021] The number of points in the coarse-screened point cloud data that fall into the plurality of regions of interest is calculated respectively, and the plurality of point counts are sorted.

[0022] The region of interest corresponding to the largest number of point clouds is selected as the target region of interest.

[0023] In one embodiment, the region of interest is a rectangular region, and the coordinates of the four boundary corner points of the rectangular region are obtained in the following way:

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] Where, x C y C This represents the coordinates of a corner point C on the boundary of the rectangular region; x D y D This represents the coordinates of a corner point D of the rectangular region, x. E y E This represents the coordinates of a corner point E of the rectangular region, x. F y F R represents the coordinates of a corner point F of the rectangular region; R represents the rotation matrix; α represents an assumed angle; d w d represents half the lateral width of the container loaded on the semi-trailer. o d represents the longitudinal distance in the direction of vehicle travel between the front panel of the container and the articulation point on the semi-trailer. B δ represents the longitudinal distance between the lidar and the hinge point, and δ represents the fault tolerance length.

[0030] In one embodiment, the initial angle parameters of the hinge angle include an initial hinge angle and an initial hinge angular velocity, the predicted angle parameters include a predicted hinge angle and a predicted hinge angular velocity, and the final angle parameters include a final hinge angle and a final hinge angular velocity.

[0031] In one embodiment, the first angle range is [α1-π / 8, α1+π / 8], where α1 represents the first angle parameter, and the first angle range is divided with a preset radian as the step size.

[0032] In one embodiment, the second angle range is [-π / 2, π / 2], and the division of the second angle range is performed with a preset radian as the step size. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the hardware configuration of a vehicle according to the first embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram showing the position of the articulation angle between the tractor and the trailer according to the first embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram showing the location of the region of interest of a vehicle during driving, according to the first embodiment of the present invention;

[0036] Figure 4 This is a detailed flowchart of the method for measuring the vehicle articulation angle according to the first embodiment of the present invention;

[0037] Figure 5 This is a schematic flowchart of step 103 according to the first embodiment of the present invention;

[0038] Figure 6 This is a schematic diagram of the four boundary corner points of the region of interest according to the first embodiment of the present invention. Detailed Implementation

[0039] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings to provide a clearer understanding of the purpose, features, and advantages of the present invention. It should be understood that the embodiments shown in the drawings are not intended to limit the scope of the present invention, but are merely illustrative of the essential spirit of the technical solution of the present invention.

[0040] In the following description, certain specific details are set forth for the purpose of illustrating various disclosed embodiments in order to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the art will recognize that embodiments may be practiced without one or more of these specific details. In other instances, well-known apparatuses, structures, and techniques associated with this application may not have been shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0041] Unless the context requires otherwise, throughout the specification and claims, the word “comprising” and its variations, such as “including” and “having”, shall be understood to have an open, inclusive meaning, that is, to be interpreted as “including, but not limited to”.

[0042] Throughout this specification, references to "an embodiment" or "an embodiment" indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Therefore, the appearance of "in an embodiment" or "an embodiment" in various places throughout the specification does not necessarily refer to the same embodiment. Furthermore, a particular feature, structure, or characteristic may be combined in any manner in one or more embodiments.

[0043] The singular forms “a” and “the” used in this specification and the appended claims include plural references unless otherwise expressly stated herein. It should be noted that the term “or” is generally used to include the meaning of “or / and” unless otherwise expressly stated herein.

[0044] In the following description, in order to clearly demonstrate the structure and working method of the present invention, a number of directional terms will be used. However, terms such as "front", "back", "left", "right", "outside", "inside", "outward", "inward", "up", and "down" should be understood as convenient terms and not as limiting terms.

