Method for determining pitch angle and roll angle, vehicle and computer program product

Abstract

The invention provides a pitch angle and roll angle determination method, a vehicle and a computer program product, which are applied to the field of vehicles, and the method comprises the following steps: acquiring inertial measurement unit (IMU) data and wheel speed meter data of a target vehicle; determining driving attribute information of each IMU data point based on the IMU data and the wheel speed meter data, and determining initial gravitational acceleration information corresponding to each IMU data point based on the IMU data; the initial gravitational acceleration information comprises acceleration components of gravitational acceleration in different directions; based on the driving attribute information, optimizing the initial gravitational acceleration information of the IMU data points to obtain target gravitational acceleration information; and determining a pitch angle and a roll angle of the target vehicle based on the target gravitational acceleration information. In this way, the accuracy of the pitch angle and the roll angle can be improved, and further, the effect is better when attitude estimation and driving planning are carried out based on the pitch angle and the roll angle.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle technology, and in particular to a method for determining a pitch angle and a roll angle, a vehicle, and a computer program product. Background Art

[0002] In current autonomous vehicles, the current attitude of the vehicle can generally be obtained through algorithms such as pre-integration. In attitude estimation, the pitch angle and roll angle are crucial for the current state estimation of the vehicle and the subsequent planning and control of autonomous driving. The pitch angle indicates whether the vehicle is on a ramp and the slope of the ramp, and the roll angle indicates whether the road surface on which the vehicle is traveling has a horizontal inclination and the size of the inclination. The estimation of the pitch angle and roll angle will directly affect the accuracy of attitude estimation. Summary of the invention

[0003] The embodiments of the present disclosure at least provide a method for determining a pitch angle and a roll angle, a vehicle, and a computer program product.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for determining a pitch angle and a roll angle, comprising: Obtain the inertial measurement unit (IMU) data and wheel speed meter data of the target vehicle; Based on the IMU data and the wheel speed meter data, determining the driving attribute information of each IMU data point, and based on the IMU data, determining the initial gravity acceleration information corresponding to each IMU data point; the initial gravity acceleration information includes acceleration components of gravity acceleration in different directions; Based on the driving attribute information, the initial gravity acceleration information of the IMU data point is optimized to obtain target gravity acceleration information; The pitch angle and roll angle of the target vehicle are determined based on the target gravity acceleration information.

[0005] In a possible implementation manner, the driving attribute information includes: a curve section, a non-curve section, a uniform speed driving section, and a non-uniform speed driving section.

[0006] In a possible implementation manner, the IMU data includes angular velocity information of each of a plurality of IMU data points and a corresponding first timestamp; the wheel speed meter data includes wheel speeds of each of a plurality of wheel speed data points and a corresponding second timestamp; Determining the driving attribute information of each IMU data point based on the IMU data and the wheel speed meter data includes: Based on the wheel speed of each wheel speed data point and the corresponding second timestamp, determine the first longitudinal acceleration corresponding to each wheel speed data point; wherein the longitudinal acceleration is the acceleration of the target vehicle in the front-rear direction; Based on the first timestamp corresponding to the IMU data point and the second timestamp corresponding to the wheel speed data point, after aligning the IMU data point with the wheel speed data point, determine the second longitudinal acceleration corresponding to each IMU data point; For any IMU data point, based on the second longitudinal acceleration and acceleration threshold corresponding to the IMU data point, determine whether the IMU data point is a point within the non-uniform speed driving interval; and, based on the angular velocity information and angular velocity threshold of the IMU data point, determine whether the IMU data point is a point within the curve interval.

[0007] In a possible implementation, the IMU data includes measured acceleration information of each of a plurality of IMU data points; The determining the initial gravity acceleration information corresponding to each IMU data point based on the IMU data includes: For the i-th IMU data point, based on the initial gravity acceleration information corresponding to the i-1-th IMU data point and the angular velocity information of the i-th IMU data point, determine the predicted gravity acceleration information of the i-th IMU data point; wherein the initial gravity acceleration information corresponding to the first IMU data point is the measured acceleration information initially collected by the IMU; The predicted gravity acceleration information of the i-th IMU data point and the measured acceleration information of the i-th IMU data point are weightedly summed to determine the initial gravity acceleration information of the i-th IMU data point.

[0008] In a possible implementation, the first weight of the predicted gravity acceleration information of the i-th IMU data point is the confidence of the predicted gravity acceleration information of the i-th IMU data point; the second weight of the measured acceleration information of the i-th IMU data point is used to characterize the confidence of the measured acceleration information of the i-th IMU data point; the first weight is a preset value, and the second weight is determined based on the first weight.

