Mass center position determination method and device, medium, controller, vehicle and product

Abstract

The invention relates to a mass center position determination method and device, a medium, a controller, a vehicle and a product. The method comprises the steps that candidate mass center positions determined through at least two function systems of the vehicle are obtained; and fusion processing is performed based on the candidate centroid positions to obtain the centroid position of the vehicle, so that the accuracy of the centroid position of the vehicle is improved.

Description

Technical Field

[0001] This application relates to the technical field of vehicles, and particularly to a method, device, medium, controller, vehicle, and product for determining the centroid position. Background Art

[0002] With the development of science and technology, the functions on vehicles are becoming increasingly rich. For example, vehicles have a side shift function, which can enable the vehicle to rotate, turn around, or turn.

[0003] During the process of the vehicle executing the side shift function, it is necessary to determine the centroid position of the vehicle. However, the accuracy of the currently determined centroid position of the vehicle needs to be improved. Summary of the Invention

[0004] Embodiments of this application provide a method for determining the centroid position, which improves the accuracy of the centroid position of the vehicle to at least partially solve the above technical problems.

[0005] To achieve the above object, according to the first aspect of this application, there is provided a method for determining the centroid position, including:

[0006] Obtain candidate centroid positions respectively determined by at least two functional systems of the vehicle;

[0007] Perform fusion processing based on the above candidate centroid positions to obtain the centroid position of the vehicle.

[0008] Optionally, the above performing fusion processing based on the above candidate centroid positions to obtain the centroid position of the vehicle includes:

[0009] Obtain the weights respectively corresponding to the at least two functional systems;

[0010] Perform fusion processing based on the above candidate centroid positions and the corresponding weights to obtain the centroid position of the vehicle.

[0011] Optionally, the above candidate centroid position is the position of the vehicle at the first moment, and the above performing fusion processing based on the above candidate centroid positions to obtain the centroid position of the vehicle includes:

[0012] Obtain the first Kalman gain corresponding to the first moment;

[0013] Perform fusion processing based on the above candidate centroid position and the first Kalman gain to obtain the centroid position of the vehicle at the first moment.

[0014] Optionally, there are two of the above candidate centroid positions, and the above performing fusion processing based on the above candidate centroid positions and the first Kalman gain to obtain the centroid position of the vehicle at the first moment includes:

[0015] Determine the position difference between the above-mentioned candidate centroid positions;

[0016] Multiply the first Kalman gain corresponding to the above-mentioned first moment by the above-mentioned position difference to obtain a position adjustment parameter;

[0017] Based on the above-mentioned candidate centroid positions and the above-mentioned position adjustment parameter, determine the centroid position of the above-mentioned vehicle at the first moment.

[0018] Optionally, before the above-mentioned fusion process based on the above-mentioned candidate centroid positions to obtain the centroid position of the above-mentioned vehicle, it further includes:

[0019] Perform a correction process on the above-mentioned candidate centroid positions to obtain corrected candidate centroid positions.

[0020] Optionally, the above-mentioned process of performing a correction process on the above-mentioned candidate centroid positions to obtain corrected candidate centroid positions includes:

[0021] Obtain the second Kalman gain corresponding to the first moment and obtain the candidate centroid position of the above-mentioned vehicle at the second moment, where the second moment is the moment before the first moment;

[0022] Based on the second Kalman gain corresponding to the first moment and the candidate centroid position of the above-mentioned vehicle at the second moment, perform a correction process on the above-mentioned candidate centroid positions to obtain corrected candidate centroid positions.

[0023] Optionally, before the above-mentioned obtaining of the second Kalman gain corresponding to the first moment, it further includes:

[0024] Determine the prior error covariance matrix corresponding to the first moment;

[0025] Based on the above-mentioned prior error covariance matrix, determine the second Kalman gain corresponding to the first moment.

[0026] Optionally, the above-mentioned determining of the prior error covariance matrix corresponding to the first moment includes:

[0027] Based on the posterior error covariance matrix corresponding to the second moment;

[0028] Based on the above-mentioned posterior error covariance matrix, determine the prior error covariance matrix corresponding to the first moment.

[0029] Optionally, the above-mentioned at least two functional systems include an intelligent driving system.

[0030] Optionally, the process of determining the candidate centroid position through the above-mentioned intelligent driving system includes:

[0031] Through the above-mentioned intelligent driving system, determine the first centroid position based on the sensing data of at least one sensing system;

[0032] Based on the above first centroid position, determine the above candidate centroid position.

[0033] Before determining the above candidate centroid position based on the above first centroid position, it further includes:

[0034] Perform a correction process on the above first centroid position to obtain a corrected first centroid position.

[0035] Optionally, the above at least one sensing system includes a combined positioning system. Performing a correction process on the above first centroid position to obtain a corrected first centroid position includes:

[0036] Perform a correction process on the first centroid position determined based on the sensing data of the above combined positioning system through a real-time kinematic positioning algorithm to obtain a corrected first centroid position.

[0037] Optionally, the above first centroid position is the position of the above vehicle at a first moment. Performing a correction process on the above first centroid position to obtain a corrected first centroid position includes:

[0038] Obtain the third Kalman gain corresponding to the first moment and obtain the first centroid position of the above vehicle at a second moment, where the second moment is the moment before the first moment;

[0039] Perform a correction process on the above first centroid position based on the third Kalman gain corresponding to the first moment and the first centroid position of the above vehicle at the second moment to obtain a corrected first centroid position.

[0040] Optionally, there are at least two of the above sensing systems. Based on the above first centroid position, determining the above candidate centroid position includes:

[0041] Perform a fusion process based on the above first centroid position to obtain the above candidate centroid position.

[0042] Optionally, at least two of the above sensing systems include a combined positioning system and a first sensor of the above vehicle.

[0043] Optionally, the above first sensor is a radar.

[0044] Optionally, the above at least two functional systems include a vehicle control system.

[0045] Optionally, the process of determining the candidate centroid position through the above vehicle control system includes:

[0046] Through the above vehicle control system, determine the candidate centroid position based on the sensing data of the second sensor of the above vehicle.

[0047] Optionally, there are two of the above-mentioned second sensors. Determining the candidate centroid position based on the perception data of the second sensors of the vehicle includes:

[0048] Determining the second centroid position based on the perception data of one of the second sensors of the vehicle;

[0049] Performing correction processing on the second centroid position based on the perception data of the other second sensor of the vehicle to obtain the candidate centroid position.

