Vehicle motion parameter updating method and device, vehicle and storage medium

Abstract

The invention provides a vehicle motion parameter updating method and device, a vehicle and a storage medium. Relates to the technical field of automatic driving. The method comprises the steps of determining operation parameters of a vehicle according to motion parameters of the vehicle, obtaining residual errors of the operation parameters according to the motion state of the vehicle and a preset residual function group, and optimizing the motion parameters according to a parameter optimization function to obtain optimized motion parameters. Through the method, the efficiency and accuracy of motion parameter updating are improved, and the problems of low efficiency and insufficient accuracy when the motion parameters are manually measured and updated are solved.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a method, device, vehicle, and storage medium for updating motion parameters of a vehicle. Background Technology

[0002] In the field of autonomous driving, the motion state of a vehicle can be estimated using motion parameters (such as the position and orientation of a camera relative to the vehicle), thereby improving the safety and reliability of autonomous driving.

[0003] In existing technologies, offline calibration methods are typically used, with these motion parameters being measured and updated manually. However, this process is time-consuming and often requires professional personnel, resulting in low efficiency in updating motion parameters. Furthermore, manual measurement is susceptible to human error, which reduces the accuracy of motion parameter updates.

[0004] Therefore, there is an urgent need for a more automated and efficient solution to improve the efficiency and accuracy of motion parameter updates. Summary of the Invention

[0005] This application provides a method, apparatus, vehicle, and storage medium for updating motion parameters of a vehicle, which improves the efficiency and accuracy of motion parameter updates.

[0006] In a first aspect, this application provides a method for updating the motion parameters of a vehicle, the method comprising:

[0007] Based on the vehicle's motion parameters, the vehicle's operating parameters are determined, including the vehicle's velocity and angular velocity in different dimensions of the vehicle coordinate system.

[0008] Based on the vehicle's motion state and a preset set of residual functions, the residuals of the operating parameters are obtained. The set of residual functions includes residual functions used to calculate the magnitude of the error between the actual and expected values ​​of different operating parameters.

[0009] The motion parameters are optimized according to the parameter optimization function to obtain the optimized motion parameters. The parameter optimization function is established based on the residuals of the motion parameters.

[0010] In one possible implementation, obtaining the residuals of the operating parameters based on the vehicle's motion state and a preset set of residual functions includes:

[0011] Obtain the target residual function set corresponding to the motion state from the preset residual function set;

[0012] The residuals of the operating parameters are determined based on the operating parameters and the target residual function set.

[0013] In one possible implementation, if the motion state is a straight-line state, then determining the residuals of the motion parameters based on the motion parameters and the target residual function set includes:

[0014] Based on the longitudinal speed of the vehicle, the residual of the longitudinal speed is calculated using the longitudinal speed residual function;

[0015] Based on the lateral velocity of the vehicle, the residual of the lateral velocity is calculated using the lateral velocity residual function;

[0016] Based on the vertical velocity of the vehicle, the residual of the vertical velocity is calculated using the vertical velocity residual function;

[0017] The operating parameters include the longitudinal velocity, the lateral velocity, and the vertical velocity; the target residual function set includes the longitudinal velocity residual function, the lateral velocity residual function, and the vertical velocity residual function.

[0018] In one possible implementation, if the motion state is a turning state, then determining the residuals of the motion parameters based on the motion parameters and the target residual function set includes:

[0019] Based on the longitudinal speed of the vehicle, the residual of the longitudinal speed is calculated using the longitudinal speed residual function;

[0020] Based on the lateral velocity of the vehicle, the residual of the lateral velocity is calculated using the lateral velocity residual function;

[0021] Based on the vertical velocity of the vehicle, the residual of the vertical velocity is calculated using the vertical velocity residual function;

[0022] Based on the vertical angular velocity of the vehicle, the residual of the vertical angular velocity is calculated using the vertical angular velocity residual function;

[0023] The operating parameters include the longitudinal velocity, the lateral velocity, the vertical velocity, and the vertical angular velocity; the target residual function set includes the longitudinal velocity residual function, the lateral velocity residual function, the vertical velocity residual function, and the vertical angular velocity residual function.

[0024] In one possible implementation, the motion parameters include the attitude parameters of the camera mounted in the vehicle relative to the vehicle, the position parameters of the camera relative to the vehicle, and the track width parameters of the vehicle's rear wheels.

[0025] In one possible implementation, the motion parameters are determined based on a digital model of the vehicle, which includes structural and motion parameters of the vehicle, and is used to simulate the performance of the vehicle under different motion states.

[0026] In one possible implementation, optimizing the motion parameters according to the parameter optimization function to obtain optimized motion parameters includes:

[0027] The motion parameters are iteratively calculated using the parameter optimization function to obtain the iteratively calculated motion parameters;

[0028] If the motion parameters calculated iteratively meet the preset conditions, then the motion parameters calculated iteratively are determined as the optimized motion parameters.

[0029] The preset condition is that the residual of the operating parameters is minimized.

[0030] In one possible implementation, the weighting factors in the residual functions of the target residual function set are different when the vehicle is in a straight-ahead state and a turning state.

[0031] Secondly, this application provides a vehicle motion parameter updating device, the device comprising:

[0032] The first processing module is used to determine the vehicle's operating parameters based on the vehicle's motion parameters, the operating parameters including the vehicle's velocity and angular velocity in different dimensions of the vehicle coordinate system;

[0033] The acquisition module is used to acquire the residuals of the operating parameters based on the vehicle's motion state and a preset set of residual functions. The set of residual functions includes residual functions used to calculate the magnitude of the error between the actual and expected values ​​of different operating parameters.

[0034] The second processing module is used to optimize the motion parameters according to the parameter optimization function to obtain the optimized motion parameters. The parameter optimization function is established based on the residuals of the motion parameters.

[0035] Thirdly, this application provides an electronic device, including: a processor, a memory, and a communication interface;

[0036] The memory stores computer-executed instructions;

[0037] The processor executes computer execution instructions stored in the memory to implement the vehicle motion parameter update method according to any of the first aspects above.

[0038] Fourthly, this application provides a vehicle, including a vehicle body and electronic equipment as described in the third aspect.

[0039] Fifthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the vehicle motion parameter update method described in any of the first aspects above.

[0040] In a sixth aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the vehicle motion parameter update method described in any of the first aspects above.

