WO2023035234A1 - 车辆状态参数估计方法及装置 - Google Patents
车辆状态参数估计方法及装置 Download PDFInfo
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- the present application relates to the technical field of vehicle control, in particular to a method and device for estimating vehicle state parameters.
- the present application provides a vehicle state parameter estimation method and a vehicle state parameter estimation device, which can improve the estimation accuracy of the vehicle state parameters.
- the present application provides a method for estimating vehicle state parameters, the method comprising:
- the first process state x the first process covariance Q, the second process covariance Q(k) and the first measurement covariance R(k)
- a vehicle state parameter of the vehicle is determined.
- the application adaptively adjusts process covariance and measurement covariance based on driving state data, and then uses the adjusted process covariance and measurement covariance for vehicle state estimation, which can improve the accuracy of vehicle state parameters. Estimated accuracy.
- the driving state data includes one or more of the following: rate of change of lateral acceleration steering wheel speed Lateral acceleration a y , road surface adhesion coefficient ⁇ , wheel speed ⁇ FL of the front left wheel, wheel speed ⁇ FR of the front right wheel, wheel speed ⁇ RL of the rear left wheel or wheel speed ⁇ RR of the rear right wheel.
- the determining the second process covariance Q(k) according to the driving state data of the vehicle includes:
- the determining the first measurement covariance R(k) according to the driving state data of the vehicle includes:
- the first preset measurement covariance matrix is obtained as the first measurement covariance R(k);
- the first measurement covariance R(k) is determined according to the road surface adhesion coefficient ⁇ .
- the determining the first measurement covariance R(k) according to the road surface adhesion coefficient ⁇ includes:
- the first measured value y h of the vehicle sensor includes a measured value measured by a real sensor of the vehicle and a measured value obtained based on a virtual sensor of the vehicle, and the measured value of the virtual sensor of the vehicle is passed through a neural network.
- the network gets.
- This application introduces the results obtained by other estimation methods (such as state estimation based on kinematics, state estimation based on neural network, and state estimation based on vision) as the measurement value of "virtual sensor”, and the measurement value obtained based on real sensor measurement and the calculated measurement covariance are extended, and then the extended measurement value and measurement covariance are used for vehicle state estimation, which can further improve the estimation accuracy of the vehicle state.
- other estimation methods such as state estimation based on kinematics, state estimation based on neural network, and state estimation based on vision
- the first process state x includes one or more of the following:
- the first measured value y h includes one or more of the following:
- the vehicle state parameters include one or more of the following:
- the present application provides a device for estimating vehicle state parameters, the device comprising:
- a transceiver unit configured to acquire the driving state data of the vehicle, the first process state x and the first process covariance Q;
- a processing unit configured to determine a second process covariance Q(k) and a first measurement covariance R(k) according to the driving state data of the vehicle;
- the transceiver unit is used to obtain the first measured value y h of the vehicle sensor
- the processing unit is configured to use the first measured value y h , the first process state x, the first process covariance Q, the second process covariance Q(k) and the first
- the measurement covariance R(k) determines a vehicle state parameter of the vehicle.
- the driving state data includes one or more of the following: rate of change of lateral acceleration steering wheel speed Lateral acceleration a y , road surface adhesion coefficient ⁇ , wheel speed ⁇ FL of the front left wheel, wheel speed ⁇ FR of the front right wheel, wheel speed ⁇ RL of the rear left wheel or wheel speed ⁇ RR of the rear right wheel.
- the processing unit is used for:
- the processing unit is used for:
- the first preset measurement covariance matrix is obtained as the first measurement covariance R(k);
- the first measurement covariance R(k) is determined according to the road surface adhesion coefficient ⁇ .
- the processing unit is used for:
- the first measured value y h of the vehicle sensor includes a measured value measured by a real sensor of the vehicle and a measured value obtained based on a virtual sensor of the vehicle, and the measured value of the virtual sensor of the vehicle is passed through a neural network.
- the network gets.
- the first process state x includes one or more of the following:
- the first measured value y h includes one or more of the following:
- the vehicle state parameters include one or more of the following:
- the present application provides a device for estimating vehicle state parameters.
- the device may be a terminal device, or a device in the terminal device, or a device that can be used in conjunction with the terminal device.
- the device for estimating vehicle state parameters may also be a system-on-a-chip.
- the device for estimating vehicle state parameters can implement the method described in the first aspect.
- the functions of the vehicle state parameter estimation device can be implemented by hardware, or can be implemented by executing corresponding software on hardware.
- the hardware or software includes one or more units or modules corresponding to the above functions.
- the unit or module can be software and/or hardware.
- the present application provides a device for estimating vehicle state parameters.
- the device may be a terminal device.
- the device for estimating vehicle state parameters includes a processor and a transceiver, and the processor and the transceiver are used to execute at least one A computer program or instruction stored in the memory, so that the device implements the method according to any one of the first aspect.
- the present application provides a device for estimating a vehicle state parameter, which may be a terminal device, and the device for estimating a vehicle state parameter includes a processor, a transceiver, and a memory.
- the processor, the transceiver and the memory are coupled; the processor and the transceiver are used to implement the method according to any one of the first aspect.
- the present application provides a computer-readable storage medium, in which a computer program or instruction is stored, and when the computer program or instruction is executed by a computer, the method according to any one of the first aspect is implemented.
- the present application provides a computer program product including instructions, the computer program product including computer program code, when the computer program code is run on a computer, to implement any one of the methods in the first aspect.
- a chip system in an eighth aspect, includes a processor, and may further include a memory, for implementing any aspect and any possible design method in the aforementioned first aspect.
- the system-on-a-chip may consist of chips, or may include chips and other discrete devices.
- FIG. 1 is a schematic diagram of vehicle state estimation based on a wheel speed sensor provided by an embodiment of the present application
- FIG. 2 is a schematic diagram of multi-sensor based vehicle state estimation provided by an embodiment of the present application
- Fig. 3 is a schematic diagram of the architecture of the vehicle state parameter estimation system provided by the embodiment of the present application.
- FIG. 4 is a schematic flowchart of a method for estimating vehicle state parameters provided by an embodiment of the present application
- FIG. 5 is a schematic diagram of determining the second process covariance Q(k) based on the driving state data provided by the embodiment of the present application;
- FIG. 6 is a schematic diagram of determining the first measurement covariance R(k) based on driving state data provided by an embodiment of the present application;
- Fig. 7 is a schematic flow chart of another vehicle state parameter estimation method provided by the embodiment of the present application.
