Control method of vehicle-mounted equipment, electronic equipment and vehicle

Abstract

According to the vehicle-mounted equipment control method, the electronic equipment and the vehicle, high-precision vehicle motion parameters and user control input are directly applied to vehicle-mounted equipment control, the control precision is improved, and then the following effect of the vehicle-mounted equipment is improved. And the control logic is upgraded from tracking historical data to pre-judging the future according to the effective motion parameters, so that prospective control is realized, and the delay of the vehicle-mounted equipment is reduced. The following control combining the equipment control data and the actual position of the equipment not only ensures high performance, but also ensures the safety of the system and the robustness in a complex environment, and enlarges the application scene of the following of the vehicle-mounted equipment.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a control method for on-board equipment, electronic equipment, and vehicle. Background Technology

[0002] Using drones to automatically follow and film moving vehicles, off-road vehicles, and race cars has become a popular application in film and television production, outdoor off-road recording, and other fields. Vehicle-mounted equipment uses external sensors (such as GPS and cameras) to detect changes in the vehicle's position and then tracks the vehicle. Essentially, this is a passive response mode, which suffers from problems such as large control delays and poor environmental adaptability. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a control method, electronic equipment and vehicle for vehicle-mounted equipment, which can predict the upcoming motion state of the vehicle by acquiring real-time, high-precision motion data and driver intentions from the vehicle chassis control system, and control the vehicle-mounted equipment in advance to reduce control delay.

[0004] To achieve the above objectives, this application provides a control method for an in-vehicle device, comprising: Obtain the vehicle motion parameters at the current moment, and perform validity checks and fault data completion on the vehicle motion parameters to obtain valid motion parameters; The predicted motion state of the vehicle at the next moment is predicted based on the driver's control input and the effective motion parameters. The predicted motion state is converted into equipment control data based on the expected relative position between the vehicle and the on-board equipment. The vehicle-mounted device is followed and controlled based on the device control data and the actual location of the device.

[0005] Optionally, predicting the vehicle's predicted motion state at the next moment based on the driver's control input and the effective motion parameters includes: The control input is converted into the desired control output based on the effective motion parameters. Based on the effective motion parameters, pose prediction is performed to obtain the predicted pose state; Based on the effective motion parameters and the desired dynamic state, driver control prediction is performed to obtain the predictive control output; Vertical motion prediction is performed based on the effective motion parameters to obtain the vertical motion state; The predicted motion state is obtained by constructing a state vector based on the predicted pose state, the predicted control output, and the vertical motion state.

[0006] Optionally, the desired control output includes a desired yaw rate and a desired longitudinal acceleration, and the step of converting the control input into a desired dynamic state based on the effective motion parameters includes: The desired yaw rate is determined based on the current longitudinal velocity in the effective motion parameters and the steering wheel angle in the control input. The desired longitudinal acceleration is determined based on the pedal control signal in the control input and the effective motion parameters.

[0007] Optionally, determining the desired longitudinal acceleration based on the pedal control signal in the control input and the effective motion parameters includes: The current engine speed is determined based on the current vehicle speed and current gear in the effective motion parameters. The desired acceleration is determined based on the accelerator pedal signal and the maximum driving acceleration in the pedal control signal. The desired deceleration is determined based on the brake pedal signal and the maximum braking deceleration in the pedal control signal. The sum of the desired acceleration and the desired deceleration is determined as the desired longitudinal acceleration.

[0008] Optionally, the device control data includes the desired device position and the desired device speed; the step of converting the predicted motion state into device control data based on the desired relative position between the vehicle and the on-board equipment includes: Construct a relative position vector based on the desired relative position; The relative position vector in the vehicle coordinate system is converted into the transformed relative position in the world coordinate system according to the preset rotation matrix; The sum of the predicted vehicle position in the predicted motion state and the transformed relative position is determined as the desired position of the device; The desired position of the device is determined by time-based differentiation, and the desired velocity of the device is determined based on the differentiation result and the predicted motion state.

[0009] Optionally, the step of following the vehicle-mounted device based on the device control data and the actual location of the vehicle-mounted device includes: The device position deviation is determined based on the desired device position in the device control data and the actual device position. The desired speed of the equipment in the equipment control data is determined as the feedforward quantity; The correction speed of the cascade proportional control output of the device position deviation is determined as the feedback quantity; The feedforward and feedback quantities are synthesized based on the device position deviation to obtain the synthesized device speed, and the vehicle-mounted equipment is controlled to perform vehicle following control based on the synthesized device speed.

[0010] Optionally, the step of synthesizing the feedforward quantity and the feedback quantity based on the device position deviation to obtain the synthesized device speed includes: In response to the device position deviation being less than a preset deviation threshold, a preset default weighting coefficient is used to weight the feedforward quantity and the feedback quantity to obtain the synthesized device speed; wherein, the default weighting coefficient is a weighting coefficient corresponding to the feedforward quantity. In response to the device position deviation being greater than or equal to a preset deviation threshold, the duration for which the device position deviation is greater than or equal to the deviation threshold is determined; In response to the duration being less than or equal to a preset duration threshold, the default weighting coefficient is reduced to obtain a corrected weighting coefficient, and the feedforward quantity and the feedback quantity are weighted and calculated according to the corrected weighting coefficient to obtain the synthesis device speed; In response to the duration being less than or equal to a preset duration threshold, the feedback quantity is determined as the speed of the synthesis device.

[0011] Based on the same inventive concept, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method described above when executing the computer program.

[0012] Based on the same inventive concept, this application also provides a vehicle including the electronic equipment described above.