[0041] The vehicle motion parameter updating method, device, vehicle, and storage medium provided in this application can determine the vehicle's operating parameters based on the vehicle's motion parameters, obtain the residuals of the operating parameters based on the vehicle's motion state and a preset set of residual functions, and optimize the motion parameters according to a parameter optimization function to obtain the optimized motion parameters. The method of this application can update the vehicle's motion parameters online according to the vehicle's motion state, improving the efficiency and accuracy of motion parameter updating. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0043] Figure 1 A schematic diagram illustrating the application scenarios provided in the embodiments of this application;

[0044] Figure 2 A flowchart illustrating an embodiment of the vehicle motion parameter update method provided in this application;

[0045] Figure 3 A flowchart illustrating Embodiment 2 of the vehicle motion parameter update method provided in this application;

[0046] Figure 4 A schematic diagram of the vehicle motion parameter updating device provided in this application;

[0047] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;

[0048] Figure 6 This is a schematic diagram of the vehicle structure provided in an embodiment of this application.

[0049] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0051] With the rapid development of autonomous driving technology, sensor fusion technology plays a crucial role in autonomous driving systems. Autonomous vehicles are typically equipped with a variety of sensors, including cameras, wheel speed sensors, inertial measurement units (IMUs), and angular velocity sensors, to comprehensively perceive the surrounding environment and accurately locate themselves, thereby improving the estimation of the vehicle's motion state and enhancing the safety of autonomous driving.

[0052] In practical applications of vehicles, the vehicle's motion parameters, such as the position and orientation of the camera relative to the vehicle, and the track width of the rear wheels, can deteriorate due to aging or loosening of the mechanical structure. This can lead to changes in motion parameters, which in turn affect the vehicle's positioning accuracy and the safety of autonomous driving.

[0053] In related technologies, offline calibration methods are typically used, where changed motion parameters are updated through manual measurement. However, this process is not only time-consuming but also often requires professional personnel, resulting in low efficiency in updating motion parameters. Furthermore, manual measurement is susceptible to human factors, which may introduce measurement errors and reduce the accuracy of motion parameter updates.

[0054] To address the aforementioned problems, the inventors considered automatically updating motion parameters. Specifically, vehicle operating parameters can be determined based on motion parameters. Residuals of the operating parameters are obtained based on the vehicle's motion state and a preset set of residual functions. A parameter optimization function is then established based on these residuals to optimize the motion parameters, resulting in optimized motion parameters. This improves the efficiency and accuracy of motion parameter updates. Based on this, this application proposes a method for updating vehicle motion parameters to further improve the efficiency and accuracy of motion parameter updates, thereby enhancing vehicle positioning accuracy and the safety of autonomous driving.

[0055] Figure 1 This is a schematic diagram illustrating an application scenario provided in an embodiment of this application. Please refer to [link / reference]. Figure 1The vehicle 101 may be equipped with an electronic device 102, which can update the motion parameters of the vehicle 101 during its movement. The motion parameters may include the attitude parameters of the camera relative to the vehicle 101, the position parameters of the camera relative to the vehicle 101, and the track width parameters of the rear wheels of the vehicle 101.

[0056] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0057] Figure 2 A flowchart illustrating an embodiment of the vehicle motion parameter update method provided in this application.

[0058] Please see Figure 2 The method may include:

[0059] S201. Determine the vehicle's operating parameters based on the vehicle's motion parameters.

[0060] The execution subject in this application embodiment can be an electronic device or a vehicle motion parameter updating device installed in an electronic device. The vehicle motion parameter updating device can be implemented through software or a combination of software and hardware. The vehicle motion parameter updating device can be a processor in the electronic device. For ease of understanding, the following description will use an electronic device as the execution subject.

[0061] In this step, the electronic device can determine the vehicle's operating parameters based on the vehicle's motion parameters, including the vehicle's velocity and angular velocity in different dimensions of the vehicle coordinate system.

[0062] Optionally, the vehicle's rear axle center can be used as the origin of the coordinate system, the positive direction of the X-axis can be defined along the vehicle's forward direction, the positive direction of the Y-axis can be defined along the vehicle's width direction (from the driver's left to the right), and the positive direction of the Z-axis can be defined along the direction perpendicular to the ground upwards, thus establishing the vehicle coordinate system.

[0063] The vehicle's motion parameters may include the attitude parameters of the camera relative to the vehicle, the position parameters of the camera relative to the vehicle, and the track width parameters of the vehicle's rear wheels.

[0064] It should be noted that the camera's attitude and position parameters relative to the vehicle are determined based on the vehicle coordinate system. Specifically, the attitude parameters describe the camera's rotation within the vehicle coordinate system, while the position parameters indicate the camera's exact location within the vehicle coordinate system.

[0065] In one optional implementation, if the vehicle has not updated its motion parameters during its historical driving process, the vehicle's operating parameters can be determined based on the vehicle's motion parameters by using a pre-established Vehicle Digital Model (VDM) to determine the motion parameters (i.e., the initial values ​​of the motion parameters). The VDM includes the vehicle's structural parameters and motion parameters, which are used to simulate the vehicle's performance under different motion states.

[0066] In another optional implementation, if the vehicle's motion parameters have been updated during its historical driving process, the motion parameters used to determine the vehicle's operating parameters (i.e., the initial values ​​of the motion parameters) can be the motion parameters after the last historical update. These motion parameters after the last historical update can be determined based on the vehicle's overall digital model.

[0067] For ease of understanding, in this application, the motion parameters included in the whole vehicle digital model are used as the motion parameters of the vehicle (i.e., the initial values ​​of the motion parameters).

[0068] In one alternative implementation, the vehicle's operating parameters can be determined based on the vehicle's kinematic equations, using the vehicle's motion parameters. These kinematic equations are established based on the camera's operating speed and its three-degree-of-freedom angular velocity.

[0069] Vehicle operating parameters may include the vehicle's longitudinal velocity, lateral velocity, vertical velocity, longitudinal angular velocity, lateral angular velocity, and vertical angular velocity.

[0070] S202. Obtain the residuals of the operating parameters based on the vehicle's motion state and the preset residual function set.

[0071] In this step, the residuals of the operating parameters can be obtained based on the vehicle's motion state and a preset set of residual functions. The residual function set includes residual functions used to calculate the magnitude of the error between the actual and expected values ​​of different operating parameters.

[0072] Optionally, the vehicle's motion state can include straight-going state and turning state.

[0073] In one alternative implementation, the lateral acceleration of the vehicle can be acquired using a lateral acceleration sensor, and an acceleration threshold can be set based on vehicle characteristics and driving environment experience to distinguish between straight-ahead and turning states. For example, the set acceleration threshold is 0.1 m / s². 2 When the vehicle is traveling straight, the lateral acceleration sensor reading is 0.02 m / s².2 Due to 0.02m / s 2 Less than the set threshold of 0.1 m / s 2 Therefore, it can be determined that the vehicle is traveling straight. Conversely, when the vehicle enters a curve, the lateral acceleration sensor reading rises to 0.3 m / s². 2 Due to 0.3m / s 2 Greater than the set threshold of 0.1 m / s 2 Therefore, it can be determined that the vehicle is turning.