- Fig. 8 is a schematic structural diagram of a device for estimating vehicle state parameters provided by an embodiment of the present application.
- FIG. 9 is a schematic structural diagram of another vehicle state parameter estimation device provided by an embodiment of the present application.
- FIG. 10 is a schematic structural diagram of a chip provided by an embodiment of the present application.
- words such as “exemplary” or “for example” are used to mean an example, illustration or illustration. Any embodiment or design described herein as “exemplary” or “for example” is not to be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as “exemplary” or “such as” is intended to present related concepts in a concrete manner.
- Unscented Kalman filter (unscented kalman filter, UKF): UKF is another way to solve nonlinear Kalman filtering. It uses unscented transformation to solve the problem of nonlinear transformation of probability distribution. The unscented Kalman filter does not need to calculate the Jacobian matrix like the extended Kalman filter, and can obtain more accurate nonlinear processing effects with roughly the same amount of calculation.
- IMU Inertial measurement unit
- Steering angle sensor (steering angle sensor, SAS): It is used to measure the rotation angle of the steering wheel when the vehicle is turning, and is mainly installed in the steering column under the steering wheel.
- Wheel speed sensor A sensor used to measure the speed of a car wheel.
- the commonly used wheel speed sensors mainly include magnetoelectric wheel speed sensors and Hall wheel speed sensors.
- Master cylinder pressure sensor A sensor used to measure the pressure in the master cylinder.
- Advanced driver assistance system uses various sensors (millimeter wave radar, laser radar, camera and satellite navigation) installed on the car to sense the surrounding environment at any time during the driving process of the car. Environment, collect data, identify, detect and track static and dynamic objects, and combine navigation map data to perform systematic calculation and analysis, so that drivers can be aware of possible dangers in advance and effectively increase driving comfort. and security.
- sensors millimeter wave radar, laser radar, camera and satellite navigation
- FIG. 1 is a schematic diagram of vehicle state estimation based on wheel speed sensors provided by an embodiment of the present application.
- the estimation of the side slip angle of the vehicle center of mass and the road surface adhesion coefficient can be obtained indirectly through four estimations, and the estimation accuracy will continue to decrease with each estimation. This is because each estimate introduces some estimation error.
- parameters such as the wheel speeds of the front left wheel, front right wheel, rear left wheel, and rear right wheel of the vehicle can be obtained based on WSS measurements, which are respectively denoted as ⁇ FL , ⁇ FR , ⁇ RL , ⁇ RR , and for convenience of description, referred to as WSS measured value.
- WSS measured value for convenience of description
- the longitudinal vehicle speed and longitudinal acceleration etc. can be estimated based on the measured value of WSS.
- the slip rate and yaw rate can be estimated based on the longitudinal vehicle speed
- the lateral acceleration can be estimated based on the measured values of the longitudinal vehicle speed and WSS
- the master cylinder pressure value can be estimated based on the longitudinal acceleration.
- the lateral vehicle speed is estimated based on the yaw rate and the lateral acceleration
- the vertical force and the lateral force are respectively estimated based on the lateral acceleration
- the longitudinal force is estimated based on the lateral acceleration and the pressure value of the master cylinder.
- the side slip angle is estimated based on the longitudinal vehicle speed obtained by the first estimation and the lateral vehicle speed obtained by the third estimation, and the road surface adhesion coefficient is obtained based on the vertical force, lateral force and longitudinal force estimation obtained by the third estimation.
- FIG. 2 is a schematic diagram of multi-sensor based vehicle state estimation provided by an embodiment of the present application.
- the wheel speed sensor i.e. SAS
- the inertial measurement unit i.e. IMU
- the master cylinder pressure sensor i.e. MPS
- the side slip angle of the vehicle center of mass and the road surface adhesion coefficient need to pass two
- the reduction of the number of estimates can improve the estimation accuracy of the sideslip angle of the center of mass and the coefficient of adhesion of the road surface.
- parameters such as the wheel speeds of the front left wheel, front right wheel, rear left wheel, and rear right wheel of the vehicle can be obtained based on WSS measurements, which are respectively denoted as ⁇ FL , ⁇ FR , ⁇ RL , ⁇ RR , and for convenience of description, referred to as WSS measured value.
- WSS measured value parameters such as the wheel speeds of the front left wheel, front right wheel, rear left wheel, and rear right wheel of the vehicle can be obtained based on WSS measurements, which are respectively denoted as ⁇ FL , ⁇ FR , ⁇ RL , ⁇ RR , and for convenience of description, referred to as WSS measured value.
- Parameters such as the steering wheel angle are obtained based on the SAS measurement, and are referred to as the SAS measurement value for convenience of description.
- Parameters such as longitudinal acceleration, lateral acceleration, and yaw rate are obtained based on IMU measurement. For convenience of description, they are referred to as IMU measurement values.
- Parameters such as the master cylinder pressure value are obtained based on the MPS measurement, which are referred to as the MPS measurement value for convenience of description.
- the longitudinal vehicle speed can be estimated based on the measured value of WSS
- the lateral vehicle speed can be estimated based on the measured value of SAS and IMU
- the road surface inclination angle, slope, vertical force and lateral force can be estimated based on the measured value of IMU respectively.
- the longitudinal force is estimated based on the measured value of MPS, etc.
- the slip rate can be calculated based on the longitudinal vehicle speed obtained from the first estimation
- the side slip angle can be calculated based on the longitudinal vehicle speed and the road surface inclination angle
- the road adhesion coefficient can be obtained based on the estimation of the vertical force, lateral force and longitudinal force, etc. .
- FIG. 3 is a schematic structural diagram of a system for estimating vehicle state parameters provided by an embodiment of the present application.
- the vehicle state parameter estimation system mainly includes three modules: 1 sensor measurement module, 2 driving state adaptive module and 3 UKF vehicle state estimation module.
- 1 the sensor measurement module includes real sensor measurement and virtual sensor measurement, and its measured value is used as the input of the UKF vehicle state estimation module, mainly for its posterior estimation sub-module.
- the real sensor measurement mainly includes information such as the acceleration of the vehicle (such as the lateral acceleration and longitudinal acceleration of the vehicle) and yaw rate measured by the inertial measurement unit (IMU), and the four wheel speeds measured by the wheel speed sensor (WSS).