[0009] In a possible implementation, based on the driving attribute information, the initial gravity acceleration information of the IMU data point is optimized to obtain the target gravity acceleration information, including: Determine the driving attribute information corresponding to the IMU data point as the data point to be optimized of the target attribute; Based on the optimization data matching the target attribute, the initial gravity acceleration information corresponding to the data point to be optimized is optimized to obtain the optimized gravity acceleration information of the data point to be optimized; wherein the target attribute is an attribute that affects the gravity acceleration information; The optimized gravity acceleration information of the data point to be optimized and the initial gravity acceleration information of other IMU data points are used as the target gravity acceleration information.

[0010] In a possible implementation manner, when the target attribute includes a curve section, the optimization data matching the target attribute includes a vertical acceleration in the initial gravity acceleration information; wherein the vertical acceleration is an acceleration in a direction perpendicular to the ground; The optimizing data matching the target attribute, optimizing the initial gravity acceleration information corresponding to the data point to be optimized, comprises: For any data point to be optimized, determining the turning radius of the target vehicle based on the wheelbase of the target vehicle and the steering wheel angle corresponding to the data point to be optimized; Determining a first lateral acceleration of the data point to be optimized based on the turning radius of the target vehicle and the vertical acceleration; Based on the first lateral acceleration of the data point to be optimized, the second lateral acceleration in the initial gravity acceleration information of the data point to be optimized is optimized; wherein the lateral acceleration is the acceleration of the target vehicle in the left and right directions.

[0011] In a possible implementation manner, when the target attribute includes a non-uniform speed driving interval, the optimization data matching the target attribute includes the second longitudinal acceleration; The optimizing data matching the target attribute is used to optimize the initial gravity acceleration information corresponding to the data point to be optimized, so as to obtain the optimized gravity acceleration information of the data point to be optimized, including: For any data point to be optimized, the third longitudinal acceleration in the initial gravity acceleration information of the data point to be optimized is optimized based on the second longitudinal acceleration corresponding to the data point to be optimized.

[0012] In a second aspect, an embodiment of the present disclosure further provides a vehicle, which is used to execute the steps in the above-mentioned first aspect, or any possible implementation of the first aspect.

[0013] In a third aspect, an embodiment of the present disclosure further provides a computer program product, including a computer program, which, when executed, implements the method described in the above-mentioned first aspect, or any possible implementation manner of the first aspect.

[0014] In the pitch angle and roll angle determination method, vehicle and computer program product provided by the embodiments of the present disclosure, the pitch angle and roll angle can be estimated in combination with the wheel speed meter data of the target vehicle. After the driving attribute information of each IMU data point is determined in combination with the IMU data and the wheel speed meter data, and the initial gravity acceleration information of each IMU data point is determined based on the IMU data, the initial gravity acceleration information of the IMU data point can be optimized in a targeted manner for different driving attribute information, and then the pitch angle and roll angle of the target vehicle can be determined based on the target gravity acceleration information obtained after optimization. In this way, the accuracy of the pitch angle and roll angle can be improved, and the effect is better when the posture estimation and driving planning are performed based on the pitch angle and roll angle.

[0015] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following is a brief introduction to the drawings required for use in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can also be obtained based on these drawings without creative work.

[0017] Figure 1 A schematic diagram of a three-dimensional coordinate system of a target vehicle provided by an embodiment of the present disclosure is shown; Figure 2 A flow chart of a method for determining a pitch angle and a roll angle provided by an embodiment of the present disclosure is shown; Figure 3 A schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure claimed for protection, but merely represents selected embodiments of the present disclosure.

[0019] Research has found that the current attitude of the vehicle can generally be obtained through algorithms such as pre-integration in current autonomous vehicles. In attitude estimation, the pitch angle and roll angle are crucial for the current state estimation of the vehicle and the subsequent planning and control of autonomous driving. The pitch angle indicates whether the vehicle is on a ramp and the slope of the ramp, and the roll angle indicates whether the road surface on which the vehicle is traveling has a horizontal inclination and the size of the inclination. The estimation of the pitch angle and roll angle will directly affect the accuracy of attitude estimation.

[0020] In the related technology, there is a method that estimates the direction of gravity by fusing the Inertial Measurement Unit (IMU) data and the Global Positioning System (GPS) data, and then estimates the pitch and roll angles based on the gravity direction. However, this method is highly dependent on the GPS signal and will fail in occluded scenes; there are also studies that focus on estimating the pitch and roll angles of the vehicle only through IMU data, but it cannot handle the problem of pitch angle estimation drift when the vehicle accelerates or decelerates suddenly.