[0074] In another alternative implementation, the steering angle of the vehicle's steering wheel can be obtained using an angle sensor, and an angle threshold can be set to distinguish between straight-ahead and turning states. For example, the set angle threshold is ±10°. When the vehicle is traveling straight, the angle sensor reads a steering wheel angle of 2°. Since 2° is within the set threshold range (±10°), it can be determined that the vehicle is traveling straight. Conversely, when the vehicle enters a curve, the angle sensor reads a steering wheel angle of 50°. Since 50° exceeds the set threshold range (±10°), it can be determined that the vehicle is turning.

[0075] In another alternative implementation, the vehicle's angular velocity can be obtained using a gyroscope installed on the vehicle, and an angular velocity threshold can be set to distinguish between straight-moving and turning states. For example, the set angular velocity threshold is ±5° / s. When the vehicle is moving straight, the gyroscope outputs an angular velocity of 2° / s. Since 2° / s is within the set threshold range (±5° / s), it can be determined that the vehicle is moving straight. Conversely, when the vehicle enters a curve, the gyroscope outputs an angular velocity of 7° / s. Since 7° / s exceeds the set threshold range (±5° / s), it can be determined that the vehicle is turning.

[0076] The preset set of residual functions can include at least one residual function, and each residual function can calculate a residual of an operating parameter.

[0077] For example, the preset set of residual functions can be as shown in formula (1):

[0078]

[0079] In formula (1), F1 represents the longitudinal velocity residual, k1 represents the longitudinal wheel speed factor, and v x W represents the longitudinal velocity of the vehicle. r W represents the speed of the vehicle's right rear wheel. l F2 represents the left rear wheel speed of the vehicle, F2 represents the residual lateral velocity, k2 represents the lateral zero-speed constraint factor, and v represents the lateral zero-speed constraint factor. yF3 represents the vehicle's lateral velocity, F3 represents the residual vertical velocity, k3 represents the vertical zero-velocity constraint factor, and v represents the vertical velocity. z F4 represents the vertical velocity of the vehicle, k4 represents the residual vertical angular velocity, and w represents the vertical angular velocity factor. z This represents the vertical angular velocity, and L represents the track width of the vehicle's rear wheels.

[0080] Among them, k1 can be determined by the noise of the wheel speed sensor; k2 can be determined by the speed statistics obtained from the vehicle's lateral speed test; k3 can be determined by the speed statistics obtained from the vehicle's vertical speed test; and k4 can be determined by the noise of the wheel speed sensor and the noise of the gyroscope.

[0081] Optionally, a target residual function set can be determined from a preset residual function set based on the vehicle's motion state, and the residuals of the operating parameters can be determined based on the operating parameters and the target residual function set.

[0082] For example, based on the vehicle's motion state being straight, a target residual function set can be determined from a preset residual function set. The target residual function set can be as shown in formula (2):

[0083]

[0084] The longitudinal velocity v can be obtained from formula (2). x The residual F1 and the lateral velocity v y The residual F2 and the vertical velocity v z The residual F3.

[0085] S203. Optimize the motion parameters according to the parameter optimization function to obtain the optimized motion parameters.

[0086] In this step, the motion parameters (i.e., the initial values ​​of the motion parameters) can be optimized based on the parameter optimization function established from the residuals of the running parameters, resulting in the optimized motion parameters.

[0087] It should be noted that since the vehicle's rear wheel track parameter usually does not change when the vehicle is moving in a straight line, the rear wheel track parameter does not need to be optimized when moving in a straight line. That is, only the camera's attitude parameter and position parameter relative to the vehicle need to be optimized.

[0088] Optionally, the motion parameters can be iteratively calculated using a parameter optimization function to obtain the iteratively calculated motion parameters. If the iteratively calculated motion parameters meet the preset conditions, then the iteratively calculated motion parameters are determined as the optimized motion parameters.

[0089] The preset condition is that the residual of the operating parameters is minimized. Specifically, minimizing the residual of the operating parameters can be, but is not limited to, any one of the following two cases:

[0090] Case 1: Based on the vehicle's motion state and the preset residual function set, at least one of the residuals of all operating parameters is minimized.

[0091] Case 2: Based on the vehicle's motion state and the preset residual function set, the residuals of all operating parameters are determined, and the sum of their squares is taken as the minimum square root.

[0092] In one alternative implementation, minimizing the residuals of the operating parameters means that during the iterative calculation process, according to the parameter optimization function, each iteration yields a calculation result. This result integrates the residuals of all operating parameters and is used to represent the accuracy of the calculated motion parameters. When the residuals are minimized, the obtained motion parameters are the most accurate.

[0093] For example, if the vehicle is moving in a straight line, the initial values ​​of the motion parameters can be calculated for the first time using a parameter optimization function, resulting in a calculation result A0. Based on the calculation result A0, the initial values ​​of the motion parameters can be calculated in reverse to obtain the motion parameters after the first calculation.

[0094] The parameter optimization function can be used to perform a second calculation on the motion parameters after the first calculation, resulting in a second calculation result A1. Based on the calculation result A1, the motion parameters after the first calculation can be calculated in reverse to obtain the second calculation result.

[0095] The parameter optimization function can be used to perform a third calculation on the motion parameters after the second calculation, resulting in the calculation result A2 after the third calculation.

[0096] If the calculation result A2 is the same as the calculation result A1, it means that in the third calculation, the calculation result A2 did not decrease further than the calculation result A1. This indicates that the residual of the calculation result A1 after the second calculation, i.e. the running parameters, has been minimized. Therefore, the motion parameters obtained from the second calculation can be determined as the optimized motion parameters.

[0097] In this embodiment, the vehicle's operating parameters can be determined based on its motion parameters. By analyzing the vehicle's motion state and a preset set of residual functions, the residual values ​​of the operating parameters are obtained. Then, the motion parameters are adjusted using a parameter optimization function to obtain optimized motion parameters. In this process, the motion parameters can be optimized by analyzing the vehicle's motion state during operation, thereby improving the efficiency and accuracy of motion parameter updates.

[0098] exist Figure 2Based on the illustrated embodiment, the following, in conjunction with Figure 3 The method for updating the motion parameters of the aforementioned vehicles will be explained in further detail.

[0099] Figure 3 This is a flowchart illustrating Embodiment 2 of the method for updating the motion parameters of a vehicle provided in this application.

[0100] Please see Figure 3 The method may include:

[0101] S301. Determine the vehicle's operating parameters based on the vehicle's motion parameters.

[0102] Optionally, the vehicle's operating parameters can be determined using the calculation formula shown in formula (3).