- IMU inertial measurement unit
- WSS wheel speed sensor
- the virtual sensor measurement mainly uses the results of other state estimation methods as virtual measurement values to be input into the UKF vehicle state estimation module, which mainly includes state estimation results based on kinematics, state estimation results based on neural networks, and state estimation results based on vision etc., no limitation here.
- the driving state adaptive module obtains the vehicle's motion state information (such as acceleration, yaw rate, wheel speed, steering wheel angle, driving torque and braking torque, etc.) and related environmental information (such as road adhesion coefficient, etc.) According to the analysis results of the driving state characteristics, the adaptive strategy of the process covariance and measurement covariance is determined, that is, the process covariance and measurement covariance are adaptively adjusted.
- This module can solve the UKF's dependence on the accuracy of the dynamic model, and improve the estimation accuracy and fusion accuracy of the dynamic UKF.
- 3UKF vehicle state estimation module uses the unscented Kalman filter method to comprehensively estimate the vehicle state, such as vehicle speed, center of mass side slip angle, tire force, slip rate, tire side slip angle, etc.
- This module mainly includes the following five sub-modules: Sigma points generation, Sigma points unscented transformation, prior estimation, posterior estimation and output model.
- Sigma points generation Sigma points generation
- Sigma points unscented transformation prior estimation
- posterior estimation posterior estimation
- output model For the functions of the above five sub-modules, please refer to the description of each step in the process shown in Figure 4 below. Not detailed here.
- FIG. 4 is a schematic flowchart of a method for estimating vehicle state parameters provided by an embodiment of the present application.
- the method can be implemented based on a vehicle state parameter estimation device, and the method at least includes the following steps S401-S404:
- the driving state data of the vehicle may include the environment information when the vehicle is running and the motion state information of the vehicle.
- the environmental information may include road surface adhesion coefficient ⁇ , etc.
- the motion state information of the vehicle may include acceleration (such as lateral acceleration a y , longitudinal acceleration a x ), yaw rate r, wheel speed (such as the wheel speed of the front left wheel of the vehicle speed ⁇ FL , front right wheel speed ⁇ FR , rear left wheel speed ⁇ RL , rear right wheel speed ⁇ RR ), steering wheel speed
- acceleration such as lateral acceleration a y , longitudinal acceleration a x
- yaw rate r wheel speed
- wheel speed such as the wheel speed of the front left wheel of the vehicle speed ⁇ FL , front right wheel speed ⁇ FR , rear left wheel speed ⁇ RL , rear right wheel speed ⁇ RR
- wheel speed such as the wheel speed of the front left wheel of the vehicle speed ⁇ FL , front right wheel speed ⁇ FR , rear left wheel speed ⁇ RL , rear right wheel speed ⁇ RR
- steering wheel speed such as the wheel speed
- the yaw rate can be expressed as The derivative of the steering wheel angle ⁇ obtained based on SAS measurement is equal to the steering wheel speed.
- the steering wheel speed can be expressed as Among them, the yaw angular acceleration
- the product of the longitudinal velocity v x is equal to the rate of change of lateral acceleration.
- the driving state data involved in the embodiment of the present application may include one or more of the following parameters: rate of change of lateral acceleration steering wheel speed Lateral acceleration a y , road surface adhesion coefficient ⁇ , wheel speed ⁇ FL of the front left wheel, wheel speed ⁇ FR of the front right wheel, wheel speed ⁇ RL of the rear left wheel or wheel speed ⁇ RR of the rear right wheel.
- the first process state x may include one or more of the following parameters: longitudinal velocity v x , lateral velocity v y , yaw rate r, wheel speed ⁇ FL of the front left wheel, wheel speed ⁇ FR of the front right wheel , the wheel speed ⁇ RL of the rear left wheel, the wheel speed ⁇ RR of the rear right wheel, or the road surface adhesion coefficient ⁇ , etc., are not limited here. That is to say, the first process state in the embodiment of the present application can be defined as the following matrix x:
- the first process covariance Q(k) can be defined as a diagonal matrix Q1, Right now:
- S402. Determine the second process covariance Q(k) and the first measurement covariance R(k) according to the driving state data of the vehicle.
- the second process covariance Q(k) and the first measurement covariance R(k) are determined according to the driving state data of the vehicle.
- the determination of the second process covariance Q(k) according to the driving state data of the vehicle above can be understood as: when the road surface adhesion coefficient ⁇ is greater than or equal to the first preset road surface adhesion coefficient threshold value ⁇ TH , and the lateral acceleration change rate absolute value of Greater than the preset lateral jerk threshold and/or, the absolute value of the steering wheel speed Greater than the preset steering wheel speed threshold
- obtain the first preset process covariance matrix as the second process covariance Q(k).
- the second preset process covariance matrix is obtained as the second process covariance Q(k).
- the third preset process covariance matrix is obtained as the second process covariance Q(k).
- FIG. 5 is a schematic diagram of determining the covariance Q(k) of the second process based on driving state data provided by an embodiment of the present application.
- a given road surface adhesion coefficient threshold value that is, the first preset road surface adhesion coefficient threshold value ⁇ TH
- the second process covariance Q(k) can take the first preset process covariance matrix.
- the first preset process covariance matrix may be an optimal process covariance matrix under transient conditions.
- the first preset process covariance matrix q r 0.1215, (Right now ); if the absolute value of the lateral acceleration rate Less than or equal to the preset lateral acceleration rate threshold and the absolute value of the steering wheel speed Less than or equal to the preset steering wheel speed threshold.
- the second process covariance Q(k) can be further adaptively adjusted according to the absolute value range of the lateral acceleration y of the vehicle, as shown in State1 to State5 in FIG. 8 below.
- the second process covariance Q(k) may be a second preset process covariance matrix.
- the first preset process covariance matrix and the second preset process covariance matrix may be the same or different, which is not limited here.
- q ⁇ in state0-state7 in Figure 5 can be the same or different, and this q ⁇ can be a preset value, or it can also be calculated based on the road adhesion coefficient of the actual driving road surface of the vehicle
- the output value is not limited here.