[0013] As can be seen from the above, the vehicle-mounted device control method, electronic equipment, and vehicle provided in this application can acquire the vehicle motion parameters at the current moment, perform validity checks and fault data completion on the vehicle motion parameters to obtain valid motion parameters; predict the vehicle's predicted motion state at the next moment based on the driver's control input and the valid motion parameters; convert the predicted motion state into equipment control data based on the expected relative position between the vehicle and the vehicle-mounted device; and perform follow control on the vehicle-mounted device based on the equipment control data and the actual position of the device. Applying high-precision vehicle motion parameters and user control input directly to the vehicle-mounted device control improves control accuracy and thus enhances the following effect of the device. Upgrading the control logic from tracking historical data to predicting the future based on valid motion parameters achieves forward-looking control and reduces the latency of the vehicle-mounted device. The follow control combining equipment control data and the actual position of the device ensures both high performance and system safety and robustness in complex environments, expanding the applicable scenarios for the vehicle-mounted device's following function. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of the control method for the vehicle-mounted device according to an embodiment of this application; Figure 2 This is a schematic diagram of the control device for the vehicle-mounted equipment according to an embodiment of this application; Figure 3 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0017] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] In this article, it is important to understand that any number of elements in the accompanying figures is for illustrative purposes and not for limitation, and any naming is for distinction only and has no limiting meaning.

[0019] Based on the above background description, the following situations also exist in the related technologies: Taking vehicle-mounted drones as an example, the related technologies for following control of vehicle-mounted drones include GPS-based following schemes and vision-based following schemes.

[0020] For GPS-based tracking solutions, vehicle-mounted drones and vehicles track each other by sharing GPS location information. However, GPS only provides historical location information of the vehicle, and the drone can only respond with a relatively "lagging" follow-up. That is, the vehicle moves first, and the vehicle-mounted drone follows and flies alongside the vehicle based on the changes in the positioning information generated after the vehicle moves. This lagging follow-up method results in a large delay when the vehicle-mounted drone is following and flying alongside the vehicle. Furthermore, the vehicle-mounted drone cannot predict the vehicle's next turning or acceleration actions when following and flying alongside the vehicle. In scenarios where the vehicle's speed and attitude change drastically (such as off-road scenarios), the following effect is poor.

[0021] For vision-based tracking solutions, drones use onboard cameras to identify and track vehicles. However, these cameras are affected by weather conditions, limiting their applicability to unobstructed driving scenarios in good weather, resulting in poor adaptability. Furthermore, vision recognition requires significant computational resources, and trajectory prediction based on visual data takes considerable time, making low-latency follow-up impossible and leading to poor tracking performance.

[0022] It can be seen that in related technologies, vehicle-mounted drones perceive the "already occurred" positional changes of vehicles through external sensors (GPS, cameras) and then perform "lagging" tracking, resulting in a large following delay and being affected by the environment, leading to poor environmental adaptability.

[0023] The control method, electronic equipment, and vehicle for in-vehicle equipment provided in this application directly apply high-precision vehicle motion parameters and user control inputs to the control of the in-vehicle equipment, improving control accuracy and thus enhancing the following performance of the in-vehicle equipment. The control logic is upgraded from tracking historical data to predicting the future based on effective motion parameters, achieving proactive control and reducing latency of the in-vehicle equipment. The combination of equipment control data and actual equipment position tracking control ensures high performance while guaranteeing system safety and robustness in complex environments, expanding the applicable scenarios for in-vehicle equipment tracking. The external passive response control strategy is transformed into an internal proactive prediction control strategy. The in-vehicle equipment directly obtains real-time, high-precision motion data and driver intentions from the vehicle chassis control system, predicts the vehicle's upcoming motion state, and controls the in-vehicle equipment accordingly in advance.

[0024] The control method for the vehicle-mounted equipment provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0025] In some embodiments, such as Figure 1 As shown, a control method for an in-vehicle device includes steps 101-104.

[0026] Step 101: Obtain the vehicle motion parameters at the current moment, and perform validity checks and fault data completion on the vehicle motion parameters to obtain valid motion parameters.

[0027] In practical implementation, vehicle motion parameters include longitudinal velocity v, steering wheel angle δ, yaw rate ω, longitudinal acceleration a, vehicle yaw angle θ, vehicle pitch angle θ_Pitch, vehicle vertical velocity v_z, and vehicle vertical acceleration a_z. These vehicle motion parameters are raw data directly obtained from the vehicle chassis domain by the vehicle's Controller Area Network (CAN) bus. To ensure that the data output to subsequent modules (state prediction modules) is continuous, reasonable, and reliable, validity checks and fault data completion are required for the vehicle motion parameters. Validity checks can identify invalid, erroneous, or unreliable data. Fault data completion enables the generation of alternative data using appropriate strategies when data problems are identified, ensuring continuous system operation.

[0028] Validity checks require examining vehicle motion parameters from different dimensions.

[0029] 1. Physical range check. Determine whether the vehicle's motion parameters are within a reasonable range.

[0030] For example, the longitudinal speed v can range from [-50 km / h, 300 km / h]. A negative value might represent reversing, but a positive value far exceeding engine capacity (such as 500 km / h) is clearly incorrect. Depending on the vehicle design, the steering wheel angle δ has a maximum limit, for example, [-540°, +540°]. Values ​​outside this range are invalid. The yaw rate ω is related to vehicle speed and the road surface adhesion coefficient. As a rule of thumb, the absolute value of ω generally does not exceed the theoretical value calculated from 0.8g / v (where g is the acceleration due to gravity). At a vehicle speed of 100 km / h, the maximum yaw rate is approximately on the order of ±15° / s. A value of 100° / s is impossible on ordinary roads. Longitudinal and lateral accelerations typically do not exceed 1g, and vertical acceleration does not change drastically on normal road surfaces.