[0103]

[0104] In formula (3), R ex p represents the attitude parameters. ex Indicates the position parameter, v x The longitudinal velocity of the vehicle, v y The lateral velocity of a vehicle, v z w represents the vertical velocity of the vehicle. x w represents the longitudinal angular velocity of the vehicle. y w represents the lateral angular velocity of the vehicle. z V represents the vertical angular velocity of the vehicle, V represents the speed of the camera, and w represents the three-degree-of-freedom angular velocity of the camera.

[0105] Optional, This represents the transpose of the attitude parameters. Since attitude parameters are a special form of orthogonal matrices, the transpose of the attitude parameters is the inverse matrix of the attitude parameters. By transposing the attitude parameters, the rotation direction of the attitude parameters can be reversed, thus transforming the camera's running speed and three-degree-of-freedom angular velocity into the vehicle coordinate system, obtaining the vehicle's longitudinal velocity v in the vehicle coordinate system. x The lateral velocity v of the vehicle y The vertical velocity v of the vehicle z The longitudinal angular velocity w of the vehicle x lateral angular velocity w of the vehicle y and the vehicle's vertical angular velocity w z .

[0106] The operating speed V of the camera can be determined by the calculation formula shown in formula (5).

[0107]

[0108] In formula (5), p i p represents the camera position in the i-th frame. i-1 Let represent the camera position in frame i-1, and Δt represent the time difference between frame i and frame i-1.

[0109] Optionally, the camera position in the i-th frame can be the camera position at the current moment, and the camera position in the (i-1)-th frame can be the camera position at the previous moment. The previous moment can be a moment adjacent to the current moment or an earlier moment. Depending on the actual needs, those skilled in the art can set the time interval between the i-th frame and the (i-1)-th frame.

[0110] The camera position reflects the exact point of the camera in the reference coordinate system, which can be the world coordinate system or the initial coordinate system. The initial coordinate system can be a coordinate system set at a certain moment, for example, the initial moment can be the moment when the camera starts capturing frame images.

[0111] The camera position can be determined using frame images captured by the camera. In practice, visual-inertial odometry (VIO) can be used, combining frame images captured by the camera with acceleration and angular velocity data recorded by the inertial measurement unit, and determining the camera position through feature extraction, matching, and fusion.

[0112] The three-degree-of-freedom angular velocity w of the camera can be obtained through the measurement data of the gyroscope. The gyroscope can be bound to the camera, so that its measurement data can be regarded as the three-degree-of-freedom angular velocity of the camera.

[0113] For example, the longitudinal velocity v of the vehicle can be determined based on the initial values ​​of the motion parameters. x0 The lateral velocity v of the vehicle y0 The vertical velocity v of the vehicle z0 The longitudinal angular velocity w of the vehicle x0 lateral angular velocity w of the vehicle y0 and the vehicle's vertical angular velocity w z0 The initial values ​​of the motion parameters include the initial values ​​R of the attitude parameters. ex0 and the initial value p of the position parameter ex0 The subscript 0 indicates the initial state.

[0114] S302. Obtain the target residual function set corresponding to the motion state from the preset residual function set.

[0115] In this step, the target residual function set corresponding to the motion state can be obtained from the preset residual function set based on the vehicle's motion state.

[0116] In one alternative implementation, the preset set of residual functions can be determined based on the number of motion parameters.

[0117] For example, the preset set of residual functions may include a longitudinal velocity residual function determined based on the vehicle's longitudinal velocity, a lateral velocity residual function determined based on the vehicle's lateral velocity, a vertical velocity residual function determined based on the vehicle's vertical velocity, a longitudinal angular velocity residual function determined based on the vehicle's longitudinal angular velocity, a lateral angular velocity residual function determined based on the vehicle's lateral angular velocity, and a vertical angular velocity residual function determined based on the vehicle's vertical angular velocity.

[0118] Optionally, based on the vehicle's motion state being straight-ahead, a target residual function set corresponding to the straight-ahead state can be obtained. The target residual function set may include longitudinal velocity residual function, lateral velocity residual function, and vertical velocity residual function.

[0119] For example, based on the vehicle's motion state being straight-line, target residual function set 1 can be obtained. Target residual function set 1 can be represented by the following formula:

[0120]

[0121] F2=k2·(v y -0)

[0122] F3=k3·(v z -0)

[0123] In the above formula, the longitudinal wheel speed factor k1 can be determined by the noise of the wheel speed sensor. In specific implementation, k1 can be determined by any of the following methods.

[0124] Method 1: Determine k1 using the noise level of the wheel speed sensor at the time of manufacture. The noise level at the time of manufacture refers to the noise level measured during testing after the sensor is manufactured but before it is installed on the vehicle.

[0125] For example, if the noise level E1 of the vehicle's left rear wheel speed sensor is 0.04 km / h at the time of manufacture, and the noise level E2 of the vehicle's right rear wheel speed sensor is 0.06 km / h at the time of manufacture, then k1 can be determined using the calculation formula shown in formula (6):

[0126]

[0127] The calculated result k1 can be 20.

[0128] Method 2: During vehicle operation, k1 is determined by measuring the noise of the wheel speed sensor.

[0129] For example, during the vehicle's operation, the output data of the left and right rear wheel speed sensors can be recorded in real time over a preset period of time to obtain the average noise levels of the left and right rear wheel speed sensors. Then, k1 can be determined using formula (6). Here, E1 represents the average noise level of the left rear wheel speed sensor over the preset period of time, and E2 represents the average noise level of the right rear wheel speed sensor over the preset period of time.

[0130] Specifically, if the wheel speeds of both the left and right rear wheels are ideally 50 km / h, there will be no noise. The output data of the left rear wheel speed sensor within a preset time of 1 second are recorded as 50.02 km / h, 50.03 km / h, 50.05 km / h, and 50.06 km / h, and the output data of the right rear wheel speed sensor are recorded as 50.04 km / h, 50.05 km / h, 50.07 km / h, and 50.08 km / h. Therefore, the average noise E1 of the left rear wheel speed sensor within 1 second is determined to be 0.04 km / h, and the average noise E2 of the right rear wheel speed sensor is determined to be 0.06 km / h. k1 can be determined to be 20 using formula (6). The preset time can be set according to actual needs.

[0131] Regarding the right rear wheel speed W of the vehicle r and the speed W of the left rear wheel l The speed can be detected in real time by wheel speed sensors installed on the right and left rear wheels of the vehicle.

[0132] The lateral zero-speed constraint factor k2 can be determined based on the speed statistics obtained from the lateral speed test of the vehicle. Specifically, it can be determined when the vehicle is in a straight-moving state, based on the speed statistics obtained from the lateral speed test of the vehicle.