- the above determination of the first measurement covariance R(k) according to the driving state data of the vehicle can be understood as: when the maximum value max
- the above-mentioned determination of the first measurement covariance R(k) according to the road surface adhesion coefficient ⁇ can be understood as: when the road surface adhesion coefficient ⁇ is greater than or equal to the second preset road surface adhesion coefficient threshold value ⁇ ′ TH , the second preset measurement The covariance matrix is used as the first measurement covariance R(k); when the road surface adhesion coefficient ⁇ is less than the second preset road surface adhesion coefficient threshold value ⁇ ′ TH , the third preset measurement covariance matrix is obtained as the first measurement covariance R( k).
- the first measurement covariance R(k) can be defined as a diagonal matrix R1, Right now:
- r r represents the measurement covariance coefficient corresponding to the longitudinal acceleration measurement, the measurement covariance coefficient corresponding to the lateral acceleration measurement and the measurement covariance coefficient corresponding to the yaw angular velocity measurement, Represent the measurement covariance coefficients corresponding to the wheel speed measurements of the front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively.
- the first measurement covariance R(k) can also be defined as a diagonal matrix R2, Right now:
- r r represents the measurement covariance coefficient corresponding to the longitudinal acceleration, the measurement covariance coefficient corresponding to the lateral acceleration and the measurement covariance coefficient corresponding to the yaw rate
- Represent the measurement covariance coefficients corresponding to the wheel speeds of the front left wheel, front right wheel, rear left wheel, and rear right wheel represent the measurement covariance coefficient corresponding to the side slip angle of the vehicle center of mass obtained based on kinematics estimation, the measurement covariance coefficient corresponding to the side slip angle of the vehicle center of mass obtained based on neural network estimation, and the corresponding measurement covariance coefficient of the side slip angle of the vehicle center of mass obtained based on visual estimation.
- Coefficient of measurement covariance is the measurement covariance coefficient corresponding to the longitudinal acceleration, the measurement covariance coefficient corresponding to the lateral acceleration and the measurement covariance coefficient corresponding to the yaw rate
- FIG. 6 is a schematic diagram of determining the first measurement covariance R(k) based on driving state data provided by an embodiment of the present application.
- the first measurement covariance R(k) is taken as corresponding to state0 in Figure 6
- the first preset measurement covariance matrix for example, the second preset measurement covariance matrix can be the optimal measurement covariance matrix under wheel locking or slipping; when the maximum value max
- the adhesion coefficient is further adaptively adjusted: when the road surface adhesion coefficient ⁇ is greater than the second preset road surface adhesion coefficient threshold ⁇ ′
- a first measurement y h of a vehicle sensor is acquired.
- the first measurement value y h may also include a measurement value measured by a real sensor of the vehicle and a measurement value obtained based on a virtual sensor of the vehicle.
- the measurement value of the vehicle virtual sensor may include the measurement value obtained by neural network estimation, and/or the measurement value obtained by kinematics-based state estimation, and/or the measurement value obtained by vision-based state estimation, etc. Do limit.
- kinematics-based state estimation obtains vehicle mass center slip angle ⁇ Kinematic
- neural network-based state estimation obtains vehicle mass center slip angle ⁇ NN
- vision-based state estimation obtains vehicle mass center slip angle ⁇ Camera
- the first A measured value y h [a x , a y , r, ⁇ FL , ⁇ FR , ⁇ RL , ⁇ RR , ⁇ Kinematic , ⁇ NN , ⁇ Cmmera ]
- the first measurement covariance etc. which are determined according to actual scenarios, and are not limited here.
- the first measured values involved in the embodiments of the present application all include the measured values measured by the real sensors of the vehicle and the measured values obtained based on the virtual sensors of the vehicle, and the first measurement covariance includes the measured values of the real sensors of the vehicle.
- the corresponding measurement covariance coefficient and the measurement covariance coefficient corresponding to the measurement value of the vehicle virtual sensor are taken as an example for schematic illustration.
- the present application expands the measurement value and measurement covariance of the real sensor of the vehicle, that is, the first measurement value y h not only includes the measurement value measured by the real sensor of the vehicle, but also includes the measurement value obtained based on the virtual sensor of the vehicle , correspondingly, the first measurement covariance not only includes the measurement covariance coefficient corresponding to the measurement value of the vehicle real sensor, but also includes the measurement covariance coefficient corresponding to the measurement value of the vehicle virtual sensor, which can improve the estimation accuracy of the vehicle state parameters.
- the vehicle state parameters of the vehicle are determined according to the first measured value y h , the first process state x, the first process covariance Q, the second process covariance Q(k) and the first measurement covariance R(k) , can be understood as: determine the first key point data ⁇ i (k+1
- the vehicle state parameters of the vehicle which can be understood as: determine according to the first key point data ⁇ i (k+1
- k) the second measured value ⁇ i (k), prior process state Measurement estimates and measure the estimated covariance
- FIG. 7 is a schematic flowchart of another method for estimating vehicle state parameters provided by an embodiment of the present application. As shown in Figure 7, the method mainly includes the following steps S701-S705:
- the first process state x and the first process covariance Q at time k are obtained, and then Sigma points can be generated according to the first process state x and the first process covariance Q.
- Sigma points ⁇ i (k) are generated according to the sampling value of the first process state x at time k and the sampled values of the first process covariance Q.
- the generated Sigma points ⁇ i (k) satisfy:
- the Sigma points are a series of representative points including the mean point extracted from the original Gaussian distribution (that is, the Gaussian distribution based on the mean value of the first process state and the covariance of the first process), and they are distributed in the first
- the process state mean is around and represents the entire Gaussian distribution.
- the more points are extracted from the original Gaussian distribution, the more accurate the UKF approximation to the nonlinear model will be.
- k) and the second measurement value ⁇ i (k) can be obtained by performing unscented transformation on the generated Sigma points. Specifically, based on a 7-degree of freedom (DOF) nonlinear vehicle dynamics model and a measurement model, the generated Sigma points ⁇ i (k) can be unscented transformed to obtain the predicted value at k+ The new Sigma points at time 1, that is, the first key point data ⁇ i (k+1
- DOE 7-degree of freedom
- the new Sigma points obtained after transformation (that is, the first key point data ⁇ i (k+1
- k) and the second measured value ⁇ i (k) respectively satisfy:
- f represents the state transition function
- u(k) represents the state of the control variable
- w represents the process noise
- h represents the measurement function
- v represents the measurement noise.