[0031] 2. Rate of Change Check. This check examines whether the changes in vehicle motion parameters between consecutive time points are reasonable, and is used to identify signal jumps or glitches.

[0032] For example, it's impossible for a vehicle to accelerate from 0 to 100 km / h in 0.01 seconds. A maximum rate of change threshold can be set, such as 20 m / s³ (jerk). If (a_k - a_{k-1}) / Δt exceeds this threshold, the acceleration data is considered unreliable. The rate of change of steering wheel angle is also limited by the driver's hand speed and the capability of the power steering system; the rate of change can also be checked by setting a threshold.

[0033] 3. Signal Reliability Check. Vehicle sensors or control units typically indicate the reliability of their data through status bits in CAN messages. The corresponding signal status bits are directly parsed from the CAN message. For example, the message from the Electronic Stability Control (ESC) system might contain an ESP_Status field, indicating whether the provided yaw rate, wheel speed, and other data are valid, invalid, or in degraded mode.

[0034] 4. Multi-sensor consistency check. Cross-validation is performed using the physical relationships between data from multiple sensors on the vehicle.

[0035] For example: Vehicle speed consistency: Compare the speeds from the four wheel speed sensors, the output shaft speed from the drivetrain, and the speed from GPS. When driving straight without slippage, these should be largely consistent. If a wheel speed sensor reading differs significantly from the others, that sensor may be faulty or the wheel may be slipping.

[0036] Steering and yaw rate consistency: At low speeds, there exists a simplified relationship: ω = v × δ / L (where L is the wheelbase). Although this relationship is not precise at high speeds, there must be a strong correlation between ω, δ, and v. If a large steering wheel angle is detected but the yaw rate is zero (or vice versa), then at least one signal is faulty.

[0037] 5. Data Freshness Check. Check if the data is "expired". CAN bus communication may experience message loss or delays due to high network load or control unit restarts. Maintain a timestamp for each signal. If the difference between the current time and the timestamp exceeds a threshold (e.g., 100ms), the signal data is considered invalid.

[0038] For fault data completion strategies, data will be completed after a validity check identifies a problem, ensuring data continuity. The priority of fault data completion strategies is typically as follows: 1. Sensor redundancy replacement. This is the ideal compensation method. If data from a certain sensor fails, but other sensors can provide the same or similar information, the redundant data should be used first.

[0039] For example, if the yaw rate sensor fails, it can be estimated using a vehicle model based on the steering wheel angle δ and vehicle speed v: ω_estimated = v ×δ / (L + K × v²) (where K is the understeer gradient).

[0040] If a wheel speed sensor fails, it can be replaced by the average of the other three wheel speeds, or by using the speed output from the transmission system.

[0041] 2. Model prediction completion. When there are no directly redundant sensors, the value of the fault signal can be predicted based on other normal and relevant signals using the vehicle dynamics model.

[0042] For example: The longitudinal acceleration sensor 'a' fails. The longitudinal acceleration 'a' can be estimated using the vehicle's longitudinal dynamics model based on signals such as engine torque, transmission gear position, and brake pressure. Essentially, this involves constructing a simplified, real-time "virtual sensor."

[0043] 3. Hold Last Valid Value. For short-term signal loss or glitches, the simplest strategy is to use the last valid value of the signal as a replacement. A maximum hold time must be set (e.g., 2 seconds). After this time, due to the significant change in vehicle status, continuing to use the old value will introduce large errors, and a more advanced degradation strategy should be triggered. This method is suitable for signals that change relatively slowly, such as pitch and yaw angles.

[0044] 4. Historical data interpolation / extrapolation. If the time period of data loss can be identified, short-term extrapolation can be performed using the data change trend just before the data loss.

[0045] For example: the signal is valid at times t-1 and t, and is lost at time t+1. It can be assumed that the signal retains the value at time t at time t+1 (0th order retention), or that a first-order extrapolation is performed according to the rate of change of (value_t - value_{t-1}) / Δt.

[0046] After performing validity checks and fault data completion on the vehicle motion parameters obtained directly from the chassis domain, valid motion parameters are obtained. These valid motion parameters have high continuity and accuracy and can be directly used for calculations.

[0047] Step 102: Predict the vehicle's predicted motion state at the next moment based on the driver's control input and effective motion parameters.

[0048] In practice, the control input is the embodiment of the driver's intention, which is the control signal generated by the driver's operation of the vehicle. The predicted motion state is represented in the form of a state vector, where the state vector is defined as:

[0049] Where X, Y, and Z are the three-dimensional coordinates of the vehicle in the world coordinate system, with X being the abscissa, Y the ordinate, and Z the altitude. Taking k-1 as the current time and k as the next time, the state prediction model for predicting the vehicle's motion state in the next time step is as follows:

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] in, The prediction time step represents the unit time interval between the current and next moments. ω_desired is the desired yaw rate, derived from the steering wheel angle and the two-degree-of-freedom model formula in the control input. a_desired is the desired longitudinal acceleration, calculated from the accelerator pedal and brake pedal signals in the control input. τ_ω is the yaw response time constant (initial value 0.2s); τ_a is the acceleration response time constant (initial value 0.15s), both of which can be obtained through analysis of a large amount of real vehicle data. The control input can be expressed as:

[0059] The process of determining the predicted motion state can be broken down into multiple steps, and the process of determining the predicted motion state is shown in the following embodiment.