[0133] The vertical zero-speed constraint factor k3 can be determined based on the speed statistics obtained from the vertical speed test of the vehicle. Specifically, it can be determined when the vehicle is in a straight-moving state, based on the speed statistics obtained from the vertical speed test of the vehicle.

[0134] For example, when the vehicle is traveling in a straight line, a lateral speed test can be performed to obtain k2, and a vertical speed test can be performed to obtain k3. Specific data can be seen in Table 1.

[0135] Table 1

[0136] vehicle motion status Vehicle lateral speed (km / h) Vertical speed of the vehicle (km / h) Straight ahead G1 H1 Straight ahead G2 H2 Straight ahead G3 H3 Zero-velocity constraint factor k2 = 1 / [(G1 + G2 + G3) / 3] k3 = 1 / [(H1+H2+H3) / 3]

[0137] For example, if G1 is 0.01 km / h, G2 = 0.02 km / h, and G3 = 0.03 km / h, then the calculated result of k3 can be 50.

[0138] It should be noted that the number of lateral speed tests and vertical speed tests on the vehicle can be determined according to actual needs. In addition, the vehicle being tested can be the vehicle whose motion parameters need to be updated, or it can be a vehicle of the same type.

[0139] Optionally, based on the vehicle's motion state being a turning state, a target residual function set corresponding to the turning state can be obtained. The target residual function set may include longitudinal velocity residual function, lateral velocity residual function, vertical velocity residual function, and vertical angular velocity residual function.

[0140] For example, based on the vehicle's motion state being a turning state, target residual function set 2 can be obtained, which can be represented by the following formula:

[0141]

[0142] F2=k2·(v y -0)

[0143] F3=k3·(v z -0)

[0144]

[0145] In the above formula, the longitudinal wheel speed factor k1 can be determined by the noise of the wheel speed sensor. The specific implementation can refer to the process of determining k1 under straight-ahead conditions.

[0146] The lateral zero-speed constraint factor k2 can be determined based on the speed statistics obtained from lateral speed tests on the vehicle. Specifically, it can be determined while the vehicle is turning, based on the speed statistics obtained from lateral speed tests. The specific implementation can refer to the process of determining k2 under straight-moving conditions.

[0147] The vertical zero-speed constraint factor k3 can be determined based on the speed statistics obtained from the vehicle's vertical speed test. Specifically, it can be determined when the vehicle is turning, based on the speed statistics obtained from the vehicle's vertical speed test. The specific implementation can refer to the process of determining k3 under straight-moving conditions.

[0148] The vertical angular velocity factor k4 is determined based on the noise from the wheel speed sensor and the gyroscope when the vehicle is turning.

[0149] Specifically, the noise of a gyroscope is determined by the noise level at the time the gyroscope leaves the factory, or by measuring the noise of the gyroscope during vehicle operation.

[0150] For example, the noise of the left rear wheel speed sensor is E1, the noise of the right rear wheel speed sensor is E2, and the noise of the gyroscope is E3. Ignoring the influence of different noise dimensions on the weights, k4 can be determined using the calculation formula shown in formula (7):

[0151]

[0152] Where L0 represents the initial value of the wheel track parameter of the vehicle's rear wheels.

[0153] For example, the noise of the left rear wheel speed sensor is E1, the noise of the right rear wheel speed sensor is E2, and the noise of the gyroscope is E3. Considering the influence of different noise dimensions on the weights, k4 can be determined by the calculation formula shown in formula (8):

[0154]

[0155] Where R represents the radius of the vehicle wheel, the noise E3 of the gyroscope multiplied by the radius R of the vehicle wheel is to take into account the influence of different noise dimensions on the weight.

[0156] Optionally, the weighting factors in the residual functions of the target residual function set are different when the vehicle is in a straight-ahead state and a turning state.

[0157] Optionally, different weighting factors can refer to different numbers of weighting factors or different values ​​of weighting factors.

[0158] For example, the weighting factors that can be included when a vehicle is traveling straight are k1, k2, and k3, and the weighting factors that can be included when a vehicle is turning are k1, k2, k3, and k4.

[0159] For example, the value of k1 determined when the vehicle is going straight and when it is turning can be the same or different; the value of k2 determined when going straight can be greater than the value of k2 determined when turning, and the value of k3 determined when going straight can be greater than the value of k3 determined when turning.

[0160] Optionally, when adjusting and determining weighting factors based on vehicle motion states, the observability of data can be considered. By understanding the actual vehicle situation through observed data, different weighting factors can be assigned values. Specifically, k2 and k3 can be assigned larger weights when driving straight, and smaller weights when turning; k1 can be assigned the same non-zero weight in both straight and turning states; k4 can be left unassigned or assigned a value of zero when driving straight, and only assigned a value when turning. The values ​​of each weighting factor can be determined based on actual driving experience.

[0161] For example, based on the vehicle's motion state being a turning state, the target residual function set 2 corresponding to the turning state can be obtained from the preset residual function set.

[0162] S303. Determine the residuals of the operating parameters based on the operating parameters and the target residual function set.

[0163] In this step, the residuals of the operating parameters can be determined based on the operating parameters and the target residual function set determined by the vehicle's motion state.

[0164] In one optional implementation, if the motion state is a straight-line state, the residuals of the motion parameters are determined based on the motion parameters and the target residual function set, including:

[0165] Based on the vehicle's longitudinal speed, the longitudinal speed residual function is used to calculate the longitudinal speed residual.

[0166] Based on the vehicle's lateral velocity, the lateral velocity residual is calculated using the lateral velocity residual function;

[0167] Based on the vehicle's vertical velocity, the vertical velocity residual is calculated using the vertical velocity residual function.

[0168] The operating parameters include longitudinal velocity, lateral velocity, and vertical velocity; the target residual function set includes longitudinal velocity residual function, lateral velocity residual function, and vertical velocity residual function.

[0169] For example, based on the vehicle's motion state being straight-line, the target residual function set 1 can be determined, and the vehicle's longitudinal velocity v can be... x0 The lateral velocity v of the vehicle y0 and the vehicle's vertical velocity v z0 Substituting into the target residual function set 1, we can obtain the residuals F10 for longitudinal velocity, F20 for lateral velocity, and F30 for vertical velocity.

[0170] In another optional implementation, if the motion state is a turning state, then the residuals of the motion parameters are determined based on the motion parameters and the target residual function set, including:

[0171] Based on the vehicle's longitudinal speed, the longitudinal speed residual function is used to calculate the longitudinal speed residual.

[0172] Based on the vehicle's lateral velocity, the lateral velocity residual is calculated using the lateral velocity residual function;

[0173] Based on the vehicle's vertical velocity, the vertical velocity residual is calculated using the vertical velocity residual function.