- the prior process state can be determined according to the first key point data ⁇ i (k+1
- the measurement estimate is determined from the second measurement ⁇ i (k) and the first measurement covariance R(k) and measure the estimated covariance That is, according to the second measured value ⁇ i (k) and the first measured covariance R(k), the measured estimated value and measure the estimated covariance Make estimates, specifically, measure estimates and measure the estimated covariance Satisfied respectively:
- k) and prior process state Compute the cross-covariance matrix Specifically, the cross-covariance matrix satisfy:
- the covariance is estimated from the measurement and the cross-covariance matrix Calculate the Kalman feedback gain matrix K(k+1
- the prior process covariance Measure estimated covariance prior process state Measurement estimates and the first measurement y h (k) determines the posterior process state and the posterior process covariance That is, according to the Kalman feedback gain matrix K(k+1
- the posterior process covariance That is, the predicted process covariance at time k+1
- the posterior process state That is, the predicted process state at time k+1.
- the posterior process state Determine the vehicle state parameters at time k+1 Specifically, according to the posterior process state and the control variable state u(k), to make a comprehensive estimate of the vehicle state parameters at time k+1:
- the posterior process covariance and the posterior process state It can be used as the input of the vehicle state parameter estimation at time k+2, that is, the posterior process covariance As the sampled value of the new process covariance, the posterior process state As the sampling value of the new process state, it is used for the vehicle state estimation at the next moment of the current moment, or understood that the embodiment of the present application can estimate the vehicle state parameter at the current moment through the vehicle state at the previous moment at the current moment. That is to say, the aforementioned steps S701 to S704 may be performed cyclically based on the new process state and process covariance generated by the posterior estimation.
- the application adaptively adjusts process covariance and measurement covariance based on driving state data, and then uses the adjusted process covariance and measurement covariance for vehicle state estimation, which can improve the accuracy of vehicle state parameters. Estimated accuracy.
- this application introduces the results obtained by other estimation methods (such as state estimation based on kinematics, state estimation based on neural network, and state estimation based on vision) as the measured value of "virtual sensor”, and compares the results obtained based on real sensor measurement.
- the measurement value and the calculated measurement covariance are extended, and then the extended measurement value and measurement covariance are used for vehicle state estimation, which can further improve the estimation accuracy of the vehicle state and help improve the performance of the vehicle dynamics control algorithm.
- the device for estimating vehicle state parameters provided by the present application will be described in detail below with reference to FIGS. 8 to 10 .
- FIG. 8 is a schematic structural diagram of a device for estimating vehicle state parameters provided by an embodiment of the present application.
- the device for estimating vehicle state parameters shown in FIG. 8 may be used to perform some or all of the functions in the method embodiments described above in FIG. 4 to FIG. 7 .
- the device may be a terminal, such as a vehicle-mounted terminal, or a device in the terminal, or a device that can be matched with the terminal.
- the device for estimating vehicle state parameters may also be a system-on-a-chip.
- the device for estimating vehicle state parameters shown in FIG. 8 may include a transceiver unit 801 and a processing unit 802 . Wherein, the processing unit 802 is configured to perform data processing.
- the transceiver unit 801 is integrated with a receiving unit and a sending unit.
- the transceiver unit 801 may also be called a communication unit.
- the transceiver unit 801 may also be split into a receiving unit and a sending unit.
- the processing unit 802 below is the same as the transceiver unit 801 , and details will not be repeated below. in:
- a transceiver unit 801 configured to acquire the driving state data of the vehicle, the first process state x and the first process covariance Q;
- a processing unit 802 configured to determine a second process covariance Q(k) and a first measurement covariance R(k) according to the driving state data of the vehicle;
- the transceiver unit 801 is configured to acquire the first measured value y h of the vehicle sensor
- the processing unit 802 is configured to, according to the first measured value y h , the first process state x, the first process covariance Q, the second process covariance Q(k) and the first A measurement covariance R(k) determines vehicle state parameters of the vehicle.
- the driving state data includes one or more of the following: rate of change of lateral acceleration steering wheel speed Lateral acceleration a y , road surface adhesion coefficient ⁇ , wheel speed ⁇ FL of the front left wheel, wheel speed ⁇ FR of the front right wheel, wheel speed ⁇ RL of the rear left wheel or wheel speed ⁇ RR of the rear right wheel.
- the processing unit 802 is configured to:
- the processing unit 802 is configured to:
- the first preset measurement covariance matrix is obtained as the first measurement covariance R(k);
- the first measurement covariance R(k) is determined according to the road surface adhesion coefficient ⁇ .
- the processing unit 802 is configured to:
- the first measured value y h of the vehicle sensor includes a measured value measured by a real sensor of the vehicle and a measured value obtained based on a virtual sensor of the vehicle, and the measured value of the virtual sensor of the vehicle is passed through a neural network.
- the network gets.
- the first process state x includes one or more of the following:
- the first measured value y h includes one or more of the following:
- the vehicle state parameters include one or more of the following:
- FIG. 9 is a schematic structural diagram of another device for estimating vehicle state parameters provided by an embodiment of the present application.
- the device for estimating vehicle state parameters includes: a processor 901 , a communication interface 902 and a memory 903 .
- the processor 901 , the communication interface 902 and the memory 903 are coupled through a bus 904 .
- the processor 901 may be one or more central processing units (central processing unit, CPU).
- CPU central processing unit
- the CPU may be a single-core CPU or a multi-core CPU.
- the processor 901 is used to read the program stored in the memory, and cooperate with the communication interface 902 to execute some or all steps of the method performed by the vehicle state parameter estimation device in the above-mentioned embodiments of the present application.
- Memory 903 includes but not limited to random access memory (random access memory, RAM), erasable programmable read-only memory (erasable programmable rom, EPROM), read-only memory (read-only memory, ROM) or portable read-only memory (compact disc read-only memory, CD-ROM), etc., the memory 903 is used to store programs, and the processor 901 can read the programs stored in the memory 903 to execute the programs shown in Figures 4 to 7 in the above-mentioned embodiments of the present application. Each step in the method will not be repeated here.
- RAM random access memory
- EPROM erasable programmable read-only memory
- read-only memory read-only memory
- CD-ROM compact disc read-only memory
- FIG. 10 is a schematic structural diagram of a chip provided by an embodiment of the present application.