[0060] In some embodiments, predicting the vehicle's predicted motion state at the next moment based on the driver's control input and effective motion parameters includes: The control input is converted into the desired control output based on the effective motion parameters. Based on the effective motion parameters, the pose is predicted to obtain the predicted pose state. Based on the effective motion parameters and the desired dynamic state, driver control prediction is performed to obtain the predictive control output; Vertical motion is predicted based on effective motion parameters to obtain the vertical motion state; The predicted motion state is obtained by constructing a state vector based on the predicted pose state, predicted control output, and vertical motion state.

[0061] In practice, when performing state prediction, it is first necessary to analyze the driver's intention, including analyzing steering intention and longitudinal movement intention. The process of analyzing the driver's intention is shown in the following example.

[0062] In some embodiments, the desired control output includes a desired yaw rate and a desired longitudinal acceleration, and the control input is converted into a desired dynamic state based on effective motion parameters, including: The desired yaw rate is determined based on the current longitudinal velocity in the effective motion parameters and the steering wheel angle in the control input. The desired longitudinal acceleration is determined based on the pedal control signal and effective motion parameters in the control input.

[0063] In practice, interpreting the steering intent involves calculating the desired yaw rate ω_desired. The inputs for calculating the desired yaw rate are the steering wheel angle δ from the control inputs and the current longitudinal speed v from the effective vehicle operating parameters.

[0064] A linear two-degree-of-freedom vehicle model is used to calculate the desired yaw rate. This model establishes the relationship between the steering wheel angle and the vehicle's steady-state yaw rate. The linear two-degree-of-freedom vehicle model is as follows: ω_desired = (v / L) / (1 + K × v²) × (δ / i) Where L is the vehicle wheelbase, i is the steering system transmission ratio (the ratio of steering wheel angle to front wheel angle), and K is the stability factor, representing the vehicle's steering characteristics (e.g., understeer, neutral steering, oversteer). K > 0 indicates understeer, common in passenger cars, where ω_desired saturates with increasing speed at high speeds. K = 0 indicates neutral steering. K < 0 indicates oversteer, which is unstable. A linear two-degree-of-freedom vehicle model is used to represent the stable rotational state (ω_desired) the vehicle will eventually reach when the driver turns the steering wheel by a certain longitudinal speed v and the vehicle turns the steering wheel by an angle δ.

[0065] The process of interpreting the longitudinal motion intention is the same as the process of calculating the desired longitudinal acceleration a_desired, and the calculation process is shown in the following example.

[0066] In some embodiments, determining the desired longitudinal acceleration based on the pedal control signal and effective motion parameters in the control input includes: The current engine speed is determined based on the current vehicle speed and current gear in the valid motion parameters; The desired acceleration is determined based on the accelerator pedal signal and the maximum driving acceleration in the pedal control signal. The desired deceleration is determined based on the brake pedal signal and the maximum braking deceleration in the pedal control signal. The sum of the desired acceleration and the desired deceleration is defined as the desired longitudinal acceleration.

[0067] In practice, the inputs for the calculation are the accelerator pedal opening α_throttle, the brake pedal opening α_brake, the current vehicle speed v, and the current gear. The calculation models are the inverse engine model and the inverse braking model. The inverse engine model is used to analyze the accelerator pedal intention. Based on the accelerator pedal opening and engine speed (calculated from the current vehicle speed and current gear in the effective motion parameters), it looks up the maximum driving acceleration a_drive_max that the engine can provide in the current state through a mapping table (e.g., the engine universal characteristic curve). Then the desired acceleration a_desired_drive = α_throttle × a_drive_max. The inverse braking model is used to analyze the braking intention. Based on the brake pedal opening, it looks up the maximum braking deceleration a_brake_max through a table in the braking system model. Then the desired deceleration a_desired_brake = -α_brake × a_brake_max.

[0068] Finally, the desired deceleration and desired acceleration are synthesized to obtain the desired longitudinal acceleration a_desired = a_desired_drive + a_desired_brake. This realizes the transformation of the driver's physical operations (steering, pedal pressing) into a clear expectation of the vehicle's dynamic state.

[0069] After converting the control inputs into vehicle dynamic parameters, it is necessary to further predict the vehicle's predicted motion state at the next moment based on the desired longitudinal acceleration, desired yaw rate, and effective motion parameters. The predicted motion state is calculated using Kalman filtering, employing state transition equations to predict future states. The output is a state vector, where each variable fully describes the vehicle's pose and motion state in 3D space. Compared to pure GPS extrapolation, the state transition equations incorporate rich internal states.

[0070] Pose prediction includes position prediction and attitude prediction. Taking the state transition in the X direction as an example, the position prediction is:

[0071] in, This represents the displacement caused by the longitudinal velocity at the current moment. Decompose the velocity in the vehicle coordinate system onto the X-axis of the world coordinate system. This represents the displacement increment caused by the current acceleration. It is a uniformly accelerated motion model, which is more accurate than the constant velocity model.

[0072] Similarly, the position prediction in the Y direction is as follows:

[0073] The predicted position in the Z direction is:

[0074] Attitude prediction is:

[0075] It can be seen that the change in the vehicle's orientation angle is directly obtained by integrating the yaw rate.

[0076] Furthermore, a first-order lag model is introduced to respond to the driver's intentions. This is the most critical part of the prediction model, which transforms the prediction from an "open loop" to a "closed loop".