[0021] Based on the above research, the present disclosure provides a method for determining pitch angle and roll angle, a vehicle and a computer program product, which can estimate the pitch angle and roll angle in combination with the wheel speed meter data of the target vehicle, determine the driving attribute information of each IMU data point in combination with the IMU data and the wheel speed meter data, and determine the initial gravity acceleration information of each IMU data point based on the IMU data, and then optimize the initial gravity acceleration information of the IMU data point for different driving attribute information, and then determine the pitch angle and roll angle of the target vehicle based on the optimized target gravity acceleration information. In this way, there is no need to rely on GPS signals, and the gravity acceleration information can be optimized for vehicle turning and rapid acceleration scenarios (i.e., different driving attribute information), so the pitch angle and roll angle can be accurately determined, and further, when posture estimation and driving planning are performed based on the pitch angle and roll angle, the effect is better.

[0022] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0023] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0024] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the personal information involved in the present disclosure (including but not limited to attribute information, facial images, etc.) is obtained with the authorization of the user. Specifically, a prompt message requesting authorization is sent to the user through a pop-up window on the page, information push, etc., and the above personal information is obtained after the user agrees to the authorization.

[0025] To facilitate understanding of the present embodiment, a method for determining the pitch angle and the roll angle disclosed in the embodiment of the present disclosure is first introduced in detail. The executor of the method for determining the pitch angle and the roll angle provided in the embodiment of the present disclosure is generally an intelligent driving vehicle or other computer equipment.

[0026] First, the “horizontal”, “longitudinal” and “vertical” in the following embodiments are briefly introduced. Figure 1 As shown, a three-dimensional coordinate system of a target vehicle provided in an embodiment of the present disclosure is provided, wherein the direction along the front of the vehicle is the X-axis, the direction along the left and right of the vehicle is the Y-axis, the direction perpendicular to the ground is the Z-axis, the direction pointed by the X-axis is the longitudinal direction, the direction pointed by the Y-axis is the lateral direction, and the direction pointed by the Z-axis is the vertical direction.

[0027] See also Figure 2 FIG. 2 is a flowchart of a method for determining a pitch angle and a roll angle provided by an embodiment of the present disclosure, wherein the method comprises steps 201 to 204, wherein: Step 201: Obtain IMU data and wheel speed meter data of the target vehicle.

[0028] Step 202: Determine the driving attribute information of each IMU data point based on the IMU data and the wheel speed meter data, and determine the initial gravity acceleration information corresponding to each IMU data point based on the IMU data; the initial gravity acceleration information includes the acceleration components of gravity acceleration in different directions.

[0029] Step 203: Based on the driving attribute information, the initial gravity acceleration information of the IMU data points is optimized to obtain the target gravity acceleration information.

[0030] Step 204: Determine the pitch angle and roll angle of the target vehicle based on the target gravity acceleration information.

[0031] In the above method, the pitch angle and roll angle can be estimated in combination with the wheel speed meter data of the target vehicle. After the driving attribute information of each IMU data point is determined by combining the IMU data and the wheel speed meter data, and the initial gravity acceleration information of each IMU data point is determined based on the IMU data, the initial gravity acceleration information of the IMU data point can be optimized for different driving attribute information, and then the pitch angle and roll angle of the target vehicle can be determined based on the optimized target gravity acceleration information. In this way, there is no need to rely on GPS signals, and the gravity acceleration information can be optimized for vehicle turning and rapid acceleration scenarios (i.e. different driving attribute information), so the pitch angle and roll angle can be accurately determined, and the effect is better when posture estimation and driving planning are performed based on the pitch angle and roll angle.

[0032] The target vehicle may be equipped with an IMU and a wheel speedometer. The IMU data and wheel speedometer data of the target vehicle obtained in step 201 may be the IMU data and wheel speedometer data collected in real time by the IMU and the wheel speedometer during the driving process of the target vehicle, or may be the IMU data and wheel speedometer data stored on the target vehicle.

[0033] The IMU data may include angular velocity information of each of the multiple IMU data points and a corresponding first timestamp, wherein the angular velocity information of the IMU data point may include angular velocity components in each direction. For example, if the angular velocity information is transmitted through To characterize, ,in, Respectively represent the angular velocity components in the x, y, and z axis directions; in addition, the IMU data can also include the measured acceleration information of each IMU data point. The measured acceleration information can include the acceleration components in each direction measured by the IMU. For example, if the measured acceleration information is passed To characterize, ,in, They respectively represent the acceleration components in the x, y, and z axes measured by the IMU.

[0034] The wheel speed meter data may include the wheel speed of each of the plurality of wheel speed data points and the corresponding second timestamp. In addition, the wheel speed meter data may also include the steering wheel angle of each wheel number data point.

[0035] The driving attribute information in step 202 can be used to characterize changes in the driving area / driving speed of the target vehicle. Exemplarily, the driving attribute information may include: a curve interval, a non-curve interval, a uniform speed driving interval, and a non-uniform speed driving interval.

[0036] In this way, by distinguishing the driving attribute information of different data points, different optimizations are then performed on the initial gravity acceleration information of the data points with different driving attribute information, so that more accurate gravity acceleration information that conforms to the driving attributes can be obtained.