- the chip 100 may include: a processor 1001 , and one or more communication interfaces 1002 coupled to the processor 1001 . in:
- Processor 1001 may be used to read and execute computer readable instructions.
- the processor 1001 may mainly include a controller, an arithmetic unit, and a register.
- the controller is mainly responsible for instruction decoding, and sends out control signals for the operations corresponding to the instructions.
- the arithmetic unit is mainly responsible for performing fixed-point or floating-point arithmetic operations, shift operations, and logic operations, and can also perform address operations and conversions.
- the register is mainly responsible for saving the register operands and intermediate operation results temporarily stored during the execution of the instruction.
- the hardware architecture of the processor 1001 may be an application specific integrated circuit (application specific integrated circuits, ASIC) architecture, MIPS architecture, ARM architecture, or NP architecture, and so on.
- ASIC application specific integrated circuits
- MIPS micro-semiconductor
- ARM programmable gate array
- NP neuronephrine
- Processor 1001 may be single-core or multi-core.
- the communication interface 1002 can be used to input the data to be processed to the processor 1001, and can output the processing result of the processor 1001 to the outside.
- the communication interface 1002 can be a general purpose input output (GPIO) interface, which can be connected with multiple peripheral devices (such as a display (LCD), a camera (camera), a radio frequency (radio frequency, RF) module, etc.) .
- GPIO general purpose input output
- peripheral devices such as a display (LCD), a camera (camera), a radio frequency (radio frequency, RF) module, etc.
- the communication interface 1002 is connected with the processor 1001 through the bus 1003 .
- the processor 1001 may be configured to call the implementation program of the vehicle state parameter estimation method provided by one or more embodiments of the present application from the memory, and execute the instructions contained in the program.
- the communication interface 1002 can be used to output the execution result of the processor 1001 .
- the communication interface 1002 may be specifically used to output the estimation result of the vehicle state parameter by the processor 1001 .
- the method for estimating vehicle state parameters provided by one or more embodiments of the present application reference may be made to the various embodiments shown in FIGS. 4 to 7 above, which will not be repeated here.
- processor 1001 and the communication interface 1002 can be realized by hardware design, software design, or a combination of software and hardware, which is not limited here.
- the problem-solving principle and beneficial effect of the vehicle state parameter estimation device provided in the embodiment of the present application are similar to the problem-solving principle and beneficial effect of the vehicle state parameter estimation method in the method embodiment of the present application. Please refer to the implementation of the method In addition, the relationship between the steps executed by each related module can also refer to the description of related content in the foregoing embodiments, and for the sake of brevity, details are not repeated here.
- the embodiment of the present application also provides a computer storage medium, which can be used to store the computer software instructions used by the vehicle state parameter estimation device in the embodiments shown in Fig. 4 to Fig.
- the program designed by the state parameter estimation device includes but is not limited to flash memory, hard disk, and solid state hard disk.
- a computer program product is also provided.
- the computer product When the computer product is run by the vehicle state parameter estimation device, it can execute the vehicle program designed for the vehicle state parameter estimation device in the above-mentioned embodiments shown in FIGS. 4 to 7. State parameter estimation method.