[0050] Optionally, the perception data of one of the second sensors is acceleration. Determining the second centroid position based on the perception data of one of the second sensors of the vehicle includes:

[0051] Performing low-pass filtering on the perception data of one of the second sensors of the vehicle to obtain filtered data;

[0052] Performing double integration on the filtered data to obtain the second centroid position.

[0053] Optionally, one of the second sensors is an inertial measurement unit, and the other second sensor is a wheel speed sensor.

[0054] According to the second aspect of the present application, there is provided a centroid position determination device, including:

[0055] An acquisition module for acquiring candidate centroid positions determined by at least two functional systems of a vehicle respectively;

[0056] A fusion module for performing fusion processing based on the candidate centroid positions to obtain the centroid position of the vehicle.

[0057] According to the third aspect of the present application, there is also provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the embodiments of the present application are implemented.

[0058] According to the fourth aspect of the present application, there is also provided a controller, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the embodiments of the present application are implemented.

[0059] According to the fifth aspect of the present application, there is also provided a vehicle, including the controller in the embodiments of the present application.

[0060] According to the sixth aspect of the present application, there is also provided a computer program product, including a computer program or instruction, and when the computer program or instruction is executed by a processor, the steps in the embodiments of the present application are implemented.

[0061] In summary, in the embodiments of the present application, candidate centroid positions respectively determined by at least two functional systems of a vehicle are obtained, and fusion processing is performed based on the candidate centroid positions to obtain the centroid position of the vehicle, so as to realize obtaining the centroid position of the vehicle by performing fusion processing on at least two candidate centroid positions, and improve the accuracy of the centroid position of the vehicle.

[0062] Other features and advantages of the present application will be described in detail in the following specific implementation section. Brief Description of the Drawings

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative efforts.

[0064] In order to more fully understand the present application and its beneficial effects, the following will be described in conjunction with the drawings, where the same reference numerals represent the same parts in the following description.

[0065] Figure 1 is a flowchart of the steps of a method for determining a centroid position provided in an exemplary embodiment of the present disclosure;

[0066] Figure 2 is another schematic diagram of a method for determining a centroid position provided in an exemplary embodiment of the present disclosure;

[0067] Figure 3 is a schematic diagram of a system of a vehicle provided in an exemplary embodiment of the present disclosure;

[0068] Figure 4 is a schematic diagram of a centroid position determination device provided in an exemplary embodiment of the present disclosure;

[0069] Figure 5 is a schematic diagram of the architecture of a vehicle provided in an exemplary embodiment of the present disclosure. Detailed Description of the Embodiments

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present application.

[0071] The following describes the Kalman filter algorithm.

[0072] In Kalman filtering, the Kalman gain refers to a key parameter in the Kalman filter, which is used to weight and combine the predicted position and the observed position to obtain the optimal estimated position.

[0073] First, based on the position at time k-1, predict the predicted position at time k. Specifically, the predicted position can be obtained through the following formula (1):

[0074]

[0075] where, represents the predicted position at time k, F represents the state transition matrix, represents the position at time k-1, B represents the control matrix, and μ k-1 represents the control vector at time k-1.

[0076] Next, based on the posterior error covariance matrix corresponding to time k-1, determine the prior error covariance matrix corresponding to time k. Based on the prior error covariance matrix corresponding to time k, determine the Kalman gain corresponding to time k. Among them, the posterior error covariance matrix corresponding to time k-1 can be obtained based on the prior error covariance matrix corresponding to time k-1 and the Kalman gain. Specifically, the prior error covariance matrix corresponding to time k-1 and the Kalman gain can be substituted into formula (2) for calculation to obtain the posterior error covariance matrix corresponding to time k-1:

[0077]

[0078] where, P k-1 represents the posterior error covariance matrix corresponding to time k-1, I represents the identity matrix, K k-1 represents the Kalman gain corresponding to time k-1, H k-1 represents the observation matrix corresponding to time k-1, represents the prior error covariance matrix corresponding to time k-1.

[0079] After obtaining the posterior error covariance matrix corresponding to time k-1, the posterior error covariance matrix corresponding to time k-1 can be substituted into the following formula (3) to obtain the prior error covariance matrix corresponding to time k:

[0080]

[0081] where, represents the prior error covariance matrix corresponding to time k, P k-1 represents the posterior error covariance matrix corresponding to time k-1, F represents the state transition matrix, T represents the transpose, and Q is the process noise covariance matrix.

[0082] Then, substitute the prior error covariance matrix corresponding to time k into Equation (4) for calculation to obtain the Kalman gain corresponding to time k:

[0083]

[0084] where K represents the Kalman gain corresponding to time k, represents the prior error covariance matrix corresponding to time k, H represents the observation matrix, T represents the transpose, and R represents the observation noise covariance matrix.

[0085] After obtaining the Kalman gain corresponding to time k, the Kalman gain corresponding to time k, the predicted position at time k, and the observed position at time k can be substituted into Equation (5) for calculation to obtain the optimal estimated position corresponding to time k:

[0086]

[0087] where represents the optimal estimated position corresponding to time k, K represents the Kalman gain corresponding to time k, z k represents the observed position at time k, H represents the observation matrix, represents the predicted position at time k.

[0088] It can be understood that the posterior error refers to the difference between the true position and the optimal estimated position, and the posterior error covariance matrix refers to the covariance matrix of the posterior error. The prior error refers to the difference between the true position and the predicted position, and the prior error covariance matrix refers to the covariance matrix of the prior error. Formulas (2) and (3) can be pre-constructed based on the posterior error and the prior error, so that the posterior error covariance matrix corresponding to time k-1 can be directly obtained through Formula (2) based on the prior error covariance matrix corresponding to time k-1, and the prior error covariance matrix corresponding to time k can be directly obtained through Formula (3) based on the posterior error covariance matrix corresponding to time k-1.

[0089] This application provides a method for determining the centroid position. Please refer to Figure 1 , the method for determining the centroid position provided by the embodiments of this application includes Step 100 - Step 200, which will be introduced in detail below.

[0090] Step 100: Obtain candidate centroid positions respectively determined by at least two functional systems of the vehicle.