[0045] The first embodiment of this invention relates to a method for measuring the articulation angle of a vehicle, applied to a vehicle articulation angle measuring device, such as... Figure 1 As shown, the vehicle includes a tractor unit 1 and a semi-trailer 2 articulated with the tractor unit 1 at an articulation point, specifically as follows: Figure 2 As shown, the hinge angle is the angle alpha between the tractor 1 and the semi-trailer 2, where point C represents the hinge point, which is an abstract point of the connection structure between the tractor and the semi-trailer; point B represents the center position of the rear panel of the tractor 1; and point A represents the center position of the front panel of the tractor 1. A lidar 4 is installed on the rear panel of the tractor 1, and the lidar 4 is a lidar device that performs single-line scanning facing the front panel of the container loaded on the semi-trailer 2. The tractor 1 is equipped with a controller that can read the data from the lidar 4, and the controller also runs an angle calculation program to calculate the hinge angle.

[0046] like Figure 4 The diagram shown is a flowchart illustrating the specific process for measuring the vehicle articulation angle in this embodiment.

[0047] Step 101: Obtain the angle parameters and vehicle configuration parameters from the previous moment.

[0048] Specifically, the system obtains the first angle parameter of the hinge angle at the previous moment and the vehicle configuration parameters. Since the angle calculation program running on the controller is cyclical, when it runs for the first time, it initializes the first angle parameter from the previous moment, for example, by setting it to a preset angle parameter. After the angle calculation program has run, it obtains the first angle parameter of the vehicle at the previous moment and directly uses it. The vehicle configuration parameters can be read by the controller or written to the controller after actual measurement. In this embodiment, the vehicle configuration parameters include the fixed physical dimensions between the towing vehicle 1 and the trailer 2, such as... Figure 3 As shown, for example, the longitudinal distance d between the front panel of the container and the articulation point on the semi-trailer in the vehicle's forward direction. o The longitudinal distance d between the lidar and the hinge point B Half the lateral width d of the container loaded on the semi-trailer w As the vehicle moves forward, the articulation angle between the tractor and the semi-trailer changes continuously, while the vehicle configuration parameters remain constant.

[0049] It should be noted that the first angle parameter includes the hinge angle value and the corresponding angular velocity value. All angle parameters mentioned in this embodiment include the angle value and the corresponding angular velocity value. The angular velocity value serves as an additional reference for the angle value, allowing the user to more accurately grasp the hinge angle. In the following description, the hinge angle can also be used to represent the angle parameter.

[0050] Step 102: Acquire a frame of point cloud data using LiDAR. If point cloud data is acquired, proceed to step 103; otherwise, proceed to step 108.

[0051] Step 108: Determine if point cloud data is lost for 3 consecutive frames. If yes, the process ends; otherwise, return to step 102.

[0052] Specifically, raw point cloud data is collected using a lidar system on the tractor unit. The lidar is mounted on the rear panel of the tractor unit and faces the front panel of the container loaded on the semi-trailer, performing a single-line scan. The lidar collects raw point cloud data frame by frame. First, it determines whether raw point cloud data can be acquired. If it can, the acquired raw point cloud data is used to determine the region of interest. If no raw point cloud data is acquired, the process proceeds to step 108, where it is determined whether three consecutive frames of point cloud data have been lost. During this determination, if any frame of raw point cloud data is acquired, the process returns to step 102 and proceeds to step 103 to determine the region of interest. The "three consecutive frames" is an example; this embodiment is not limited to three consecutive frames.

[0053] Step 103: Extract point cloud data within the region of interest.

[0054] Specifically, a region of interest (ROI) is determined based on the first angle parameter and the vehicle configuration parameters from the previous time step, and target point cloud data is selected from the original point cloud data based on the ROI. The first angle parameter and vehicle configuration parameters from the previous time step have already been obtained in step 101; the ROI is defined in this step.

[0055] In one example, step 103 includes the following sub-steps, specifically as follows: Figure 5 The diagram shown is a flowchart illustrating the specific steps of step 103.

[0056] Step 1031, coarse screening of point cloud.

[0057] Specifically, a coarse screening area for the point cloud is defined with a preset point as the center and a circle with a radius equal to a predetermined distance from the preset point as the boundary. Coarse screening point cloud data falling within this area is then extracted. In this embodiment, the hinge point is used as the preset point, i.e. Figure 3 Point B in the diagram is used as the center of a circle with a radius of 3m to define the circumference as the boundary. This boundary is used as the boundary of the coarse screening area of ​​the point cloud. Points falling into this coarse screening area are considered as coarse screening point cloud data.