- An embodiment of the present application also provides a sensor system, which is used to provide a vehicle state parameter estimation function for a vehicle. It includes at least one device for estimating vehicle state parameters mentioned in the above-mentioned embodiments of the present application, and at least one of other sensors such as cameras or radars. At least one sensor device in the system can be integrated into a complete machine or device, or the The at least one sensor device within the system may also be provided independently as an element or device.
- the embodiment of the present application also provides a system, which is applied in unmanned driving or intelligent driving, which includes at least one of the vehicle state parameter estimation device, camera, radar and other sensors mentioned in the above embodiments of the present application, and at least one of other sensors, At least one device in the system can be integrated into a complete machine or equipment, or at least one device in the system can also be independently configured as a component or device.
- any of the above systems can interact with the central controller of the vehicle to provide information such as vehicle state parameters for the decision-making or control of the vehicle driving.
- An embodiment of the present application further provides a terminal, the terminal including at least one device for estimating vehicle state parameters mentioned in the above-mentioned embodiments of the present application or any one of the above-mentioned systems.
- the above-mentioned terminal may include a vehicle, a camera, or a drone, etc., which is not limited here.
- the modules in the device embodiment of the present application can be combined, divided and deleted according to actual needs.
Landscapes
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
本申请提供了一种车辆状态参数估计方法及装置，属于汽车控制技术领域。该方法包括：获取车辆的驾驶状态数据、第一过程状态x和第一过程协方差Q。根据车辆的驾驶状态数据确定第二过程协方差Q（k）和第一测量协方差R（k）。获取车辆传感器的第一测量值y h。根据第一测量值y h、第一过程状态x、第一过程协方差Q、第二过程协方差Q（k）和第一测量协方差R（k）确定车辆的车辆状态参数。本申请在车辆状态参数的估计过程中，基于驾驶状态数据自适应调整过程协方差和测量协方差，进而将调整后的过程协方差和测量协方差用于车辆状态估计，可提高车辆状态参数的估计精度。
Description
本申请涉及汽车控制技术领域，尤其涉及一种车辆状态参数估计方法及装置。
近年来，随着智能汽车的发展，包括电子稳定系统(electronic stability program，ESP)、制动防抱死系统(antilock brake system，ABS)、牵引力控制系统(traction control system，TCS)等在内的车辆控制系统在车辆上的应用越来越广泛。目前，为了实现对车辆的自动控制，通常需要收集车辆的各种状态数据以用于调动上述各种车辆控制系统。一般来说，可通过在车辆上加装各类传感器，以收集车辆的状态数据。然而包括质心侧偏角在内的一些车辆状态数据通常需要额外加装价格昂贵的传感器才能测量得到。考虑到需要控制量产车的制造成本，人们越来越倾向于通过车辆上现有搭载的车载传感器所收集来的信号，进行估计得到用于车辆控制的其他参数，如：质心侧偏角，横摆角速度以及纵向车速等。但是，目前相关技术中提出的车辆状态参数的估计方法精度较低，无法满足车辆控制的高精度需求。
发明内容
本申请提供了一种车辆状态参数估计方法及车辆状态参数估计装置，可提高车辆状态参数的估计精度。
第一方面，本申请提供了一种车辆状态参数估计方法，该方法包括：
获取车辆的驾驶状态数据、第一过程状态x和第一过程协方差Q；
根据所述车辆的驾驶状态数据确定第二过程协方差Q(k)和第一测量协方差R(k)；
获取车辆传感器的第一测量值y
h；
根据所述第一测量值y
h、所述第一过程状态x、所述第一过程协方差Q、所述第二过程协方差Q(k)和所述第一测量协方差R(k)确定所述车辆的车辆状态参数。
本申请在车辆状态参数的估计过程中，基于驾驶状态数据自适应调整过程协方差和测量协方差，进而将调整后的过程协方差和测量协方差用于车辆状态估计，可提高车辆状态参数的估计精度。
在一种可能的实现中，所述驾驶状态数据包括以下一项或多项：侧向加速度变化率
方向盘转速
横向加速度a
y、路面附着系数μ、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL或后右轮的轮速ω
RR。
在一种可能的实现中，所述根据所述车辆的驾驶状态数据确定第二过程协方差Q(k)，包括：
当所述路面附着系数μ大于或者等于第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
大于预设侧向加速度变化率阈值
和/或，所述方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第一预设过程协方差矩阵作为所述第二过程协方差Q(k)；或者
当所述路面附着系数μ大于或者等于第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
小于或者等于所述预设侧向加速度变化率阈值
以及所述方向盘转速的绝对值
小于或者等于所述预设方向盘转速阈值
时，根据所述横向加速度a
y确定所述第二过程协方差Q(k)；或者
当所述路面附着系数μ小于所述第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
大于预设侧向加速度变化率阈值
和/或，所述方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第二预设过程协方差矩阵作为所述第二过程协方差Q(k)； 或者
当所述路面附着系数μ小于所述第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
小于或者等于所述预设侧向加速度变化率阈值
以及所述方向盘转速的绝对值
小于或者等于所述预设方向盘转速阈值
时，获取第三预设过程协方差矩阵作为所述第二过程协方差Q(k)。
在一种可能的实现中，所述根据所述车辆的驾驶状态数据确定第一测量协方差R(k)，包括：
当不同车轮的轮速差的绝对值的最大值max|Δω
i|大于预设轮速差阈值Δω
TH，和/或，不同车轮的轮速的绝对值的最小值min|ω
i|小于预设轮速阈值ω
TH时，获取第一预设测量协方差矩阵作为所述第一测量协方差R(k)；
当不同车轮的轮速差的绝对值的最大值max|Δω
i|小于或者等于所述预设轮速差阈值Δω
TH，且不同车轮的轮速的绝对值的最小值min|ω
i|大于或者等于所述预设轮速阈值ω
TH时，根据所述路面附着系数μ确定所述第一测量协方差R(k)。
在一种可能的实现中，所述根据所述路面附着系数μ确定所述第一测量协方差R(k)，包括：
当所述路面附着系数μ大于或者等于第二预设路面附着系数阈值μ′
TH时，获取第二预设测量协方差矩阵作为所述第一测量协方差R(k)；
当所述路面附着系数μ小于所述第二预设路面附着系数阈值μ′
TH时，获取第三预设测量协方差矩阵作为所述第一测量协方差R(k)。
在一种可能的实现中，所述车辆传感器的所述第一测量值y
h包括车辆真实传感器测得的测量值和基于车辆虚拟传感器获取的测量值，所述车辆虚拟传感器的测量值通过神经网络得到。
本申请通过引入其他估计方法(例如基于运动学的状态估计、基于神经网络的状态估计以及基于视觉的状态估计)获取的结果作为“虚拟传感器”的测量值，对基于真实传感器测量得到的测量值和计算出的测量协方差进行扩展，进而将扩展后的测量值和测量协方差用于车辆状态估计，可进一步提高对车辆状态的估计精度。