[0077] Because a vehicle is not an ideal system, it cannot instantly achieve the desired longitudinal acceleration a_desired and the desired yaw rate ω_desired during control. The engine, transmission, braking system, and tires all exhibit response delays.

[0078] This delay is simulated using a first-order inertial element, and the driver control predictions include yaw rate prediction, longitudinal acceleration prediction, and longitudinal velocity prediction.

[0079] The predicted yaw rate is as follows:

[0080] in, The yaw response time constant represents the deviation between the desired yaw rate and the actual yaw rate in the effective state parameters. (For example, 0.2 seconds) defines the response speed of vehicle yaw dynamics to steering wheel input. The smaller the yaw response time constant, the faster the response. Yaw rate prediction means that within each prediction time step Δt, the yaw rate ω does not jump directly to ω_desired, but approaches it exponentially. The speed of the approach is determined by the yaw response time constant, which conforms to the real vehicle dynamics and improves prediction accuracy.

[0081] Longitudinal acceleration prediction is

[0082] in, The acceleration response time constant represents the deviation between the desired longitudinal acceleration and the actual longitudinal acceleration in the effective state parameters. (For example, 0.15 seconds) defines the vehicle's response speed to the pedal signal, reflecting the response delay of the powertrain or braking system. The smaller the acceleration response time constant, the faster the response. Longitudinal acceleration prediction represents the longitudinal acceleration within each prediction time step Δt. It doesn't jump directly to Instead, it approximates the actual vehicle dynamics exponentially, with the approximation speed determined by the acceleration response time constant, which conforms to real vehicle dynamics and improves prediction accuracy.

[0083] The longitudinal velocity is predicted as follows:

[0084] Longitudinal velocity prediction represents the longitudinal velocity within each prediction time step Δt. It doesn't jump directly to Instead of directly predicting the vehicle's speed, the prediction approximates it linearly, with the approximation speed determined by the longitudinal acceleration. This approach aligns with real vehicle dynamics and improves prediction accuracy.

[0085] Furthermore, vertical motion prediction is also required, which includes vertical velocity prediction and vertical acceleration prediction. Vertical velocity prediction includes:

[0086] This study assumes that the vehicle's vertical velocity is primarily caused by the longitudinal motion component on the slope. If the road surface is flat, the predicted vertical velocity is close to zero.

[0087] Vertical acceleration is predicted as follows:

[0088] In this case, it is assumed that the vertical acceleration remains constant over a short period of time, or that a simple prediction is made based on the rate of change of the pitch angle.

[0089] The predicted motion state is the state vector constructed from the predicted pose state, predicted control output, and vertical motion state. This method offers lower latency compared to related technologies that use airborne sensors for prediction.

[0090] For example, the vehicle is traveling in a straight line at 50 km / h, and the driver suddenly turns the steering wheel quickly with the intention of turning left.

[0091] When using GPS-based tracking solutions in related technologies, the onboard equipment can only detect changes in GPS coordinates after the vehicle has already begun to deviate from its straight trajectory, and then begin to turn, resulting in a significant delay.

[0092] Taking a vehicle-mounted drone as an example, this embodiment calculates the desired yaw rate ω_desired as a positive value (indicating an imminent left turn) immediately upon a sudden increase in the steering wheel angle δ and longitudinal velocity. The state prediction model begins to operate; before the vehicle physically generates a significant yaw rate ω, ω_k in the model begins to gradually increase according to the first-order hysteresis model. This gradually increasing ω_k is immediately fed into the kinematics calculation module and converted into control data for the vehicle-mounted drone. Even before the vehicle begins or has started to move laterally, the vehicle-mounted drone has already begun to move forward and to the left based on the predicted ω_k to maintain the relative position between the vehicle and the drone. This achieves true following with virtually no delay.

[0093] Intent parsing translates driver input into the vehicle's desired dynamic state. Kinematic state prediction accurately predicts changes in the vehicle's state, not just its geometric position. By combining high-frequency, low-latency data from within the chassis (such as yaw rate ω) with driver intent signals, accurate prediction of the vehicle's motion state is achieved.

[0094] Step 103: Convert the predicted motion state into equipment control data based on the expected relative position between the vehicle and the on-board equipment.

[0095] In practice, if the predicted motion state is the object of the vehicle, and the vehicle-mounted drone is to be controlled based on the predicted motion state, it needs to be converted into equipment control data that can directly control the drone.

[0096] In some embodiments, the device control data includes the desired device position and the desired device speed; converting the predicted motion state into device control data based on the desired relative position between the vehicle and the onboard device includes: Construct a relative position vector based on the desired relative position; The relative position vector in the vehicle coordinate system is converted into the transformed relative position in the world coordinate system according to the preset rotation matrix; The sum of the predicted vehicle position and the converted relative position in the predicted motion state is determined as the desired position of the equipment. The desired position of the equipment is determined by time-based differentiation. Based on the differentiation result and the predicted motion state, the desired velocity of the equipment is determined.

[0097] In practice, the equipment control data is used to control the vehicle-mounted drone to fly in the world coordinate system and maintain a fixed relative position in the vehicle coordinate system, which is in relative motion with the vehicle.