[0058] Step 1032: Obtain the hinge angle at the previous moment. If the first angle parameter is obtained, proceed to step 1033. If the first angle parameter is not obtained, proceed to step 1034.

[0059] Step 1033: Determine the first angle range and divide the first angle range to obtain multiple hypothetical angles.

[0060] Step 1034: Divide the preset second angle range to obtain the plurality of assumed angles.

[0061] Specifically, after obtaining the first angle parameter α from the previous moment, based on the assumption that the hinge angle changes at a constant speed, the hinge angle at the current moment should be near the hinge angle from the previous moment. Therefore, the first angle range [α-π / 8, α+π / 8] is divided into smaller angles, for example, with a step size of 0.0174 rad. If the first angle parameter from the previous moment is not obtained, the second angle range [-π / 2, +π / 2] is divided into smaller angles, also with a step size of 0.0174 rad. In this way, multiple hypothetical angles can be obtained.

[0062] Step 1035: Generate the region of interest.

[0063] Step 1036: Select the case where the most points fall within the region of interest.

[0064] Step 1037: Output the selected point cloud as the point cloud of interest.

[0065] Specifically, multiple regions of interest are determined based on the multiple assumed angles and the vehicle configuration parameters, and the coarse-screened point cloud data falling into each region of interest are counted. Based on the counting results, a target region of interest is selected, and the coarse-screened point cloud data falling into the target region of interest is used as the target point cloud data.

[0066] First, for each assumed angle, multiple boundary corner points are determined based on the assumed angle and the vehicle configuration parameters, and the multiple boundary corner points are combined to form a region of interest corresponding to the assumed angle.

[0067] In one example, if the region of interest is a rectangular region, it is only necessary to determine the four boundary corners of the rectangular region to determine the region of interest.

[0068] The coordinates of the four boundary corner points of the rectangular region are obtained in the following way:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] Where, x C y C This represents the coordinates of a corner point C on the boundary of the rectangular region; x D y D This represents the coordinates of a corner point D of the rectangular region, x. E y E This represents the coordinates of a corner point E of the rectangular region, x. F y F R represents the coordinates of a corner point F of the rectangular region; R represents the rotation matrix; α represents an assumed angle; d w d represents half the lateral width of the container loaded on the semi-trailer. o d represents the longitudinal distance in the direction of vehicle travel between the front panel of the container and the articulation point on the semi-trailer. B δ represents the longitudinal distance between the lidar and the hinge point, and δ represents the fault tolerance length.

[0075] In the calculation of the region of interest, such as Figure 6 As shown in the figure, point A represents the far point of the lidar coordinate system, which follows the right-hand rule. Point B is the coordinate point of the hinge point in the lidar coordinate system. The base of the dashed triangle represents the projection of the container front panel onto the xy plane when the hinge angle is 0, and the base of the solid triangle represents the projection of the container front panel onto the xy plane when the hinge angle is α. Points C, D, E, and F in the figure constitute the four boundary corner points of the region of interest.

[0076] After determining the region of interest, the number of points in the coarse-screened point cloud data that fall into each of the multiple regions of interest is calculated, and the multiple point counts are sorted. Finally, the region of interest with the largest number of point cloud data is taken as the target region of interest.

[0077] Step 104: Linear fitting to estimate the angle measurement value.

[0078] Specifically, based on the target point cloud data in the region of interest, a second angle parameter of the hinge angle at the current moment is obtained. This second angle parameter can also be the measured angle value of the hinge angle.

[0079] In one example, the linear equation is obtained by fitting a linear equation using the least squares method. The specific equation is as follows:

[0080]

[0081] The second angle parameter at the current moment is calculated using the following formula:

[0082]

[0083] The second angle parameter is represented by α, and x1, y1, x2, and y2 are the coordinates of the target point cloud data in the region of interest on the x-axis and y-axis of the lidar coordinate system, respectively. x and y are the corresponding linear fitting values.

[0084] In one example, the second angle parameter can also be obtained by using the random sampling consensus method to solve the linear equation.