[0174] Based on the vehicle's vertical angular velocity, the residual of the vertical angular velocity is calculated using the vertical angular velocity residual function;

[0175] The operating parameters include longitudinal velocity, lateral velocity, vertical velocity, and vertical angular velocity; the target residual function set includes longitudinal velocity residual function, lateral velocity residual function, vertical velocity residual function, and vertical angular velocity residual function.

[0176] For example, based on the vehicle's motion state being a turning state, the target residual function set 2 can be determined, and the vehicle's longitudinal velocity v can be... x0 The lateral velocity v of the vehicle y0 The vertical velocity v of the vehicle z0 The vehicle's vertical angular velocity w z0 Substituting the wheelbase L0 of the vehicle's rear wheels into the objective residual function set 2, we can obtain the residuals F10 for longitudinal velocity, F20 for lateral velocity, F30 for vertical velocity, and F40 for vertical angular velocity.

[0177] S304. The motion parameters are iteratively calculated using a parameter optimization function to obtain the iteratively calculated motion parameters.

[0178] In this step, a parameter optimization function can be established based on the residuals of the running parameters, and the initial values ​​of the motion parameters can be iteratively optimized to obtain the motion parameters after iterative calculation.

[0179] Optionally, the minimum square root of the sum of the squared residuals of the running parameters can be used as the criterion for determining whether the optimized motion parameters meet the preset conditions.

[0180] In one alternative implementation, a parameter optimization function 1, as shown in formula (9), can be established based on the vehicle's motion state being straight-ahead.

[0181]

[0182] Formula (9) can be understood as: when the vehicle's motion state is straight-line, solving for a set of parameters This minimizes the magnitude of the vector composed of residuals F1, F2, and F3. The magnitude of the vector can be determined by summing the squares of each residual and taking the square root.

[0183] For example, if the vehicle is moving in a straight line, the initial values ​​of the motion parameters {R} can be calculated using parameter optimization function 1. ex0 ,p ex0 The calculation result is A0. Based on the calculation result A0, the initial values ​​of the motion parameters {R} are... ex0 ,p ex0Perform reverse calculation to obtain the motion parameters {R} after the first calculation. ex1 ,p ex1}

[0184] In another alternative implementation, a parameter optimization function 2, as shown in formula (10), can be established based on the vehicle's motion state as a turning state:

[0185]

[0186] Similarly, formula (10) can be understood as: solving for a set of parameters This minimizes the magnitude of the vector composed of residuals F1, F2, F3, and F4.

[0187] For example, if the vehicle is in a turning state, the initial values ​​of the motion parameters {R} can be calculated using parameter optimization function 2. ex0 ,p ex0 The calculation result of L0} is B0. Based on the calculation result B0, the initial values ​​of the motion parameters {R} are... ex0 ,p ex0 The motion parameters {R} are calculated in reverse order from L0} to obtain the motion parameters after the first calculation. ex1 ,p ex1 ,L1}.

[0188] S305. If the motion parameters calculated iteratively meet the preset conditions, then the motion parameters calculated iteratively are determined as the optimized motion parameters.

[0189] In this step, the calculated motion parameters are obtained through iterative calculation based on the parameter optimization function. If the calculation results meet preset conditions, the iteratively calculated motion parameters are determined as the optimized motion parameters. The preset condition is that the residual of the running parameters is minimized.

[0190] For example, if the vehicle's motion state is straight-line, the motion parameters {R} can be obtained through parameter optimization function 1. ex1 ,p ex1 The calculation results A1 and motion parameters {R} ex2 ,p ex2 The calculation result A2 is the same as the calculation result A1. Therefore, it can be determined that the residual of the calculation result A1, i.e., the running parameters, has been minimized. Thus, the motion parameters {R} can be... ex1 ,p ex1 The optimized motion parameters are defined as follows. Among them, the motion parameters {R} are... ex1 ,p ex1} and motion parameters {R ex2 ,p ex2}same.

[0191] For example, if the vehicle is in a turning state, the motion parameters {R} can be obtained through parameter optimization function 2. ex1 ,p ex1 The calculation result B1 of L1} and the motion parameters {R} ex2 ,p ex2 The calculation result B2 of L2}. Since the calculation result B2 is the same as the calculation result B1, it can be determined that the residual of the calculation result B1, i.e., the running parameter, has been minimized. Therefore, the motion parameter {R} can be... ex1 ,p ex1 The optimized motion parameters are defined as follows. Among them, the motion parameters {R} are... ex1 ,p ex1 L1} and motion parameters {R} ex2 ,p ex2 The same applies to L2.

[0192] In one alternative implementation, the final determined motion parameters can be used as a reference only for the initial values ​​of the motion parameters. In actual use, motion state data collected in real time by on-board sensors (such as lidar and radar) can be used to perform secondary optimization and correction on the final motion parameters through data fusion and filtering algorithms (such as Kalman filtering), thereby further improving the safety and reliability of autonomous driving.

[0193] Optionally, multi-objective nonlinear optimization can be achieved using the Gauss-Newton method to obtain the optimized motion parameters. The specific implementation may include the following steps:

[0194] Step 1: Set optimization goals and preset conditions.

[0195] Optionally, taking the vehicle's motion state as the turning state, the optimized motion parameters are the camera's attitude parameters R relative to the vehicle. ex The position parameter p of the camera relative to the vehicle ex And the wheel track parameter L of the vehicle's rear wheels.

[0196] The preset condition is to minimize the residuals of the running parameters. For example, the preset condition is to minimize the magnitude of the vector composed of the residual terms.

[0197] Step 2: Initialize the optimization objective.

[0198] Optionally, the motion parameters included in the vehicle digital model can be used as the initial values ​​of the motion parameters to complete the initialization of the motion parameters. The initial values ​​of the motion parameters include the initial values ​​R of the attitude parameters. ex0 The initial value of the position parameter p ex0 And the initial value L0 of the wheel track parameter of the vehicle's rear wheels.

[0199] Step 3: Determine the residuals of the initial values ​​of the motion parameters using the target residual function set.

[0200] For example, the residuals of longitudinal velocity F10, lateral velocity F20, vertical velocity F30, and vertical angular velocity F40 can be obtained through the target residual function set 2.

[0201] Step 4: Determine whether the magnitude of the vector composed of the residual terms meets the preset conditions.

[0202] In this step, a convergence check can be performed to verify whether the magnitude of the vector composed of the residual terms meets a preset condition. If the preset condition is met, the optimization process ends, and the final motion parameters are output.

[0203] Step 5: Perform partial derivative operations on the residual function to obtain the Jacobian matrix.