[0098] The input for data transformation is the predicted motion state and the desired relative position vector. The desired relative position vector is constructed based on the desired relative position, which is a preset, fixed three-dimensional vector. The relative position vector defined in the vehicle coordinate system is as follows:

[0099] In this context, d_x represents the forward / backward offset, with a positive value indicating the drone follows the vehicle from behind (the most common orientation), and a negative value indicating it follows from the front. d_y represents the left / right offset, with a positive value indicating the drone follows the vehicle from the left, and a negative value indicating it follows from the right. A non-zero d_y value can achieve the classic "45-degree front-side" following angle. d_z represents the altitude offset, with a positive value indicating the drone is above the vehicle. This can be set to a fixed value or designed as a function that varies with vehicle speed (e.g., the higher the vehicle speed, the higher the flight altitude to ensure safety).

[0100] After determining the relative position vectors, the relative position vectors in the vehicle coordinate system are converted to the transformed relative positions in the world coordinate system according to a preset rotation matrix. During this conversion, d_x, d_y, and d_z cannot be simply added to the vehicle's X_k, Y_k, and Z_k, because the vehicle itself is rotating (yaw angle θ_k changes). Coordinate transformation must be performed using a rotation matrix. Taking only the yaw angle as an example, the rotation matrix is:

[0101] The rotation matrix R(θ) is used to transform a point from one coordinate system (vehicle coordinate system) to another (world coordinate system). The R(θ) rotation matrix is ​​a rotation about the Z-axis (vertical axis), meaning it only considers the yaw angle. The process of transforming the relative position using the rotation matrix is ​​as follows:

[0102] in, This represents the expected x-coordinate of the vehicle-mounted drone in the world coordinate system. This represents the desired ordinate of the vehicle-mounted drone in the world coordinate system. This represents the desired altitude coordinates of the vehicle-mounted drone in the world coordinate system.

[0103] The desired position of the device is differentiated over time. The parameters from the predicted motion state are then substituted into the derivative to obtain the desired velocity of the device.

[0104]

[0105]

[0106] in, Let represent the translation component along the horizontal axis, and let represent the velocity component of the vehicle's own motion in the X direction. To keep up with the vehicle, the onboard equipment must possess this base velocity. Let represent the rotation component along the horizontal axis. This represents the additional speed caused by the vehicle's rotation (ω_p).

[0107] The vertical axis represents the translation component, while the Y-axis represents the velocity component of the vehicle's own motion. In order to keep up with the vehicle, the onboard equipment must possess this baseline speed.

[0108] The rotational component along the vertical axis represents the additional velocity caused by the vehicle's rotation (ω_p).

[0109] This means that the vehicle's vertical movement in the opposite direction has almost no impact on the tracking performance of the onboard equipment, and no vertical changes are required.

[0110] Alternatively, if coordinate transformation under three-dimensional pose is considered, the complete 3D rotation matrix R( θ , φ , ψ )for:

[0111]

[0112]

[0113]

[0114] in, φ Indicates pitch angle, ψ Indicates the roll angle. Indicates rotation about the z-axis. Indicates rotation about the x-axis. To represent rotation about the y-axis, use the 3D rotation matrix R( θ , φ , ψ Transforming the relative position vector can fully account for the vehicle's pitch and roll attitude.

[0115] Step 104: Perform follow control on the vehicle-mounted equipment based on the equipment control data and the actual location of the vehicle-mounted equipment.

[0116] In some embodiments, following control of the vehicle-mounted device based on device control data and the actual location of the device includes: Determine the equipment position deviation based on the expected and actual equipment positions in the equipment control data. The desired speed of the equipment in the equipment control data is determined as the feedforward quantity; The correction speed of the cascade proportional control output of the equipment position deviation is determined as the feedback quantity; The feedforward and feedback quantities are synthesized based on the equipment position deviation to obtain the synthesized equipment speed, and the on-board equipment is controlled to perform vehicle following control based on the synthesized equipment speed.

[0117] In practice, the follower control adopts a feedforward + feedback composite control, and the control output is: Control output = α × feedforward + (1 α) × Feedback quantity Wherein, α is the feedforward weighting coefficient, the magnitude of which can be determined based on the predicted deviation of the on-board equipment state. The feedforward quantity represents the expected speed of the equipment calculated based on the predicted state; the feedback quantity represents the PID control output of the equipment position deviation, which is the deviation between the expected position and the actual position of the equipment. The synthesis of the feedforward and feedback quantities is shown in the following embodiment.

[0118] In some embodiments, the feedforward and feedback quantities are synthesized based on the device position deviation to obtain the synthesized device speed, including: In response to a device position deviation being less than a preset deviation threshold, the preset default weighting coefficients are used to weight the feedforward and feedback quantities to obtain the synthesized device speed; where the default weighting coefficients are the weighting coefficients corresponding to the feedforward quantity. In response to a device position deviation being greater than or equal to a preset deviation threshold, the duration for which the device position deviation is greater than or equal to the deviation threshold is determined; In response to a duration less than or equal to a preset duration threshold, the default weighting coefficient is reduced to obtain a corrected weighting coefficient. The feedforward and feedback quantities are then weighted according to the corrected weighting coefficient to obtain the synthesis device speed. In response to a duration less than or equal to a preset duration threshold, the feedback quantity is determined as the synthesis device speed.

[0119] In practical implementation, the feedforward variable directly sends the desired speed command to the onboard flight controller, essentially instructing the onboard equipment to "fly at this speed to keep up with the vehicle" the moment the vehicle begins to move. Using feedforward for UAV control has extremely low latency; it doesn't rely on reacting after errors occur but rather provides proactive control. The feedback variable, based on PID control, is used to reduce the deviation between the actual and target speeds, correcting speed errors caused by calculation errors or environmental factors. It overcomes various uncertainties, precisely stabilizing the onboard equipment at the desired position. In short, the feedforward variable represents the predicted speed, and the feedback variable corrects the feedforward variable.