[0091] Among them, the type of the vehicle can be set according to the actual situation. For example, the vehicle can be a fuel vehicle, a plug-in hybrid vehicle, a new energy vehicle, etc., and this embodiment does not make any limitations here.

[0092] The functional system of a vehicle refers to the combination of hardware and / or software designed to achieve a certain function on the vehicle, which can be set according to the actual situation. For example, the functional system may include at least one of the intelligent driving system, vehicle control system, and perception system of the vehicle. The perception system is used to perceive the motion state information of the vehicle and / or the environment in which the vehicle is located, and this embodiment does not limit it here.

[0093] The center of mass of a vehicle, which can also be referred to as the center of gravity of the vehicle, refers to a point where the vehicle's mass is concentrated. The candidate center of mass position refers to the center of mass position determined by the functional system. Each functional system can determine a candidate center of mass position.

[0094] In some embodiments, when at least two functional systems include the intelligent driving system, the process of determining the candidate center of mass position through the intelligent driving system includes:

[0095] Through the intelligent driving system, determine the first center of mass position based on the perception data of at least one perception system;

[0096] Based on the first center of mass position, determine the candidate center of mass position.

[0097] Among them, the perception system can be used to perceive the motion state information of the vehicle and / or the environment in which the vehicle is located. That is, the perception data of the perception system is used to represent the motion state information of the vehicle and / or the environment in which the vehicle is located. The motion state information of the vehicle includes at least one of the vehicle's position, speed, and acceleration. When the perception system is a combined positioning system (P-box), the perception data of the perception system is positioning data. When the perception system is an inertial measurement unit (IMU), the perception data of the perception system is acceleration and angular velocity.

[0098] The perception data of the perception system refers to the data collected by the perception system. The first center of mass position is the center of mass position determined based on the perception data of at least one perception system. Based on the perception data of one perception system, one first center of mass position can be determined. For example, when there are two perception systems, two first center of mass positions can be obtained.

[0099] In this embodiment, through the intelligent driving system, determine the first center of mass position based on the perception data of at least one perception system, and based on the first center of mass position, determine the candidate center of mass position, so as to determine the candidate center of mass position through the intelligent driving system, and obtain the center of mass position of the vehicle based on the candidate center of mass position determined by the intelligent driving system, further improving the accuracy of the center of mass position.

[0100] In some embodiments, before determining the candidate center of mass position based on the first center of mass position, it further includes:

[0101] Perform a correction process on the first centroid position to obtain the corrected first centroid position.

[0102] In this embodiment, perform a correction process on the first centroid position to obtain the corrected first centroid position, and then determine the candidate centroid position based on the corrected first centroid position, improving the accuracy of the first centroid position, thereby further improving the accuracy of the candidate centroid position.

[0103] In some embodiments, at least one sensing system includes a combined positioning system. The process of performing a correction process on the first centroid position to obtain the corrected first centroid position includes:

[0104] Perform a correction process on the first centroid position determined based on the sensing data of the combined positioning system through a real-time kinematic (RTK) algorithm to obtain the corrected first centroid position.

[0105] Among them, the real-time kinematic (RTK) algorithm is an algorithm that realizes centimeter-level precise positioning by fusing data from the base station and the mobile station.

[0106] The vehicle can initially estimate the first centroid position of the vehicle through the sensing data of the combined positioning system (GNSS system and IMU) and the geometric parameters of the vehicle, and combine the RTK positioning method to correct the first centroid position to obtain the corrected first centroid position, and the accuracy of the corrected first centroid position can reach the centimeter level.

[0107] In this embodiment, perform a correction process on the first centroid position determined based on the sensing data of the combined positioning system through a real-time kinematic (RTK) algorithm to obtain the corrected first centroid position, and then determine the candidate centroid position based on the corrected first centroid position, improving the accuracy of the first centroid position, thereby further improving the accuracy of the candidate centroid position.

[0108] In some embodiments, the first centroid position is the position of the vehicle at the first moment. The process of performing a correction process on the first centroid position to obtain the corrected first centroid position includes:

[0109] Obtain the third Kalman gain corresponding to the first moment and obtain the first centroid position of the vehicle at the second moment, where the second moment is the moment before the first moment;

[0110] Perform a correction process on the first centroid position based on the third Kalman gain corresponding to the first moment and the first centroid position of the vehicle at the second moment to obtain the corrected first centroid position.

[0111] Specifically, the third Kalman gain corresponding to the first moment can be determined based on the first prior error covariance matrix corresponding to the first moment. Among them, the first prior error covariance matrix corresponding to the first moment can be determined based on the first posterior error covariance matrix corresponding to the second moment, and the first posterior error covariance matrix corresponding to the second moment can be determined based on the first prior error covariance matrix corresponding to the second moment.

[0112] For example, the second moment can be represented by the (k - 1)th moment, and the first moment can be represented by the kth moment. Substitute the first prior error covariance matrix corresponding to the second moment into formula (2) for calculation to obtain the first posterior error covariance matrix corresponding to the second moment. Substitute the first posterior error covariance matrix corresponding to the second moment into formula (3) for calculation to obtain the first prior error covariance matrix corresponding to the first moment. Finally, substitute the first prior error covariance matrix corresponding to the first moment into formula (4) for calculation to obtain the third Kalman gain corresponding to the first moment.

[0113] After the vehicle obtains the first centroid position at the second moment, it can predict the predicted first centroid position of the vehicle at the first moment based on the first centroid position of the vehicle at the second moment. The predicted first centroid position is the predicted position at the first moment. Specifically, the first centroid position at the second moment can be substituted into formula (1) for calculation to obtain the predicted first centroid position at the first moment. At this time, in formula (1), represents the first centroid position at the second moment (which can also represent the corrected first centroid position at the second moment), and in formula (1), represents the predicted first centroid position at the first moment.

[0114] The first centroid position of the vehicle at the first moment is the observed position at the first moment. Substitute the predicted first centroid position at the first moment, the third Kalman gain corresponding to the first moment, and the first centroid position at the first moment into formula (5) for calculation to obtain the corrected first centroid position. The corrected first centroid position is the optimal estimated position at the first moment. At this time, in formula (5), represents the corrected first centroid position, K represents the third Kalman gain corresponding to the first moment, and z k represents the first centroid position corresponding to the first moment.