[0037] In a possible implementation, when determining the driving attribute information of each IMU data point based on the IMU data and the wheel speed meter data, it can be achieved through the following steps a1-a3: Step a1: determining a first longitudinal acceleration corresponding to each wheel speed data point based on the wheel speed of each wheel speed data point and the corresponding second timestamp; wherein the longitudinal acceleration is the acceleration in the front-rear direction of the target vehicle; Specifically, for the i-th wheel speed data point, the first longitudinal acceleration corresponding to the i-th wheel speed data point can be determined based on the wheel speed of the i-th wheel speed data point, the wheel speed of the i+1-th wheel speed data point, the second timestamp corresponding to the i-th wheel speed data point, and the second timestamp of the i+1-th wheel speed data point.

[0038] For example, when calculating the first longitudinal acceleration, the following formula may be used:

[0039] in, represents the first longitudinal acceleration of the i-th wheel speed data point, represents the wheel speed of the i-th wheel speed data point, represents the second timestamp of the i-th wheel speed data point; represents the wheel speed of the i+1th wheel speed data point, Indicates the second timestamp of the i+1th wheel speed data point.

[0040] Step a2, based on the first timestamp corresponding to the IMU data point and the second timestamp corresponding to the wheel speed data point, align the IMU data point and the wheel speed data point, and determine the second longitudinal acceleration corresponding to each IMU data point; Here, since the frequency of IMU data collection is inconsistent with the frequency of wheel speed meter data collection, the time interval between the first timestamps of two adjacent IMU data points is different from the time interval between the second timestamps of two adjacent wheel speed data points. Therefore, the IMU data points and the wheel speed data points can be aligned based on the first timestamp corresponding to the IMU data points and the second timestamp corresponding to the wheel speed data points.

[0041] Specifically, for each IMU data point, the wheel speed data point corresponding to the second timestamp closest to the first timestamp of the IMU data point can be used as the wheel speed data point corresponding to the IMU data point. The wheel speed data point corresponding to the IMU data point can be regarded as the wheel speed data point collected at the same time as the collection time of the IMU data point.

[0042] After the IMU data points and the wheel speed data points are aligned, the second longitudinal acceleration corresponding to each IMU data point can be determined according to the first longitudinal acceleration corresponding to the wheel speed data point corresponding to each IMU data point.

[0043] Step a3: for any IMU data point, determine whether the IMU data point is a point within the non-uniform speed driving interval based on the second longitudinal acceleration and the acceleration threshold corresponding to the IMU data point; and determine whether the IMU data point is a point within the curve interval based on the angular velocity information and the angular velocity threshold of the IMU data point.

[0044] In a specific implementation, the second longitudinal acceleration of each IMU data point can be traversed, and if the second longitudinal acceleration of a certain IMU data point is greater than the acceleration threshold, the IMU data point can be determined as a point within the non-uniform speed driving interval. And, The angular velocity information of each IMU data point is traversed. If the vertical angular velocity in the angular velocity information of a certain IMU data point is greater than the angular velocity threshold, the IMU data point can be determined as a point within the curve interval.

[0045] Among them, whether the second longitudinal acceleration is greater than the acceleration threshold may refer to whether the absolute value of the second longitudinal acceleration is greater than the acceleration threshold; and whether the vertical angular velocity is greater than the angular velocity threshold may refer to whether the absolute value of the vertical angular velocity is greater than the angular velocity threshold.

[0046] It should be noted that the driving attribute information of points within the non-uniform speed driving interval is the non-uniform speed driving interval, and the driving attribute information of points within the curve interval is the curve interval; judging whether a certain IMU data point belongs to the non-uniform speed driving interval and whether it belongs to the curve interval is judged in parallel, that is, there may be some IMU data points that belong to both the non-uniform speed driving interval and the curve interval.

[0047] In this way, the IMU data and the wheel speed meter data can be combined to realize the division of the IMU data points, so as to carry out targeted processing on the data points obtained under different driving attributes of the target vehicle.

[0048] In a possible implementation, when determining the initial gravity acceleration information corresponding to each IMU data point based on the IMU data, it can be implemented through the following steps b1 to b2: Step b1: for the i-th IMU data point, based on the initial gravity acceleration information corresponding to the i-1-th IMU data point and the angular velocity information of the i-th IMU data point, determine the predicted gravity acceleration information of the i-th IMU data point; wherein, the initial gravity acceleration information corresponding to the first IMU data point is the measured acceleration information initially collected by the IMU.