[0204] Optionally, if the magnitude of the vector composed of the residual terms does not meet a preset condition, partial derivative operations can be performed on the residual function to obtain the Jacobian matrix. The Jacobian matrix contains the first-order partial derivatives of the residual function with respect to each motion parameter, describing the changes of the residual function near the current motion parameter estimation point. By using the Jacobian matrix, a nonlinear problem can be approximated as a linear problem, thereby iteratively updating the initial values ​​of the motion parameters and gradually approaching the optimal solution.

[0205] It should be noted that the operating parameters in the target residual function group 2 are determined by the motion parameters. Therefore, the operating parameters can be considered as intermediate variables that change with the motion parameters. Thus, the significance of performing partial derivative calculations on the residual function lies in performing partial derivative calculations on the motion parameters of the optimization target.

[0206] Step 6: Calculate the update step size.

[0207] In this step, the update step size for the motion parameters can be calculated based on the Jacobian matrix obtained in the previous step. Specifically, the gradient vector g can be calculated using the vector composed of the Jacobian matrix and the residual terms. Then, the Hessian matrix H is approximated using the Jacobian matrix. Next, the system of linear equations is solved using the gradient vector g and the Hessian matrix H to obtain the update step size D.

[0208] The formula for the linear equation system is: H*D = -g; -g represents the negative value of the gradient vector g.

[0209] Step 7: Iteratively update motion parameters using the update step size.

[0210] In this step, the updated motion parameters are used as new initial values, and the process returns to step ③ to recalculate the residuals and determine whether the preset conditions are met. If the preset conditions are met, the optimization process ends, and the final motion parameters are output. If the preset conditions are not met, the iteration continues, repeating steps ③ to ⑥ until the preset conditions are met.

[0211] Step 8: Output the optimized motion parameters.

[0212] In this step, when the magnitude of the vector composed of the residual terms meets a preset condition, the current motion parameters are output as the final motion parameters. The final motion parameters include the camera's attitude parameters relative to the vehicle, the camera's position parameters relative to the vehicle, and the wheelbase parameters of the vehicle's rear wheels.

[0213] In an optional implementation, the preset condition can also be that when the maximum number of iterations is reached, the current motion parameters are output as the final motion parameters.

[0214] Optionally, after determining the optimized motion parameters, the motion parameters can be periodically optimized during the subsequent driving process of the vehicle. Alternatively, it can be determined whether the optimized parameters need to be optimized again based on the vehicle's motion state and pre-established constraints. If it is determined that the motion parameters need to be optimized again, the motion parameters can be optimized again according to the vehicle motion parameter update method provided in the above embodiments.

[0215] The constraints may include, but are not limited to, any one or a combination of the following constraints:

[0216] Constraint 1: The lateral velocity and vertical velocity of the vehicle, obtained from the estimated camera attitude parameters and position parameters relative to the vehicle coordinate system, as well as the estimated camera velocity, should be zero.

[0217] Constraint 2: The longitudinal velocity of the vehicle, obtained from the estimated camera attitude parameters and position parameters relative to the vehicle coordinate system, and the estimated camera velocity, should be equal to the average value of the rear wheel velocities.

[0218] Constraint 3: The angular velocity of the camera, calculated based on the estimated attitude parameters and position parameters of the camera relative to the vehicle coordinate system, the difference in speed between the two rear wheels of the vehicle, and the wheelbase parameter, should be equal to one component of the angular velocity output by the gyroscope. For example, this component could be the vertical angular velocity of the vehicle output by the gyroscope.

[0219] For example, after determining the optimized motion parameters, during the subsequent driving process of the vehicle, if the vehicle's motion state is a straight line, and the estimated camera's attitude parameters, position parameters, and estimated camera speed result in the vehicle's lateral velocity and vertical velocity being non-zero, it can be determined that the motion parameters need to be optimized again. Then, the motion parameters can be optimized again according to the vehicle motion parameter update method provided in the above scheme.

[0220] In this embodiment, the vehicle's operating parameters can be determined based on its motion parameters. Then, a target residual function set corresponding to the vehicle's motion state is obtained from a preset residual function set. Next, the residuals of the operating parameters are calculated using the operating parameters and the target residual function set. The motion parameters are iteratively calculated using a parameter optimization function to obtain the iteratively calculated motion parameters. If the iteratively calculated motion parameters meet preset conditions, they are determined as the optimized motion parameters. In this process, the vehicle's motion parameters can be updated in real time based on its motion state, thereby improving the efficiency and accuracy of motion parameter updates.

[0221] Figure 4 A schematic diagram of the vehicle motion parameter updating device provided in this application. Please refer to [link / reference]. Figure 4 The vehicle's motion parameter updating device 10 includes:

[0222] The first processing module 11 is used to determine the vehicle's operating parameters based on the vehicle's motion parameters, the operating parameters including the vehicle's velocity and angular velocity in different dimensions of the vehicle coordinate system;

[0223] The acquisition module 12 is used to acquire the residuals of the operating parameters based on the vehicle's motion state and a preset set of residual functions. The set of residual functions includes residual functions used to calculate the magnitude of the error between the actual value and the expected value of different operating parameters.

[0224] The second processing module 13 is used to optimize the motion parameters according to the parameter optimization function to obtain the optimized motion parameters. The parameter optimization function is established based on the residuals of the motion parameters.

[0225] The vehicle motion parameter updating device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0226] In one possible implementation, the acquisition module 12 is specifically used for:

[0227] Obtain the target residual function set corresponding to the motion state from the preset residual function set;

[0228] The residuals of the operating parameters are determined based on the operating parameters and the target residual function set.

[0229] In one possible implementation, if the motion state is a straight-line state, then the acquisition module 12 is specifically used for:

[0230] Based on the longitudinal speed of the vehicle, the residual of the longitudinal speed is calculated using the longitudinal speed residual function;

[0231] Based on the lateral velocity of the vehicle, the residual of the lateral velocity is calculated using the lateral velocity residual function;

[0232] Based on the vertical velocity of the vehicle, the residual of the vertical velocity is calculated using the vertical velocity residual function;

[0233] The operating parameters include the longitudinal velocity, the lateral velocity, and the vertical velocity; the target residual function set includes the longitudinal velocity residual function, the lateral velocity residual function, and the vertical velocity residual function.

[0234] In one possible implementation, if the motion state is a turning state, then the acquisition module 12 is specifically used for:

[0235] Based on the longitudinal speed of the vehicle, the residual of the longitudinal speed is calculated using the longitudinal speed residual function;

[0236] Based on the lateral velocity of the vehicle, the residual of the lateral velocity is calculated using the lateral velocity residual function;

[0237] Based on the vertical velocity of the vehicle, the residual of the vertical velocity is calculated using the vertical velocity residual function;

[0238] Based on the vertical angular velocity of the vehicle, the residual of the vertical angular velocity is calculated using the vertical angular velocity residual function;

[0239] The operating parameters include the longitudinal velocity, the lateral velocity, the vertical velocity, and the vertical angular velocity; the target residual function set includes the longitudinal velocity residual function, the lateral velocity residual function, the vertical velocity residual function, and the vertical angular velocity residual function.