[0115] In this embodiment, the third Kalman gain corresponding to the first moment is obtained, and the first centroid position of the vehicle at the second moment is obtained. The second moment is the moment immediately preceding the first moment. Based on the third Kalman gain corresponding to the first moment and the first centroid position of the vehicle at the second moment, the first centroid position is corrected to obtain the corrected first centroid position, thereby realizing the correction of the first centroid position through the Kalman filter algorithm, further improving the accuracy of the first centroid position, and thus further improving the accuracy of the candidate centroid position.

[0116] In some embodiments, the first centroid position can be corrected simultaneously by the real-time kinematic (RTK) algorithm and the Kalman filter algorithm. At this time, the first centroid position corrected by the RTK algorithm can be referred to as the initial first centroid position. First, the first centroid position is corrected by the RTK algorithm to obtain the initial first centroid position, and then the initial first centroid position is corrected by the Kalman filter algorithm to obtain the corrected first centroid position.

[0117] In some embodiments, when at least one sensing system includes an integrated positioning system, determining a candidate centroid position based on the first centroid position includes:

[0118] Determining the first centroid position as the candidate centroid position.

[0119] Among them, the integrated positioning system usually consists of a Global Navigation Satellite System (GNSS), an Inertial Measurement Unit (IMU), and a computing chip. It receives satellite signals through the GNSS to achieve global positioning, and is calibrated through the IMU, and has the positioning ability to maintain a certain time accuracy for a certain period when the GNSS signal is lost.

[0120] In some embodiments, when there are at least two sensing systems, determining a candidate centroid position based on the first centroid position includes:

[0121] Performing fusion processing based on the first centroid position to obtain the candidate centroid position.

[0122] Among them, when there are at least two sensing systems, the first centroid position also includes at least two. Performing fusion processing based on the first centroid position can refer to directly performing fusion processing on the first centroid position, or performing fusion processing on the corrected first centroid position. This embodiment does not make a limitation here.

[0123] In this embodiment, there are at least two perception systems, and there are at least two first centroid positions. Based on the first centroid positions, fusion processing is performed to obtain candidate centroid positions, realizing obtaining candidate centroid positions through at least two first centroid positions, and further improving the accuracy of the candidate centroid positions.

[0124] In some embodiments, when there are two perception systems, the two perception systems include an integrated positioning system and a first sensor of the vehicle. Among them, one perception system is an integrated positioning system, and one perception system includes a first sensor of the vehicle. The type of the first sensor can be set according to the actual situation. For example, the first sensor can be a radar or a GPS, and this embodiment does not make a limitation here.

[0125] In this embodiment, the two perception systems include an integrated positioning system and a first sensor of the vehicle, realizing obtaining candidate centroid positions through the integrated positioning system and the first sensor of the vehicle, and further improving the accuracy of the candidate centroid positions. Since the perception data of the radar can include the environmental characteristics and scene information where the vehicle is located, when the first sensor is a radar, the accuracy of the candidate centroid positions can be further improved.

[0126] In some embodiments, the method of performing fusion processing based on the first centroid positions to obtain candidate centroid positions can be set according to the actual situation. For example, it can be through a weighting method, an averaging method or a Kalman filtering algorithm to perform fusion processing based on the first centroid positions, and this embodiment does not make a limitation here.

[0127] When performing fusion processing based on the first centroid positions through the weighting method, performing fusion processing based on the first centroid positions to obtain candidate centroid positions includes:

[0128] Performing fusion processing on the first centroid positions based on the weights respectively corresponding to each first centroid position to obtain candidate centroid positions.

[0129] For example, the first centroid positions include the first centroid position d1 and the first centroid position d2. The weight corresponding to the first centroid position d1 is the first weight, and the weight corresponding to the first centroid position d2 is the second weight. Multiply the first centroid position d1 by the first weight to obtain the first adjusted position, multiply the second centroid position d2 by the second weight to obtain the second adjusted position, and add the first adjusted position and the second adjusted position to obtain the candidate centroid position.

[0130] When performing fusion processing based on the first centroid positions through the averaging method, performing fusion processing based on the first centroid positions to obtain candidate centroid positions includes:

[0131] Determine the average value of the first centroid positions as the candidate centroid position.

[0132] When performing fusion processing based on the first centroid position through the Kalman filtering algorithm, performing fusion processing based on the first centroid position to obtain a candidate centroid position, including:

[0133] Based on each first centroid position, determine the position difference information;

[0134] Based on the first target centroid position, the fourth Kalman gain, and the position difference information, determine the candidate centroid position.

[0135] Among them, the first target centroid position can be any one of the first centroid positions. Specifically, the Kalman filtering algorithm can be used to fuse positions through formula (6):

[0136]

[0137] Among them, represents the fused position, z1 and z2 represent the positions to be fused respectively, k represents the Kalman gain, and (z2 - z1) represents the position difference information.

[0138] In this embodiment, substituting the first target centroid position, the fourth Kalman gain, and the position difference information into formula (6) for calculation, the candidate centroid position can be obtained, and the candidate centroid position is z1 and z2 represent the first centroid position and z1 represents the first target centroid position at the same time, and k represents the fourth Kalman gain.

[0139] It can be understood that when there are more than two first centroid positions, two first centroid positions can be selected from the first centroid positions first and substituted into formula (6) for calculation to obtain an intermediate position, and then a new first centroid position can be selected from the first centroid positions, and the intermediate position and the new first centroid position are used as z1 and z2 in formula (6) to continue the calculation until all the first centroid positions are substituted into formula (6) for calculation and then stop.

[0140] In some embodiments, when the perception system includes a combined positioning system and corrects the first centroid position determined from the perception data based on the combined positioning system through the real-time kinematic positioning algorithm to obtain the corrected first centroid position, the corrected first centroid position can be determined as the candidate centroid position.

[0141] In some embodiments, when at least two perception systems include an integrated positioning system and, through a real-time kinematic positioning algorithm, perform correction processing on a first centroid position determined from perception data based on the integrated positioning system to obtain a corrected first centroid position, a fusion process may be performed based on the corrected first centroid position and a first centroid position determined from perception data of other perception systems, where the other perception systems refer to perception systems other than the integrated positioning system among the at least two perception systems.