[0049] Specifically, when calculating the predicted gravity acceleration information, you can first calculate the timestamp interval between the i-th IMU data point and the i-1-th IMU data point. , and then the predicted gravity acceleration information can be calculated using the following formula:

[0050]

[0051] in, The quaternion representation of the rotation change of the i-th IMU data point is the Euler angle to quaternion function, Represents the angular velocity information of the i-th IMU data point, Represents the predicted gravity acceleration information of the i-th IMU data point, Represents the predicted gravity acceleration information of the i-1th IMU data point.

[0052] Step b2: perform weighted summation on the predicted gravity acceleration information of the ith IMU data point and the measured acceleration information of the ith IMU data point to determine the initial gravity acceleration information of the ith IMU data point.

[0053] Here, the first weight of the predicted gravity acceleration information of the i-th IMU data point is the confidence of the predicted gravity acceleration information of the i-th IMU data point; the second weight of the measured acceleration information of the i-th IMU data point is used to characterize the confidence of the measured acceleration information of the i-th IMU data point; the first weight is a preset value, and the second weight is determined based on the first weight.

[0054] For example, the prediction confidence of the predicted gravity acceleration information of the i-th IMU data point can be obtained by The first weight can be expressed as: ,in, is the time interval between the first timestamp of the i-th IMU data point and the i-1-th IMU data point.

[0055] The initial gravitational acceleration information can be expressed as: .in, is the measured acceleration information of the i-th IMU data point.

[0056] The initial gravity acceleration information calculated in this way combines the IMU detection acceleration information with the initial gravity acceleration information of other IMU data points calculated previously, thereby improving the accuracy of the initial gravity acceleration.

[0057] Since gravity acceleration is only affected in curved scenarios or non-uniform speed driving scenarios, when optimizing the initial gravity acceleration information of IMU data points, only the initial gravity acceleration information of some IMU data points can be optimized based on the driving attribute information of each IMU data point.

[0058] In a possible implementation, when optimizing the initial gravity acceleration information of the IMU data points based on the driving attribute information to obtain the target gravity acceleration information, the following steps c1 to c3 may be referred to for implementation: Step c1: Determine the driving attribute information corresponding to the IMU data point as the data point to be optimized for the target attribute.

[0059] Here, the target attribute may refer to an attribute that affects the gravity acceleration information by the driving area / driving acceleration. For example, the target attribute may include a curve section and a non-uniform speed driving section.

[0060] Step c2: based on the optimization data matching the target attribute, the initial gravity acceleration information corresponding to the data point to be optimized is optimized to obtain the optimized gravity acceleration information of the data point to be optimized; wherein the target attribute is the attribute of the driving area / driving acceleration that affects the gravity acceleration information.

[0061] Step c3: taking the optimized gravity acceleration information of the data point to be optimized and the initial gravity acceleration information of other IMU data points as the target gravity acceleration information.

[0062] Optionally, the types of optimization data matched with different target attributes may be different. Exemplarily, if the target attribute is a curve interval, the optimization data matched with the target attribute may include the vertical acceleration in the initial gravity acceleration information; wherein the vertical acceleration is the acceleration in the direction perpendicular to the ground; if the target attribute is a uniform speed driving interval, the optimization data matched with the target attribute may include the second longitudinal acceleration (determined based on step a2).

[0063] In one possible implementation, when the target attribute includes a curve interval, when optimizing the initial gravity acceleration information corresponding to the data point to be optimized based on the optimization data matching the target attribute, for any data point to be optimized, the turning radius of the target vehicle can be first determined based on the wheelbase of the target vehicle and the steering wheel angle corresponding to the data point to be optimized; then, based on the turning radius and vertical acceleration of the target vehicle, the first lateral acceleration of the data point to be optimized is determined; and then, based on the first lateral acceleration of the data point to be optimized, the second lateral acceleration in the initial gravity acceleration information of the data point to be optimized is optimized; wherein the lateral acceleration is the acceleration of the target vehicle in the left and right directions.

[0064] For example, it can be calculated by the following formula:

[0065] in, Indicates the turning radius, represents the steering wheel angle, Indicates the wheelbase of the target vehicle.

[0066]

[0067] in, represents the first lateral acceleration, represents the vertical acceleration.

[0068] When the second lateral acceleration is optimized based on the first lateral acceleration, the difference between the second lateral acceleration and the first lateral acceleration may be used as the optimized second lateral acceleration.

[0069] The first lateral acceleration is the centripetal acceleration generated when the target vehicle turns. By optimizing the second lateral acceleration in the initial gravity acceleration information by the first lateral acceleration, the influence of the centripetal force on the lateral acceleration when the target vehicle turns can be reduced, thereby improving the accuracy of the lateral acceleration. In one possible implementation, when the target attribute includes a non-uniform speed driving interval, the initial gravity acceleration information corresponding to the data point to be optimized is optimized based on the optimization data matching the target attribute to obtain the optimized gravity acceleration information of the data point to be optimized. For any data point to be optimized, the third longitudinal acceleration in the initial gravity acceleration information of the data point to be optimized can be optimized based on the second longitudinal acceleration corresponding to the data point to be optimized.