[0120] When the device position deviation is less than the preset deviation threshold, it means that the deviation is very small and only slight correction is needed. The control output (synthesized device speed) obtained by weighting the feedforward and feedback quantities using a preset default weighting coefficient (close to 1) can achieve unmanned low-latency following.

[0121] When the device position deviation is greater than or equal to the preset deviation threshold, it indicates that the deviation is large and a certain amount of feedforward is needed for correction. The correction magnitude is measured based on the duration of the device position deviation being greater than or equal to the deviation threshold. When the duration is less than or equal to the preset duration threshold, the correction magnitude is smaller. The default weighting coefficient is reduced and the weight of the feedback quantity used for correction is increased to achieve speed correction and improve correction accuracy. The feedforward and feedback quantities are weighted and calculated based on the modified correction weighting coefficient to obtain a more accurate synthesized device speed.

[0122] When the duration is less than or equal to the preset duration threshold, it indicates that simply increasing the weight of the feedback quantity has a poor corrective effect, and the vehicle-mounted drone has deviated from the target trajectory for a long time. At this time, correcting the drone is the first control priority, and the default weighting coefficient is reduced to zero. The feedback quantity is the speed of the synthesized device. The control output of the vehicle-mounted drone is repaired with the maximum correction capability to ensure the accuracy of control.

[0123] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0124] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0125] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a control device for vehicle-mounted equipment.

[0126] refer to Figure 2 The control device for the vehicle-mounted equipment includes: The data acquisition module 10 is configured to: acquire the vehicle motion parameters at the current moment, and perform validity checks and fault data completion on the vehicle motion parameters to obtain valid motion parameters; The state prediction module 20 is configured to predict the vehicle's motion state at the next moment based on the driver's control input and effective motion parameters. The motion calculation module 30 is configured to convert the predicted motion state into equipment control data based on the expected relative position between the vehicle and the on-board equipment. The control execution module 40 is configured to perform follow control on the vehicle-mounted equipment based on the equipment control data and the actual location of the vehicle-mounted equipment.

[0127] Optionally, the state prediction module 20 is also configured to: The control input is converted into the desired control output based on the effective motion parameters. Based on the effective motion parameters, the pose is predicted to obtain the predicted pose state. Based on the effective motion parameters and the desired dynamic state, driver control prediction is performed to obtain the predictive control output; Vertical motion is predicted based on effective motion parameters to obtain the vertical motion state; The predicted motion state is obtained by constructing a state vector based on the predicted pose state, predicted control output, and vertical motion state.

[0128] Optionally, the state prediction module 20 is also configured to: The desired yaw rate is determined based on the current longitudinal velocity in the effective motion parameters and the steering wheel angle in the control input. The desired longitudinal acceleration is determined based on the pedal control signal and effective motion parameters in the control input.

[0129] Optionally, the state prediction module 20 is also configured to: The current engine speed is determined based on the current vehicle speed and current gear in the valid motion parameters; The desired acceleration is determined based on the accelerator pedal signal and the maximum driving acceleration in the pedal control signal. The desired deceleration is determined based on the brake pedal signal and the maximum braking deceleration in the pedal control signal. The sum of the desired acceleration and the desired deceleration is defined as the desired longitudinal acceleration.

[0130] Optionally, the motion calculation module 30 is also configured to: Construct a relative position vector based on the desired relative position; The relative position vector in the vehicle coordinate system is converted into the transformed relative position in the world coordinate system according to the preset rotation matrix; The sum of the predicted vehicle position and the converted relative position in the predicted motion state is determined as the desired position of the equipment. The desired position of the equipment is determined by time-based differentiation. Based on the differentiation result and the predicted motion state, the desired velocity of the equipment is determined.

[0131] Optionally, the control execution module 40 is also configured to: Determine the equipment position deviation based on the expected and actual equipment positions in the equipment control data. The desired speed of the equipment in the equipment control data is determined as the feedforward quantity; The correction speed of the cascade proportional control output of the equipment position deviation is determined as the feedback quantity; The feedforward and feedback quantities are synthesized based on the equipment position deviation to obtain the synthesized equipment speed, and the on-board equipment is controlled to perform vehicle following control based on the synthesized equipment speed.

[0132] Optionally, the control execution module 40 is also configured to: In response to a device position deviation being less than a preset deviation threshold, the preset default weighting coefficients are used to weight the feedforward and feedback quantities to obtain the synthesized device speed; where the default weighting coefficients are the weighting coefficients corresponding to the feedforward quantity. In response to a device position deviation being greater than or equal to a preset deviation threshold, the duration for which the device position deviation is greater than or equal to the deviation threshold is determined; In response to a duration less than or equal to a preset duration threshold, the default weighting coefficient is reduced to obtain a corrected weighting coefficient. The feedforward and feedback quantities are then weighted according to the corrected weighting coefficient to obtain the synthesis device speed. In response to a duration less than or equal to a preset duration threshold, the feedback quantity is determined as the synthesis device speed.

[0133] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0134] The apparatus described above is used to implement the control method of the corresponding vehicle-mounted equipment in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0135] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the control method of the vehicle-mounted device described in any of the above embodiments.

[0136] Figure 3This embodiment illustrates a more specific hardware structure of an electronic device. The device may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0137] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0138] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0139] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0140] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0141] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0142] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0143] The electronic devices described above are used to implement the control methods of the corresponding vehicle-mounted devices in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0144] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to execute the control method of the vehicle-mounted device as described in any of the above embodiments.