[0142] For example, at least two perception systems include an integrated positioning system and a first sensor of the vehicle. A first centroid position d1 is obtained based on the integrated positioning system, and a first centroid position d2 is obtained based on the perception data of the first sensor. Through a real-time kinematic positioning algorithm, correction processing is performed on the first centroid position d1 to obtain a corrected first centroid position d11, and a fusion process is performed based on the first centroid position d11 and the first centroid position d2 to obtain a candidate centroid position.

[0143] In some embodiments, at least two functional systems include a vehicle control system. The vehicle control system includes a Vehicle Control Unit (VCU).

[0144] In this embodiment, at least two functional systems include a vehicle control system, and the centroid position of the vehicle is determined based on the candidate centroid position determined by the vehicle control system, further improving the accuracy of the centroid position.

[0145] In some embodiments, when at least two functional systems include a vehicle control system, the process of determining a candidate centroid position by the vehicle control system includes:

[0146] Through the vehicle control system, based on the perception data of a second sensor of the vehicle, a candidate centroid position is determined.

[0147] Among them, the type of the second sensor can be set according to the actual situation. For example, the second sensor may include at least one of an inertial measurement unit and a wheel speed sensor. When the second sensor is an inertial measurement unit, the perception data of the second sensor may include acceleration and angular velocity. When the second sensor is a wheel speed sensor, the perception data of the second sensor may include the wheel speed of the vehicle.

[0148] In this embodiment, at least two functional systems include a vehicle control system. Through the vehicle control system, based on the perception data of a second sensor of the vehicle, a candidate centroid position is determined, further improving the accuracy of the centroid position.

[0149] In some embodiments, there are two second sensors. Determining a candidate centroid position based on the perception data of the second sensors of the vehicle includes:

[0150] Determine a second centroid position based on the sensing data of a second sensor of the vehicle;

[0151] Based on the sensing data of another second sensor of the vehicle, perform a correction process on the second centroid position to obtain a candidate centroid position.

[0152] Wherein, for example, one second sensor is an inertial measurement unit and another second sensor is a wheel speed sensor, and for another example, one second sensor is a wheel speed sensor and another second sensor is an inertial measurement unit. This embodiment does not make any limitation here.

[0153] In this embodiment, based on the sensing data of a second sensor of the vehicle, determine a second centroid position, and based on the sensing data of another second sensor of the vehicle, perform a correction process on the second centroid position to obtain a candidate centroid position, so as to realize determining a candidate centroid position based on the sensing data of two second sensors, and further improve the accuracy of the candidate centroid position.

[0154] In some embodiments, the sensing data of one second sensor is acceleration. Based on the sensing data of a second sensor of the vehicle, determining a second centroid position includes:

[0155] Perform a low-pass filtering process on the sensing data of a second sensor of the vehicle to obtain filtered data;

[0156] Perform a double integral on the filtered data to obtain the second centroid position.

[0157] Wherein, the manner of performing a low-pass filtering process on the sensing data of one second sensor can be set according to actual situations, and this embodiment does not make any limitation here.

[0158] In this embodiment, the sensing data of one second sensor is acceleration. First, perform a low-pass filtering process on the sensing data of a second sensor of the vehicle to obtain filtered data, and then perform a double integral on the filtered data to obtain the second centroid position, further improving the accuracy of the second centroid position, and thus further improving the accuracy of the candidate centroid position.

[0159] In some embodiments, the process of performing a correction process on the second centroid position based on the sensing data of another second sensor of the vehicle to obtain a candidate centroid position includes:

[0160] Determine a third centroid position based on the sensing data of another second sensor of the vehicle;

[0161] Based on the third centroid position, perform a correction process on the second centroid position to obtain a candidate centroid position.

[0162] Among them, based on the perception data of another second sensor, the third centroid position is obtained through the dynamic model of the vehicle. The process of correcting the second centroid position based on the third centroid position can be understood as the fusion process of the third centroid position and the second centroid position. The process of fusing the third centroid position and the second centroid position can refer to the process of fusing the first centroid position, which will not be elaborated in this embodiment.

[0163] Step 200, perform a fusion process based on the candidate centroid position to obtain the centroid position of the vehicle.

[0164] Among them, the candidate centroid position can be an uncorrected candidate centroid position or a corrected candidate centroid position.

[0165] In this embodiment, a fusion process is performed based on the candidate centroid positions respectively determined by at least two functional systems to obtain the centroid position of the vehicle, so as to obtain the centroid position of the vehicle through multiple candidate centroid positions and improve the accuracy of the centroid position.

[0166] In some embodiments, before performing a fusion process based on the candidate centroid position to obtain the centroid position of the vehicle, it further includes:

[0167] Perform a correction process on the candidate centroid position to obtain a corrected candidate centroid position.

[0168] Among them, the method of correcting the candidate centroid position can be selected according to the actual situation. For example, the candidate centroid position can be corrected by the Kalman filter algorithm or the least squares method, which is not limited in this embodiment.

[0169] In this embodiment, performing a correction process on the candidate centroid position to obtain a corrected candidate centroid position improves the accuracy of the candidate centroid position, thereby further improving the accuracy of the centroid position.

[0170] In some embodiments, performing a correction process on the candidate centroid position to obtain a corrected candidate centroid position includes:

[0171] Obtain the second Kalman gain corresponding to the first moment and obtain the candidate centroid position of the vehicle at the second moment, where the second moment is the moment before the first moment;

[0172] Based on the second Kalman gain corresponding to the first moment and the candidate centroid position of the vehicle at the second moment, perform a correction process on the candidate centroid position to obtain a corrected candidate centroid position.

[0173] Among them, the third Kalman gain and the second Kalman gain are different gains. The third Kalman gain is the gain for the first centroid position, and the second Kalman gain is the gain for the candidate centroid position.

[0174] After the vehicle obtains the candidate centroid position at the second moment, it can predict the predicted candidate centroid position of the vehicle at the first moment based on the candidate centroid position of the vehicle at the second moment. The predicted candidate centroid position is the predicted position at the first moment. Specifically, the first moment can be represented by the k-th moment, and the second moment can be represented by the (k - 1)-th moment. Substitute the candidate centroid position at the second moment into formula (1) for calculation to obtain the predicted candidate centroid position at the first moment. At this time, represents the candidate centroid position at the second moment (which can also represent the corrected candidate centroid position at the second moment), and in formula (1) represents the predicted candidate centroid position at the first moment.