[0070] Specifically, the second longitudinal acceleration may be calculated through the above step a2. When the third longitudinal acceleration is optimized based on the second longitudinal acceleration, the difference between the third longitudinal acceleration and the second longitudinal acceleration may be used as the optimized third longitudinal acceleration.

[0071] In this way, the third longitudinal acceleration in the initial gravity acceleration information can be optimized, the influence of acceleration and deceleration of the target vehicle on the longitudinal acceleration can be reduced, and the accuracy of the longitudinal acceleration can be improved.

[0072] Optionally, if the driving attribute information of a certain IMU data point includes both a curve interval and a non-uniform speed driving interval, the second lateral acceleration and the third longitudinal acceleration in the initial gravity acceleration information of the IMU data point can be optimized simultaneously through the above implementation method.

[0073] In a possible implementation, the pitch angle of the target vehicle may be determined by the longitudinal acceleration and the vertical acceleration in the target gravity acceleration information; and the roll angle of the target vehicle may be determined by the lateral acceleration and the vertical acceleration in the target gravity acceleration information.

[0074] Exemplarily, the pitch angle can be calculated by the following formula:

[0075] in, represents the pitch angle of the target vehicle at the time corresponding to the i-th IMU data point, Represents the longitudinal acceleration in the target gravity acceleration information of the i-th IMU data point, Represents the vertical acceleration in the target gravity acceleration information of the i-th IMU data point.

[0076] The roll angle can be calculated using the following formula:

[0077] in, represents the rolling angle of the target vehicle at the time corresponding to the i-th IMU data point, Represents the lateral acceleration in the target gravity acceleration information of the i-th IMU data point.

[0078] Optionally, after determining the roll angle and pitch angle of the target vehicle at the time corresponding to each IMU data point (ie, at the time of each first timestamp), before generating the trajectory of the target vehicle, abnormal pitch angles and roll angles may be eliminated.

[0079] Specifically, the pitch angles corresponding to each IMU data point can be traversed. For the i-th IMU data point, if the pitch angle corresponding to the IMU data point is greater than the pitch angle threshold, the pitch angle value of the i-th IMU data point can be updated to the pitch angle value corresponding to the i-1-th IMU data point.

[0080] And, traverse the roll angles corresponding to each IMU data point, for the i-th IMU data point, if the roll angle corresponding to the IMU data point is greater than the roll angle threshold, the roll angle value of the i-th IMU data point can be updated to the roll angle value corresponding to the i-1-th IMU data point.

[0081] After the abnormal pitch angle and roll angle are updated, the trajectory of the target vehicle may be generated based on the updated pitch angle and roll angle.

[0082] In addition, the calculated pitch angle and roll angle can also be used to estimate the attitude of the target vehicle.

[0083] The vehicle trajectory generated in this way can be closer to the actual trajectory of the target vehicle, and the estimated posture of the target vehicle can be closer to the actual posture of the target vehicle.

[0084] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0085] Based on the same technical concept, the embodiment of the present disclosure also provides a computer device. Figure 3 As shown, it is a schematic diagram of the structure of a computer device 300 provided in an embodiment of the present disclosure, including a processor 301, a memory 302, and a bus 303. Among them, the memory 302 is used to store execution instructions, including a memory 3021 and an external memory 3022; the memory 3021 here is also called an internal memory, which is used to temporarily store the operation data in the processor 301, and the data exchanged with the external memory 3022 such as a hard disk. The processor 301 exchanges data with the external memory 3022 through the memory 3021. When the computer device 300 is running, the processor 301 communicates with the memory 302 through the bus 303, so that the processor 301 executes the following instructions: Obtain the inertial measurement unit (IMU) data and wheel speed meter data of the target vehicle; Based on the IMU data and the wheel speed meter data, the driving attribute information of each IMU data point is determined, and based on the IMU data, the initial gravity acceleration information corresponding to each IMU data point is determined; the initial gravity acceleration information includes the acceleration components of gravity acceleration in different directions; Based on the driving attribute information, the initial gravity acceleration information of the IMU data points is optimized to obtain the target gravity acceleration information; The pitch angle and roll angle of the target vehicle are determined based on the target gravity acceleration information.

[0086] In a possible implementation, in the instructions executed by the processor 301, the driving attribute information includes: a curve section, a non-curve section, a uniform speed driving section, and a non-uniform speed driving section.