[0085] Step 105: Determine whether a reliable angle observation has been obtained. If yes, proceed to step 106; otherwise, return to step 102.

[0086] Specifically, the obtained second angle parameters are judged. If the deviation of the second angle parameters is too large, the angle observation is determined to be unreliable. For example, it is compared with the second angle parameters at the previous moment. If the deviation is within a certain range, the angle observation is considered reliable and can be used for the next step of calculation. If the deviation exceeds a certain range, the angle observation is considered unreliable and the second angle parameters need to be recalculated.

[0087] Step 106: Calculate the hinge angle using Kalman filtering.

[0088] Assume the hinge angle to be calculated is α. k|k It indicates that the subscript k represents the kth cycle, and the angular velocity is expressed as... Therefore, the state variable that needs to be estimated at time k can be expressed as:

[0089]

[0090] Therefore, the state vector of the previous moment can be represented as:

[0091]

[0092] The state covariance matrix at the previous time step is P k-1|k-1 The initial covariance matrix can be set manually.

[0093] In this example, a uniformly changing matrix is ​​used as the system state transition matrix, where T is the time constant, which is 0.1 seconds in this case:

[0094]

[0095] The angle measurement obtained using linear fitting at the current moment can be represented as z. k The measurement matrix is ​​as follows:

[0096] H k =[1 0]

[0097] The system sets the noise matrix for the prediction process to Q. k The measurement noise matrix is ​​R k .

[0098] The prediction process formula is as follows:

[0099] x k|k-1 =F k x k-1|k-1

[0100] P k|k-1 =F k P k-1|k-1 F k +Q k

[0101] The update process formula is:

[0102] y k =z k -H k x k|k-1

[0103] S k =H k P k|k-1 H k +R k

[0104]

[0105] x k|k =x k|k-1 +K k y k

[0106] P k|k =(IK k H k )P k|k-1

[0107] Based on the above formula, we can determine the state x from the previous moment. k-1|k-1 Covariance matrix P k-1|k-1 and the measured value z k Find the current state value (hinged angle and angular velocity).

[0108] Step 107: Determine whether to continue executing the angle calculation program. If yes, return to step 102 and start calculating the angle for the next moment. If no, the process ends.

[0109] Specifically, the angle calculation program can be executed repeatedly in a loop. For the angle calculation of the next moment, the angle calculated this time is used as the angle parameter of the previous moment mentioned in step 101 and participates in step 102 and subsequent processes. Due to the continuity of vehicle driving, the hinge angle output by the angle calculation program in each loop is the angle given for the current vehicle driving situation, and it can continuously output the hinge angle.

[0110] The second embodiment of the present invention relates to a vehicle hinge angle measuring device, which is used to perform the vehicle hinge angle measuring method described above, that is, to perform the vehicle hinge angle measuring method in the first embodiment.

[0111] A third embodiment of the present invention relates to a vehicle, including the vehicle articulation angle measuring device of the second embodiment.

[0112] The fourth embodiment of the present invention relates to a non-transitory computer-readable storage medium storing processor-executable instructions configured to perform the vehicle articulation angle measurement method of the first embodiment.

[0113] Since the first embodiment corresponds to the second, third, and fourth embodiments, the second, third, and fourth embodiments can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment remain valid in the second, third, and fourth embodiments, and the technical effects achievable in the first embodiment can also be achieved in the second, third, and fourth embodiments. To reduce repetition, these details will not be elaborated here. Correspondingly, the relevant technical details mentioned in the second, third, and fourth embodiments can also be applied to the first embodiment.

[0114] The preferred embodiments of the present invention have been described in detail above, but it should be understood that, if necessary, aspects of the embodiments can be modified to utilize aspects, features, and concepts from various patents, applications, and publications to provide other embodiments.

[0115] In light of the detailed description above, these and other changes can be made to the embodiments. Generally, the terminology used in the claims should not be considered limited to the specific embodiments disclosed in the specification and claims, but should be understood to include all possible embodiments together with the full scope of equivalents enjoyed by these claims.