[0175] After obtaining the predicted candidate centroid position, substitute the second Kalman gain, the candidate centroid position, and the predicted candidate centroid position corresponding to the first moment into formula (5) for calculation to obtain the corrected candidate centroid position. At this time, the corrected candidate centroid position is the optimal estimated position corresponding to the first moment. in formula (5) represents the corrected candidate centroid position, K represents the second Kalman gain, and z k represents the candidate centroid position corresponding to the first moment.

[0176] In this embodiment, the second Kalman gain corresponding to the first moment is obtained, and the candidate centroid position of the vehicle at the second moment is obtained. The second moment is the previous moment of the first moment. Based on the second Kalman gain corresponding to the first moment and the candidate centroid position of the vehicle at the second moment, the candidate centroid position is corrected to obtain the corrected candidate centroid position, realizing the correction of the candidate centroid position through the Kalman filtering algorithm, improving the accuracy of the candidate centroid position, and further improving the accuracy of the centroid position.

[0177] In some embodiments, before obtaining the second Kalman gain corresponding to the first moment, it further includes:

[0178] Determine the prior error covariance matrix corresponding to the first moment;

[0179] Based on the prior error covariance matrix, determine the second Kalman gain corresponding to the first moment.

[0180] Among them, determining the prior error covariance matrix corresponding to the first moment includes:

[0181] Based on the posterior error covariance matrix corresponding to the second moment;

[0182] Based on the posterior error covariance matrix, determine the prior error covariance matrix corresponding to the first moment.

[0183] Among them, the posterior error covariance matrix corresponding to the second moment can be obtained from the prior error covariance matrix corresponding to the second moment and the second Kalman gain corresponding to the second moment. Specifically, when the first moment is the k-th moment and the second moment is the (k - 1)-th moment, the posterior error covariance matrix corresponding to the second moment can be obtained through formula (2).

[0184] After obtaining the posterior error covariance matrix corresponding to the second moment, through formula (3), based on the posterior error covariance matrix corresponding to the second moment, obtain the prior error covariance matrix corresponding to the first moment. Finally, through formula (4), based on the prior error covariance matrix corresponding to the first moment, obtain the second Kalman gain corresponding to the first moment. At this time, K in formula (4) represents the second Kalman filter corresponding to the first moment.

[0185] In some embodiments, the manner of fusing the candidate centroid positions can be set according to the actual situation. For example, it can be fused based on the candidate centroid positions through the weighting method, the averaging method, or the Kalman filter algorithm. This embodiment does not make any limitations here.

[0186] When fusing based on the candidate centroid positions through the averaging method, fusing based on the candidate centroid positions to obtain the centroid position of the vehicle includes:

[0187] Determine the average value of the candidate centroid positions;

[0188] Determine the average value as the centroid position of the vehicle.

[0189] When fusing based on the candidate centroid positions through the weighting method, fusing based on the candidate centroid positions to obtain the centroid position of the vehicle includes:

[0190] Obtain the weights corresponding to at least two functional systems respectively;

[0191] Fuse based on the candidate centroid positions and the corresponding weights to obtain the centroid position of the vehicle.

[0192] For example, at least two functional systems include an intelligent driving system and a vehicle control system. The weight corresponding to the intelligent driving system is the third weight, and the weight corresponding to the vehicle control system is the fourth weight. The candidate centroid position d3 is determined by the intelligent driving system, and the candidate centroid position d4 is determined by the vehicle control system. Multiply the candidate centroid position d3 by the third weight to obtain the first candidate centroid position, multiply the candidate centroid position d4 by the fourth weight to obtain the second candidate centroid position, and add the first candidate centroid position and the second candidate centroid position to obtain the centroid position of the vehicle.

[0193] In this embodiment, the weights corresponding to at least two functional systems are obtained, and fusion processing is performed based on the candidate centroid position and the corresponding weights to obtain the centroid position of the vehicle, realizing the acquisition of the centroid position by the weight method and improving the accuracy of the centroid position.

[0194] In some embodiments, the corrected candidate centroid position and the corresponding weight can be subjected to fusion processing to obtain the centroid position of the vehicle.

[0195] In some embodiments, when performing fusion processing based on the candidate centroid position through the Kalman filter algorithm, the candidate centroid position is the position of the vehicle at the first moment. Performing fusion processing based on the candidate centroid position to obtain the centroid position of the vehicle includes:

[0196] Obtain the first Kalman gain corresponding to the first moment;

[0197] Perform fusion processing based on the candidate centroid position and the first Kalman gain to obtain the centroid position of the vehicle at the first moment.

[0198] Among them, the first Kalman gain corresponding to the first moment, the second Kalman gain corresponding to the first moment, and the third Kalman gain are all different gains, and the first Kalman gain corresponding to the first moment is the gain for the centroid position.

[0199] In this embodiment, the first Kalman gain corresponding to the first moment is obtained, and fusion processing is performed based on the candidate centroid position and the first Kalman gain to obtain the centroid position of the vehicle at the first moment, realizing the fusion processing of the candidate centroid position through the Kalman filter algorithm and improving the accuracy of the centroid position.

[0200] In some embodiments, there are two candidate centroid positions. Performing fusion processing based on the candidate centroid position and the first Kalman gain to obtain the centroid position of the vehicle at the first moment includes:

[0201] Determine the position difference between the candidate centroid positions;

[0202] Multiply the first Kalman gain corresponding to the first moment by the position difference to obtain the position adjustment parameter;

[0203] Based on the candidate centroid position and the position adjustment parameter, determine the centroid position of the vehicle at the first moment.

[0204] Among them, the candidate centroid position in the candidate centroid position and the position adjustment parameter can be any one of at least two candidate centroid positions.

[0205] Specifically, the candidate centroid position and the first Kalman gain can be substituted into formula (6) for calculation, and the centroid position can be obtained. At this time, the centroid position is z1 and z2 represent the candidate centroid positions, k represents the first Kalman gain, (z2 - z1) represents the position difference, and k(z2 - z1) represents the position adjustment parameter.