[0087] In a possible implementation, in the instructions executed by the processor 301, the IMU data includes angular velocity information of each of the plurality of IMU data points and a corresponding first timestamp; the wheel speed meter data includes wheel speeds of each of the plurality of wheel speed data points and a corresponding second timestamp; Based on the IMU data and wheel speed meter data, the driving attribute information of each IMU data point is determined, including: Based on the wheel speed of each wheel speed data point and the corresponding second timestamp, determine the first longitudinal acceleration corresponding to each wheel speed data point; wherein the longitudinal acceleration is the acceleration in the front-rear direction of the target vehicle; Based on the first timestamp corresponding to the IMU data point and the second timestamp corresponding to the wheel speed data point, the IMU data point and the wheel speed data point are aligned to determine the second longitudinal acceleration corresponding to each IMU data point; For any IMU data point, based on the second longitudinal acceleration and acceleration threshold corresponding to the IMU data point, determine whether the IMU data point is a point within the non-uniform speed driving interval; and, based on the angular velocity information and angular velocity threshold of the IMU data point, determine whether the IMU data point is a point within the curve interval.

[0088] In a possible implementation, in the instruction executed by the processor 301, the IMU data includes measured acceleration information of each of the plurality of IMU data points; Based on the IMU data, the initial gravity acceleration information corresponding to each IMU data point is determined, including: For the i-th IMU data point, based on the initial gravity acceleration information corresponding to the i-1-th IMU data point and the angular velocity information of the i-th IMU data point, determine the predicted gravity acceleration information of the i-th IMU data point; wherein the initial gravity acceleration information corresponding to the first IMU data point is the measured acceleration information initially collected by the IMU; The predicted gravity acceleration information of the ith IMU data point and the measured acceleration information of the ith IMU data point are weightedly summed to determine the initial gravity acceleration information of the ith IMU data point.

[0089] In one possible implementation, in the instructions executed by the processor 301, the first weight of the predicted gravity acceleration information of the i-th IMU data point is the confidence of the predicted gravity acceleration information of the i-th IMU data point; the second weight of the measured acceleration information of the i-th IMU data point is used to characterize the confidence of the measured acceleration information of the i-th IMU data point; the first weight is a preset value, and the second weight is determined based on the first weight.

[0090] In a possible implementation, the instructions executed by the processor 301 optimize the initial gravity acceleration information of the IMU data points based on the driving attribute information to obtain the target gravity acceleration information, including: Determine the driving attribute information corresponding to the IMU data point as the data point to be optimized for the target attribute; Based on the optimization data matching the target attribute, the initial gravity acceleration information corresponding to the data point to be optimized is optimized to obtain the optimized gravity acceleration information of the data point to be optimized; wherein the target attribute is the attribute that affects the gravity acceleration information; The optimized gravity acceleration information of the data point to be optimized and the initial gravity acceleration information of other IMU data points are used as the target gravity acceleration information.

[0091] In a possible implementation, in the instruction executed by the processor 301, when the target attribute includes a curve section, the optimization data matching the target attribute includes a vertical acceleration in the initial gravity acceleration information; wherein the vertical acceleration is an acceleration in a direction perpendicular to the ground; Based on the optimization data matching the target attribute, the initial gravity acceleration information corresponding to the data point to be optimized is optimized, including: For any data point to be optimized, based on the wheelbase of the target vehicle and the steering wheel angle corresponding to the data point to be optimized, determine the turning radius of the target vehicle; Determining a first lateral acceleration of the data point to be optimized based on a turning radius and a vertical acceleration of the target vehicle; Based on the first lateral acceleration of the data point to be optimized, the second lateral acceleration in the initial gravity acceleration information of the data point to be optimized is optimized; wherein the lateral acceleration is the acceleration of the target vehicle in the left and right directions.

[0092] In a possible implementation, in the instruction executed by the processor 301, when the target attribute includes a non-uniform speed driving interval, the optimization data matching the target attribute includes a second longitudinal acceleration; Based on the optimization data matching the target attribute, the initial gravity acceleration information corresponding to the data point to be optimized is optimized to obtain the optimized gravity acceleration information of the data point to be optimized, including: For any data point to be optimized, the third longitudinal acceleration in the initial gravity acceleration information of the data point to be optimized is optimized based on the second longitudinal acceleration corresponding to the data point to be optimized.

[0093] The present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for determining the pitch angle and the roll angle in the above method embodiment are executed. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0094] The embodiments of the present disclosure also provide a computer program product, which carries a program code. The instructions included in the program code can be used to execute the steps of the method for determining the pitch angle and the roll angle in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0095] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium, and in another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0096] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0097] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0099] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0100] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for determining a pitch angle and a roll angle, characterized in that: include: Obtain the inertial measurement unit (IMU) data and wheel speed meter data of the target vehicle; Determine the driving attribute information of each IMU data point based on the IMU data and the wheel speed meter data, and determine the initial gravity acceleration information corresponding to each IMU data point based on the IMU data; The initial gravitational acceleration information includes acceleration components of gravitational acceleration in different directions; Based on the driving attribute information, the initial gravity acceleration information of the IMU data point is optimized to obtain target gravity acceleration information; The pitch angle and roll angle of the target vehicle are determined based on the target gravity acceleration information.