Claims

1. A method for measuring the articulation angle of a vehicle, characterized in that, The vehicle includes a tractor unit and a semi-trailer articulated with the tractor unit, the articulation angle being the angle between the tractor unit and the semi-trailer; the method includes: Obtain the first angle parameter and vehicle configuration parameters of the articulation angle at the previous moment, and collect raw point cloud data through the lidar of the tractor vehicle; The region of interest is determined based on the first angle parameter at the previous moment and the vehicle configuration parameters, and target point cloud data is selected from the original point cloud data based on the region of interest. Based on the target point cloud data, the second angle parameter of the hinge angle at the current moment is obtained; Based on the first angle parameter at the previous moment and the second angle parameter at the current moment, the final angle parameter of the hinge angle at the current moment is obtained by the Kalman filter algorithm. The step of determining the region of interest based on the first angle parameter at the previous moment and the vehicle configuration parameters, and selecting target point cloud data from the original point cloud data based on the region of interest, includes: Using a preset point as the center and a circle with a radius of a set distance from the preset point as the boundary, a coarse screening area for the point cloud is determined, and coarse screening point cloud data falling into the coarse screening area for the point cloud is extracted. If the first angle parameter is obtained, the first angle range is determined, and the first angle range is divided to obtain multiple hypothetical angles; If the first angle parameter cannot be obtained, the preset second angle range is divided to obtain the plurality of hypothetical angles; Multiple regions of interest are determined based on the multiple assumed angles and the vehicle configuration parameters, and the coarse-screened point cloud data falling into each region of interest are counted. The target region of interest is selected based on the counting results. The coarse point cloud data that falls within the target region of interest is used as the target point cloud data.

2. The method for measuring the vehicle articulation angle according to claim 1, characterized in that, The process of determining multiple regions of interest (ROIs) based on the multiple assumed angles and the vehicle configuration parameters, counting the coarsely screened point cloud data falling into each ROI, and selecting a target ROI based on the counting results includes: For each assumed angle, multiple boundary corner points are determined based on the assumed angle and the vehicle configuration parameters, and the multiple boundary corner points are combined to form a region of interest corresponding to the assumed angle; The number of points in the coarse-screened point cloud data that fall into the plurality of regions of interest is calculated respectively, and the plurality of point counts are sorted. The region of interest corresponding to the largest number of point clouds is selected as the target region of interest.

3. The method for measuring the vehicle articulation angle according to claim 2, characterized in that, The region of interest is a rectangular region, and the coordinates of the four boundary corner points of the rectangular region are obtained in the following way: in, , This represents the coordinates of a corner point C on the boundary of the rectangular region; , This represents the coordinates of point D, a boundary corner point of the rectangular region. , This represents the coordinates of point E, a boundary corner point of the rectangular region. , R represents the coordinates of a corner point F of the rectangular region; R represents the rotation matrix. To indicate a hypothetical angle, This indicates half the lateral width of the container loaded on the semi-trailer. This indicates the longitudinal distance in the direction of vehicle travel between the front panel of the container and the articulation point on the semi-trailer. This indicates the longitudinal distance between the lidar and the hinge point. Indicates the fault tolerance length.

4. The method for measuring the vehicle articulation angle according to claim 1, characterized in that, The initial angle parameters of the hinge angle include the initial hinge angle and the initial hinge angular velocity; the second angle parameters include the hinge angle and the hinge angular velocity; and the final angle parameters include the final hinge angle and the final hinge angular velocity.

5. The method for measuring the vehicle articulation angle according to claim 1, characterized in that, The first angle range is [α1-π / 8, α1+π / 8], where α1 represents the first angle parameter, and the first angle range is divided with a preset radian as the step size.

6. The method for measuring the vehicle articulation angle according to claim 1, characterized in that, The second angle range is [-π / 2, π / 2], and the division of the second angle range is performed with a preset radian step size.

7. A measuring device for the articulation angle of a vehicle, characterized in that, The measuring device is used to perform the method for measuring the vehicle articulation angle according to any one of claims 1 to 6.

8. A vehicle, characterized in that, Includes the vehicle hinge angle measuring device as described in claim 7.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores processor-executable instructions configured to perform the method for measuring the vehicle articulation angle as described in any one of claims 1 to 6.

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