[0240] In one possible implementation, the motion parameters include the attitude parameters of the camera mounted in the vehicle relative to the vehicle, the position parameters of the camera relative to the vehicle, and the track width parameters of the vehicle's rear wheels.

[0241] In one possible implementation, the motion parameters are determined based on a digital model of the vehicle, which includes structural and motion parameters of the vehicle, and is used to simulate the performance of the vehicle under different motion states.

[0242] In one possible implementation, the second processing module 13 is specifically used for:

[0243] The motion parameters are iteratively calculated using the parameter optimization function to obtain the iteratively calculated motion parameters;

[0244] If the motion parameters calculated iteratively meet the preset conditions, then the motion parameters calculated iteratively are determined as the optimized motion parameters.

[0245] The preset condition is that the residual of the operating parameters is minimized.

[0246] In one possible implementation, the weighting factors in the residual functions of the target residual function set are different when the vehicle is in a straight-ahead state and a turning state.

[0247] The vehicle motion parameter updating device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0248] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Please refer to... Figure 5 The electronic device 20 may include a processor 21, a memory 22, and a communication interface 24. Exemplarily, the processor 21, the memory 22, and the communication interface 24 are interconnected via a bus 23.

[0249] The memory 22 stores computer-executed instructions;

[0250] The processor 21 executes the computer execution instructions stored in the memory 22, causing the processor 21 to execute the vehicle motion parameter update method provided in the above method embodiment.

[0251] The electronic device provided in this application embodiment can be an odometer device installed in a vehicle for calculating the vehicle's travel distance, or it can be a domain controller of the vehicle. It can execute the technical solution shown in the above method embodiment, and its implementation principle and beneficial effects are similar, so they will not be described again here.

[0252] Figure 6 This is a structural schematic diagram of the vehicle provided in an embodiment of this application. Please refer to... Figure 6 The vehicle 30 may include a vehicle body 31 and electronic devices 32.

[0253] Furthermore, a camera 33, a wheel speed sensor 34, and a gyroscope 35 can also be installed in the vehicle body 31. The camera 33 can acquire frame images of the environment during the vehicle's driving process, the wheel speed sensor 34 can measure the wheel speed of the vehicle's wheels, and the gyroscope 35 can acquire the three-degree-of-freedom angular velocity of the camera 33.

[0254] The electronic device 32 is used to implement the vehicle motion parameter update method provided in the above method embodiment.

[0255] Accordingly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the vehicle motion parameter update method described in the above method embodiments.

[0256] Accordingly, embodiments of this application may also provide a computer program product, including a computer program, which, when executed by a processor, can implement the vehicle motion parameter update method described in the above method embodiments.

[0257] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0258] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0259] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0260] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0261] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0262] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0263] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0264] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0265] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for updating the motion parameters of a vehicle, characterized in that, The method includes: Based on the vehicle's motion parameters, the vehicle's operating parameters are determined, including the vehicle's velocity and angular velocity in different dimensions of the vehicle coordinate system; Based on the vehicle's motion state and a preset set of residual functions, the residuals of the operating parameters are obtained. The set of residual functions includes residual functions used to calculate the magnitude of the error between the actual and expected values ​​of different operating parameters. The motion parameters are optimized according to the parameter optimization function to obtain the optimized motion parameters. The parameter optimization function is established based on the residuals of the motion parameters.

2. The method according to claim 1, characterized in that, The step of obtaining the residuals of the operating parameters based on the vehicle's motion state and a preset set of residual functions includes: Obtain the target residual function set corresponding to the motion state from the preset residual function set; The residuals of the operating parameters are determined based on the operating parameters and the target residual function set.

3. The method according to claim 2, characterized in that, If the motion state is a straight-line state, then determining the residuals of the motion parameters based on the motion parameters and the target residual function set includes: Based on the longitudinal speed of the vehicle, the residual of the longitudinal speed is calculated using the longitudinal speed residual function; Based on the lateral velocity of the vehicle, the residual of the lateral velocity is calculated using the lateral velocity residual function; Based on the vertical velocity of the vehicle, the residual of the vertical velocity is calculated using the vertical velocity residual function; The operating parameters include the longitudinal velocity, the lateral velocity, and the vertical velocity; the target residual function set includes the longitudinal velocity residual function, the lateral velocity residual function, and the vertical velocity residual function.

4. The method according to claim 2, characterized in that, If the motion state is a turning state, then determining the residuals of the motion parameters based on the motion parameters and the target residual function set includes: Based on the longitudinal speed of the vehicle, the residual of the longitudinal speed is calculated using the longitudinal speed residual function; Based on the lateral velocity of the vehicle, the residual of the lateral velocity is calculated using the lateral velocity residual function; Based on the vertical velocity of the vehicle, the residual of the vertical velocity is calculated using the vertical velocity residual function; Based on the vertical angular velocity of the vehicle, the residual of the vertical angular velocity is calculated using the vertical angular velocity residual function; The operating parameters include the longitudinal velocity, the lateral velocity, the vertical velocity, and the vertical angular velocity; the target residual function set includes the longitudinal velocity residual function, the lateral velocity residual function, the vertical velocity residual function, and the vertical angular velocity residual function.

5. The method according to any one of claims 1 to 4, characterized in that, The motion parameters include the attitude parameters of the camera installed in the vehicle relative to the vehicle, the position parameters of the camera relative to the vehicle, and the track width parameters of the vehicle's rear wheels.

6. The method according to any one of claims 1 to 4, characterized in that, The step of optimizing the motion parameters according to the parameter optimization function to obtain optimized motion parameters includes: The motion parameters are iteratively calculated using the parameter optimization function to obtain the iteratively calculated motion parameters; If the motion parameters calculated iteratively meet the preset conditions, then the motion parameters calculated iteratively are determined as the optimized motion parameters; The preset condition is that the residual of the operating parameters is minimized.

7. The method according to claim 2, characterized in that, The weighting factors in the residual functions of the target residual function set are different when the vehicle is in a straight-ahead state and a turning state.

8. An electronic device, characterized in that, include: Processor, memory, and communication interface; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the vehicle motion parameter update method as described in any one of claims 1 to 7.

9. A vehicle, characterized in that, It includes the vehicle body and the electronic equipment as described in claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the vehicle motion parameter update method according to any one of claims 1 to 7.

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