[0206] It can be understood that when there are more than two candidate centroid positions, two candidate centroid positions can be first selected from the candidate centroid positions and substituted into formula (6) for calculation to obtain an intermediate candidate position. Then, new candidate centroid positions can be selected from the candidate centroid positions, and the intermediate candidate position and the new candidate centroid position are used as z1 and z2 in formula (6) for continuous calculation until all candidate centroid positions are substituted into formula (6) for calculation and then stop.

[0207] In this embodiment, determine the position difference between the candidate centroid positions, multiply the first Kalman gain corresponding to the first moment by the position difference to obtain the position adjustment parameter, and based on the candidate centroid position and the position adjustment parameter, determine the centroid position of the vehicle at the first moment, thereby realizing the fusion of the candidate centroid positions and improving the accuracy of the centroid position.

[0208] In some embodiments, after determining the centroid position of the vehicle, it further includes:

[0209] Based on the centroid position of the vehicle, execute the side shift function.

[0210] Among them, the side shift function refers to the function that can control the lateral movement of the vehicle, which can be set according to the actual situation. For example, the side shift function can be at least one of the U-turn function, the rotation function, and the turning function, and this embodiment does not make a limitation here.

[0211] In this embodiment, since the accuracy of the centroid position of the vehicle is relatively high, therefore, based on the centroid position of the vehicle, execute the side shift function, which can solve the problem that the side shift function of the vehicle is limited due to inaccurate centroid position recognition.

[0212] Next, according to Figure 2 and Figure 3, a further description of the centroid position determination method provided in this application is given. In this embodiment, an intelligent driving system and a vehicle control system group are taken as examples of two functional systems for description, a radar is taken as an example of the first sensor, an inertial measurement unit and a wheel speed sensor are taken as examples of the second sensor, and a vehicle control unit is taken as an example of the vehicle control system.

[0213] As Figure 2 shown, the intelligent driving system determines the first centroid position at the first moment through the perception data of the combined positioning system, and determines the first centroid position at the first moment through the perception data of the radar (the perception data of the radar is point cloud data). For the convenience of description, the first centroid position determined through the perception data of the combined positioning system is called the first sub-centroid position, and the first centroid position determined through the perception data of the radar is called the second sub-centroid position. The third Kalman gain includes the first sub-Kalman gain and the second sub-Kalman gain.

[0214] The first sub-centroid position is corrected through a real-time kinematic positioning algorithm to obtain an initial first centroid position, and the initial first centroid position is corrected based on the first sub-Kalman gain corresponding to the first moment and the initial first centroid position of the vehicle at the second moment to obtain a corrected first centroid position.

[0215] Based on the second sub-Kalman gain corresponding to the first moment and the second sub-centroid position of the vehicle at the second moment, the second sub-centroid position at the first moment is corrected to obtain a corrected first centroid position.

[0216] Through the weighting method, the corrected first centroid position is fused to obtain a candidate centroid position estimated by the intelligent driving system.

[0217] Through the vehicle control unit, the second centroid position is determined based on the perception data of the inertial measurement unit, and the second centroid position is corrected based on the perception data of the wheel speed sensor to obtain a candidate centroid position estimated by the vehicle control unit.

[0218] Based on the second Kalman gain corresponding to the first moment and the candidate centroid position of the vehicle at the second moment, the candidate centroid position is corrected to obtain a corrected candidate centroid position, and through the weighting method, the corrected candidate centroid position is fused to obtain the centroid position of the vehicle.

[0219] Among them, the system of the vehicle can be as Figure 3As shown in the figure, the vehicle's system includes an integrated positioning system, a real-time kinematic positioning algorithm module, a radar, an Advanced Driver Assistance Systems (ADAS) domain controller, a vehicle control unit, an inertial measurement unit, wheel speed sensors, a drive motor, a steering system, and a braking system. Among them, the ADAS domain controller constitutes an intelligent driving system, and the vehicle control unit constitutes a vehicle control system.

[0220] For the specific implementation manners and corresponding beneficial effects of this embodiment, reference may be specifically made to the above method embodiments, and details are not elaborated herein again.

[0221] As can be seen from the above, in the embodiments of the present application, candidate centroid positions respectively determined by at least two functional systems of the vehicle are obtained, and fusion processing is performed based on the candidate centroid positions to obtain the centroid position of the vehicle, thereby realizing obtaining the centroid position of the vehicle by fusing at least two candidate centroid positions, and improving the accuracy of the centroid position of the vehicle.

[0222] Figure 4 is a schematic structural diagram of a centroid position determination device provided in the embodiments of the present application. Please refer to Figure 4 , the centroid position determination device may include an acquisition module 401 and a fusion module 402. Among them:

[0223] The acquisition module 401 is configured to acquire candidate centroid positions respectively determined by at least two functional systems of the vehicle;

[0224] The fusion module 402 is configured to perform fusion processing based on the candidate centroid positions to obtain the centroid position of the vehicle.

[0225] Among them, the acquisition module 401 and the fusion module 402 may be respectively configured to execute all steps in the corresponding embodiments of the above centroid position determination method. For the specific implementation manners and more detailed contents of these modules, reference may be made to the corresponding method part, and details are not elaborated herein one by one.

[0226] The embodiments of the present application further provide a computer-readable storage medium, on which instructions are stored, and when the instructions are executed by a processor, the processor is configured to execute the above-mentioned scheduling method for mobile charging.

[0227] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0228] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0229] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0230] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0231] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0232] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0233] A computer-readable medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media do not include transitory computer-readable media, such as modulated communication signals and carrier waves.

[0234] In some embodiments, the present application further provides a controller on which a computer program is stored, and when the computer program is executed by a processor, the steps in the embodiments of the present application are implemented.

[0235] In some embodiments, the present application further provides a vehicle including the controller in the embodiments of the present application. As Figure 5 shown, it is a schematic diagram of the architecture of a vehicle provided in the embodiments of the present application. In this embodiment, the vehicle includes various functional systems. In this embodiment, the vehicle can be a fuel vehicle, a plug-in hybrid vehicle, or a new energy vehicle, etc., and the present disclosure does not make specific limitations thereto.

[0236] In some embodiments, the present application further provides a computer program product including a computer program or instructions, and when the computer program or instructions are executed by a processor, the steps in the embodiments of the present application are implemented.