2. The method according to claim 1, characterized in that The driving attribute information includes: a curve section, a non-curve section, a uniform speed driving section, and a non-uniform speed driving section.

3. The method according to claim 1, characterized in that The IMU data includes angular velocity information of each of the multiple IMU data points and a corresponding first timestamp; the wheel speed meter data includes wheel speeds of each of the multiple wheel speed data points and a corresponding second timestamp; Determining the driving attribute information of each IMU data point based on the IMU data and the wheel speed meter data includes: Based on the wheel speed of each wheel speed data point and the corresponding second timestamp, determine the first longitudinal acceleration corresponding to each wheel speed data point; wherein the longitudinal acceleration is the acceleration in the front-rear direction of the target vehicle; Based on the first timestamp corresponding to the IMU data point and the second timestamp corresponding to the wheel speed data point, after aligning the IMU data point with the wheel speed data point, determine the second longitudinal acceleration corresponding to each IMU data point; For any IMU data point, based on the second longitudinal acceleration and acceleration threshold corresponding to the IMU data point, determine whether the IMU data point is a point within the non-uniform speed driving interval; and, based on the angular velocity information and angular velocity threshold of the IMU data point, determine whether the IMU data point is a point within the curve interval.

4. The method according to claim 1, characterized in that: The IMU data includes measured acceleration information of each of a plurality of IMU data points; The determining the initial gravity acceleration information corresponding to each IMU data point based on the IMU data includes: For the i-th IMU data point, based on the initial gravity acceleration information corresponding to the i-1-th IMU data point and the angular velocity information of the i-th IMU data point, determine the predicted gravity acceleration information of the i-th IMU data point; wherein the initial gravity acceleration information corresponding to the first IMU data point is the measured acceleration information initially collected by the IMU; The predicted gravity acceleration information of the i-th IMU data point and the measured acceleration information of the i-th IMU data point are weightedly summed to determine the initial gravity acceleration information of the i-th IMU data point.

5. The method according to claim 4, characterized in that The first weight of the predicted gravity acceleration information of the i-th IMU data point is the confidence of the predicted gravity acceleration information of the i-th IMU data point; the second weight of the measured acceleration information of the i-th IMU data point is used to characterize the confidence of the measured acceleration information of the i-th IMU data point; The first weight is a preset value, and the second weight is determined based on the first weight.

6. The method according to claim 3, characterized in that Based on the driving attribute information, the initial gravity acceleration information of the IMU data point is optimized to obtain the target gravity acceleration information, including: Determine the driving attribute information corresponding to the IMU data point as the data point to be optimized of the target attribute; Based on the optimization data matching the target attribute, the initial gravity acceleration information corresponding to the data point to be optimized is optimized to obtain the optimized gravity acceleration information of the data point to be optimized; wherein the target attribute is an attribute that affects the gravity acceleration information; The optimized gravity acceleration information of the data point to be optimized and the initial gravity acceleration information of other IMU data points are used as the target gravity acceleration information.

7. The method according to claim 6, characterized in that In the case where the target attribute includes a curve section, the optimization data matching the target attribute includes the vertical acceleration in the initial gravity acceleration information; wherein the vertical acceleration is the acceleration in the direction perpendicular to the ground; The optimizing data matching the target attribute, optimizing the initial gravity acceleration information corresponding to the data point to be optimized, comprises: For any data point to be optimized, determining the turning radius of the target vehicle based on the wheelbase of the target vehicle and the steering wheel angle corresponding to the data point to be optimized; Determining a first lateral acceleration of the data point to be optimized based on the turning radius of the target vehicle and the vertical acceleration; Based on the first lateral acceleration of the data point to be optimized, the second lateral acceleration in the initial gravity acceleration information of the data point to be optimized is optimized; wherein the lateral acceleration is the acceleration of the target vehicle in the left and right directions.

8. The method according to claim 6, characterized in that In the case where the target attribute includes a non-uniform speed driving interval, the optimization data matching the target attribute includes the second longitudinal acceleration; The optimizing data matching the target attribute is used to optimize the initial gravity acceleration information corresponding to the data point to be optimized, so as to obtain the optimized gravity acceleration information of the data point to be optimized, including: For any data point to be optimized, the third longitudinal acceleration in the initial gravity acceleration information of the data point to be optimized is optimized based on the second longitudinal acceleration corresponding to the data point to be optimized.

9. A vehicle, characterized in that: The vehicle is used to perform the steps of the method for determining the pitch angle and the roll angle according to any one of claims 1 to 8.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed, the steps of the method for determining the pitch angle and the roll angle according to any one of claims 1 to 8 are implemented.

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