[0145] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0146] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the control method of the vehicle-mounted device as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0147] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a vehicle, including the control device of the electronic device or vehicle-mounted device of the above embodiments, and executes the control method of the vehicle-mounted device as described in any of the above embodiments through the electronic device or vehicle-mounted device of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0148] It is understood that before using the technical solutions of the various embodiments in this application, users will be informed of the type, scope of use, and usage scenarios of the personal information involved in an appropriate manner, and user authorization will be obtained.

[0149] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media performing the operations described in this application.

[0150] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0151] It is understood that the above notification and user authorization process is merely illustrative and does not limit the implementation of this application. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this application.

[0152] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application is limited to these examples; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0153] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0154] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0155] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the claims of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A control method for an on-board device, characterized in that, include: Obtain the vehicle motion parameters at the current moment, and perform validity checks and fault data completion on the vehicle motion parameters to obtain valid motion parameters; The predicted motion state of the vehicle at the next moment is predicted based on the driver's control input and the effective motion parameters. The predicted motion state is converted into equipment control data based on the expected relative position between the vehicle and the on-board equipment. The vehicle-mounted device is followed and controlled based on the device control data and the actual location of the device.

2. The method according to claim 1, characterized in that, The step of predicting the vehicle's predicted motion state at the next moment based on the driver's control input and the effective motion parameters includes: The control input is converted into the desired control output based on the effective motion parameters. Based on the effective motion parameters, pose prediction is performed to obtain the predicted pose state; Based on the effective motion parameters and the desired dynamic state, driver control prediction is performed to obtain the predictive control output; Vertical motion prediction is performed based on the effective motion parameters to obtain the vertical motion state; The predicted motion state is obtained by constructing a state vector based on the predicted pose state, the predicted control output, and the vertical motion state.

3. The method according to claim 2, characterized in that, The desired control output includes a desired yaw rate and a desired longitudinal acceleration. The step of converting the control input into a desired dynamic state based on the effective motion parameters includes: The desired yaw rate is determined based on the current longitudinal velocity in the effective motion parameters and the steering wheel angle in the control input. The desired longitudinal acceleration is determined based on the pedal control signal in the control input and the effective motion parameters.

4. The method according to claim 3, characterized in that, Determining the desired longitudinal acceleration based on the pedal control signal in the control input and the effective motion parameters includes: The current engine speed is determined based on the current vehicle speed and current gear in the effective motion parameters. The desired acceleration is determined based on the accelerator pedal signal and the maximum driving acceleration in the pedal control signal. The desired deceleration is determined based on the brake pedal signal and the maximum braking deceleration in the pedal control signal. The sum of the desired acceleration and the desired deceleration is determined as the desired longitudinal acceleration.

5. The method according to claim 1, characterized in that, The device control data includes the desired device position and desired device speed; the step of converting the predicted motion state into device control data based on the desired relative position between the vehicle and the onboard equipment includes: Construct a relative position vector based on the desired relative position; The relative position vector in the vehicle coordinate system is converted into the transformed relative position in the world coordinate system according to the preset rotation matrix; The sum of the predicted vehicle position in the predicted motion state and the transformed relative position is determined as the desired position of the device; The desired position of the device is determined by time-based differentiation, and the desired velocity of the device is determined based on the differentiation result and the predicted motion state.

6. The method according to claim 1, characterized in that, The step of following and controlling the vehicle-mounted device based on the device control data and the actual location of the vehicle-mounted device includes: The device position deviation is determined based on the desired device position in the device control data and the actual device position. The desired speed of the equipment in the equipment control data is determined as the feedforward quantity; The correction speed of the cascade proportional control output of the device position deviation is determined as the feedback quantity; The feedforward and feedback quantities are synthesized based on the device position deviation to obtain the synthesized device speed, and the vehicle-mounted equipment is controlled to perform vehicle following control based on the synthesized device speed.

7. The method according to claim 6, characterized in that, The step of synthesizing the feedforward quantity and the feedback quantity based on the device position deviation to obtain the synthesized device speed includes: In response to the device position deviation being less than a preset deviation threshold, a preset default weighting coefficient is used to weight the feedforward quantity and the feedback quantity to obtain the synthesized device speed; wherein, the default weighting coefficient is a weighting coefficient corresponding to the feedforward quantity. In response to the device position deviation being greater than or equal to a preset deviation threshold, the duration for which the device position deviation is greater than or equal to the deviation threshold is determined; In response to the duration being less than or equal to a preset duration threshold, the default weighting coefficient is reduced to obtain a corrected weighting coefficient, and the feedforward quantity and the feedback quantity are weighted and calculated according to the corrected weighting coefficient to obtain the synthesis device speed; In response to the duration being less than or equal to a preset duration threshold, the feedback quantity is determined as the speed of the synthesis device.

8. The method according to claim 1, characterized in that, The process of checking the validity of the vehicle motion parameters and completing fault data to obtain valid motion parameters includes: The vehicle motion parameters are checked for validity in multiple dimensions according to the preset inspection items, and the inspection results are obtained. In response to the inspection result indicating that there is no unreliable data in the vehicle motion parameters, the vehicle motion parameters are determined to be the valid motion parameters; In response to the inspection result indicating the presence of unreliable data in the vehicle motion parameters, alternative data for the unreliable data is generated according to a preset data completion strategy and the priority of the data completion strategy. The unreliable data in the vehicle motion parameters is then replaced with the corresponding alternative data to obtain the valid motion parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.

10. A vehicle, characterized in that, Including the electronic device as described in claim 9.

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