[0237] In the description of the present application, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the present application, "a plurality" means two or more, unless otherwise specifically defined.

[0238] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0239] Among the embodiments, implementation manners, and related technical features of the present application, they can be combined and replaced with each other without conflict.

[0240] The above are only the preferred embodiments of the present application, and do not impose any formal restrictions on the present application. However, any simple modifications, equivalent changes, and decorations made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application still fall within the scope of the technical solution of the present application.

Claims

1. A method for determining the centroid position, characterized in that, Including: Obtaining candidate centroid positions respectively determined by at least two functional systems of a vehicle; Performing fusion processing based on the candidate centroid positions to obtain the centroid position of the vehicle.

2. The method according to claim 1, characterized in that, The performing fusion processing based on the candidate centroid positions to obtain the centroid position of the vehicle includes: Obtaining weights respectively corresponding to the at least two functional systems; Performing fusion processing based on the candidate centroid positions and the corresponding weights to obtain the centroid position of the vehicle.

3. The method according to claim 1, wherein The candidate centroid position is the position of the vehicle at a first moment, and the performing fusion processing based on the candidate centroid positions to obtain the centroid position of the vehicle includes: Obtaining a first Kalman gain corresponding to the first moment; Performing fusion processing based on the candidate centroid position and the first Kalman gain to obtain the centroid position of the vehicle at the first moment.

4. The method according to claim 3, wherein There are two candidate centroid positions, and the performing fusion processing based on the candidate centroid positions and the first Kalman gain to obtain the centroid position of the vehicle at the first moment includes: Determining a position difference between the candidate centroid positions; Multiplying the first Kalman gain corresponding to the first moment by the position difference to obtain a position adjustment parameter; Based on the candidate centroid positions and the position adjustment parameter, determining the centroid position of the vehicle at the first moment.

5. The method according to claim 1, characterized in that, Before the performing fusion processing based on the candidate centroid positions to obtain the centroid position of the vehicle, it further includes: Performing correction processing on the candidate centroid positions to obtain corrected candidate centroid positions.

6. The method according to claim 5, wherein The performing correction processing on the candidate centroid positions to obtain corrected candidate centroid positions includes: Obtaining a second Kalman gain corresponding to the first moment and obtaining the candidate centroid position of the vehicle at a second moment, where the second moment is the moment before the first moment; Performing correction processing on the candidate centroid positions based on the second Kalman gain corresponding to the first moment and the candidate centroid position of the vehicle at the second moment to obtain corrected candidate centroid positions.

7. The method according to claim 6, characterized in that, Before the obtaining the second Kalman gain corresponding to the first moment, it further includes: Determining a prior error covariance matrix corresponding to the first moment; Based on the prior error covariance matrix, determining the second Kalman gain corresponding to the first moment.

8. The method according to claim 7, wherein The determining the prior error covariance matrix corresponding to the first moment includes: Based on the posterior error covariance matrix corresponding to the second moment; Based on the posterior error covariance matrix, determining the prior error covariance matrix corresponding to the first moment.

9. The method according to claim 1, wherein The at least two functional systems include an intelligent driving system.

10. The method according to claim 9, characterized in that, The process of determining the candidate centroid position through the intelligent driving system includes: Through the intelligent driving system, determining a first centroid position based on perception data of at least one perception system; Based on the first centroid position, determining the candidate centroid position.

11. The method according to claim 10, wherein Before the based on the first centroid position, determining the candidate centroid position, it further includes: Performing correction processing on the first centroid position to obtain a corrected first centroid position.

12. The method according to claim 11, characterized in that, The at least one perception system includes an integrated positioning system, and performs correction processing on the first centroid position to obtain a corrected first centroid position, including: Performing correction processing on the first centroid position determined from the perception data based on the integrated positioning system through a real-time kinematic positioning algorithm to obtain a corrected first centroid position.

13. The method according to claim 11, characterized in that The first centroid position is the position of the vehicle at a first moment. Performing correction processing on the first centroid position to obtain a corrected first centroid position includes: Obtaining a third Kalman gain corresponding to the first moment and obtaining the first centroid position of the vehicle at a second moment, where the second moment is the moment immediately preceding the first moment; Performing correction processing on the first centroid position based on the third Kalman gain corresponding to the first moment and the first centroid position of the vehicle at the second moment to obtain a corrected first centroid position.

14. The method according to claim 10, wherein There are at least two perception systems. Determining the candidate centroid position based on the first centroid position includes: Performing fusion processing based on the first centroid position to obtain the candidate centroid position.

15. The method according to claim 14, wherein At least two of the perception systems include an integrated positioning system and a first sensor of the vehicle.

16. The method according to claim 15, wherein The first sensor is a radar.

17. The method according to claim 1, wherein The at least two functional systems include a vehicle control system.

18. The method according to claim 17, wherein The process of determining the candidate centroid position through the vehicle control system includes: Determining the candidate centroid position through the vehicle control system based on the perception data of a second sensor of the vehicle.

19. The method according to claim 18, wherein There are two second sensors of the vehicle. Determining the candidate centroid position based on the perception data of the second sensors of the vehicle includes: Determining a second centroid position based on the perception data of one of the second sensors of the vehicle; Performing correction processing on the second centroid position based on the perception data of the other second sensor of the vehicle to obtain the candidate centroid position.

20. The method according to claim 19, wherein The perception data of one of the second sensors is acceleration. Determining the second centroid position based on the perception data of one of the second sensors of the vehicle includes: Performing low-pass filtering on the perception data of one of the second sensors of the vehicle to obtain filtered data; Performing double integration on the filtered data to obtain the second centroid position.

21. The method according to claim 19, wherein, One of the second sensors is an inertial measurement unit, and the other second sensor is a wheel speed sensor.

22. A centroid position determination device, characterized in that Including: An acquisition module for acquiring candidate centroid positions respectively determined by at least two functional systems of the vehicle; A fusion module for performing fusion processing based on the candidate centroid positions to obtain the centroid position of the vehicle.

23. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 21.

24. A controller, on which a computer program is stored, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 21.

25. A vehicle, characterized in that, Including the controller according to claim 24.

26. A computer program product, characterized in that, Including a computer program or instruction, and when the computer program or instruction is executed by a processor, it implements the steps of the method according to any one of claims 1 to 21.

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