在一种可能的实现中，所述第一过程状态x包括以下一项或多项：
纵向速度v
x、横向速度v
y、横摆角速度r、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR或路面附着系数μ。
在一种可能的实现中，所述第一测量值y
h包括以下一项或多项：
纵向加速度a
x、横向加速度a
y、横摆角速度r、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR、或质心侧偏角β。
在一种可能的实现中，所述车辆状态参数包括以下一项或多项：
纵向速度v
x、横向速度v
y、横摆角速度r、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR、路面附着系数μ、纵向加速度a
x、横向加速度a
y、质心侧偏角β。
第二方面，本申请提供了一种车辆状态参数估计装置，该装置包括：
收发单元，用于获取车辆的驾驶状态数据、第一过程状态x和第一过程协方差Q；
处理单元，用于根据所述车辆的驾驶状态数据确定第二过程协方差Q(k)和第一测量协方差R(k)；
所述收发单元，用于获取车辆传感器的第一测量值y
h；
所述处理单元，用于根据所述第一测量值y
h、所述第一过程状态x、所述第一过程协方差Q、所述第二过程协方差Q(k)和所述第一测量协方差R(k)确定所述车辆的车辆状态参数。
在一种可能的实现中，所述驾驶状态数据包括以下一项或多项：侧向加速度变化率
方向盘转速
横向加速度a
y、路面附着系数μ、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL或后右轮的轮速ω
RR。
在一种可能的实现中，所述处理单元用于：
当所述路面附着系数μ大于或者等于第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
大于预设侧向加速度变化率阈值
和/或，所述方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第一预设过程协方差矩阵作为所述第二过程协方差Q(k)；或者
当所述路面附着系数μ大于或者等于第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
小于或者等于所述预设侧向加速度变化率阈值
以及所述方向盘转速的绝对值
小于或者等于所述预设方向盘转速阈值
时，根据所述横向加速度a
y确定所述第二过程协方差Q(k)；或者
当所述路面附着系数μ小于所述第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
大于预设侧向加速度变化率阈值
和/或，所述方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第二预设过程协方差矩阵作为所述第二过程协方差Q(k)；或者
当所述路面附着系数μ小于所述第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
小于或者等于所述预设侧向加速度变化率阈值
以及所述方向盘转速的绝对值
小于或者等于所述预设方向盘转速阈值
时，获取第三预设过程协方差矩阵作为所述第二过程协方差Q(k)。
在一种可能的实现中，所述处理单元用于：
当不同车轮的轮速差的绝对值的最大值max|Δω
i|大于预设轮速差阈值Δω
TH，和/或，不同车轮的轮速的绝对值的最小值min|ω
i|小于预设轮速阈值ω
TH时，获取第一预设测量协方差矩阵作为所述第一测量协方差R(k)；
当不同车轮的轮速差的绝对值的最大值max|Δω
i|小于或者等于所述预设轮速差阈值Δω
TH，且不同车轮的轮速的绝对值的最小值min|ω
i|大于或者等于所述预设轮速阈值ω
TH时，根据所述路面附着系数μ确定所述第一测量协方差R(k)。
在一种可能的实现中，所述处理单元用于：
当所述路面附着系数μ大于或者等于第二预设路面附着系数阈值μ′
TH时，获取第二预设测量协方差矩阵作为所述第一测量协方差R(k)；
当所述路面附着系数μ小于所述第二预设路面附着系数阈值μ′
TH时，获取第三预设测量协方差矩阵作为所述第一测量协方差R(k)。
在一种可能的实现中，所述车辆传感器的所述第一测量值y
h包括车辆真实传感器测得的测量值和基于车辆虚拟传感器获取的测量值，所述车辆虚拟传感器的测量值通过神经网络得到。
在一种可能的实现中，所述第一过程状态x包括以下一项或多项：
纵向速度v
x、横向速度v
y、横摆角速度r、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR或路面附着系数μ。
在一种可能的实现中，所述第一测量值y
h包括以下一项或多项：
纵向加速度a
x、横向加速度a
y、横摆角速度r、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR、或质心侧偏角β。
在一种可能的实现中，所述车辆状态参数包括以下一项或多项：
纵向速度v
x、横向速度v
y、横摆角速度r、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR、路面附着系数μ、纵向加速度a
x、横向加速度a
y、质心侧偏角β。
第三方面，本申请提供了一种车辆状态参数估计装置，该装置可以是终端设备，也可以是终端设备中的装置，或者是能够和终端设备匹配使用的装置。其中，该车辆状态参数估计装置还可以为芯片系统。该车辆状态参数估计装置可执行第一方面所述的方法。该车辆状态参数估计装置的功能可以通过硬件实现，也可以通过硬件执行相应的软件实现。该硬件或软件包括一个或多个与上述功能相对应的单元或模块。该单元或模块可以是软件和/或硬件。该 车辆状态参数估计装置执行的操作及有益效果可以参见上述第一方面所述的方法以及有益效果，重复之处不再赘述。
第四方面，本申请提供了一种车辆状态参数估计装置，该装置可以是终端设备，所述车辆状态参数估计装置包括处理器和收发器，所述处理器和所述收发器用于执行至少一个存储器中存储的计算机程序或指令，以使得所述装置实现如第一方面中任意一项的方法。
第五方面，本申请提供了一种车辆状态参数估计装置，该装置可以是终端设备，该车辆状态参数估计装置包括处理器、收发器和存储器。其中，处理器、收发器和存储器耦合；处理器和收发器用于实现如第一方面中任意一项的方法。
第六方面，本申请提供了一种计算机可读存储介质，存储介质中存储有计算机程序或指令，当计算机程序或指令被计算机执行时，实现如第一方面中任意一项的方法。
第七方面，本申请提供一种包括指令的计算机程序产品，所述计算机程序产品中包括计算机程序代码，当计算机程序代码在计算机上运行时，以实现第一方面中任意一项的方法。
第八方面，提供一种芯片系统，该芯片系统包括处理器，还可以包括存储器，用于实现前述第一方面中任一方面以及任意可能的设计的方法。该芯片系统可以由芯片构成，也可以包含芯片和其他分立器件。
图1是本申请实施例提供的基于轮速传感器的车辆状态估计的示意图；
图2是本申请实施例提供的基于多传感器的车辆状态估计的示意图；
图3是本申请实施例提供的车辆状态参数估计系统的架构示意图；
图4是本申请实施例提供的一种车辆状态参数估计方法的流程示意图；
图5是本申请实施例提供的基于驾驶状态数据确定第二过程协方差Q(k)的示意图；
图6是本申请实施例提供的基于驾驶状态数据确定第一测量协方差R(k)的示意图；
图7是本申请实施例提供的另一种车辆状态参数估计方法的流程示意图；
图8是本申请实施例提供的一种车辆状态参数估计装置的结构示意图；
图9是本申请实施例提供的另一种车辆状态参数估计装置的结构示意图；
图10是本申请实施例提供的一种芯片的结构示意图。
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述。
在本申请的描述中，除非另有说明，“/”表示“或”的意思，例如，A/B可以表示A或B。本申请实施例中的“和/或”仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。此外，“至少一个”是指一个或多个，“多个”是指两个或两个以上。“第一”、“第二”等字样并不对数量和执行次序进行限定，并且“第一”、“第二”等字样也并不限定一定不同。
本申请中，“示例性的”或者“例如”等词用于表示作例子、例证或说明。本申请中被描述为“示例性的”或者“例如”的任何实施例或设计方案不应被解释为比其他实施例或设计方案更优选或更具优势。确切而言，使用“示例性的”或者“例如”等词旨在以具体方式呈现相关概念。
为了便于理解，下面先对本申请实施例中的部分用语进行解释说明。
1、无迹卡尔曼滤波(unscented kalman filter，UKF)：UKF是解决非线性卡尔曼滤波的另一种思路，它利用无迹变换来解决概率分布非线性变换的问题。无迹卡尔曼滤波不需要像扩展卡尔曼滤波一样计算雅克比矩阵，在计算量大致相当的情况下，能够获得更加精确的非线性处理效果。
2、惯性测量单元(inertial measurement unit，IMU)：测量物体三轴姿态角以及加速度的装置，一般包含三个单轴的加速度计和三个单轴的陀螺。
3、方向盘转角传感器(steering angle sensor，SAS)：用于测量车辆转向时方向盘的旋转角度，主要安装在方向盘下方的方向柱内。
4、轮速传感器(wheel speed sensor，WSS)：用来测量汽车车轮转速的传感器，常用的轮速传感器主要有磁电式轮速传感器、霍尔式轮速传感器。
5、主缸压力传感器(master cylinder pressure sensor，MPS)：用于测量主缸内压力的传感器。
6、高级驾驶辅助系统(advanced driver assistance system，ADAS)：利用安装在车上的各式各样传感器(毫米波雷达、激光雷达、摄像头以及卫星导航)，在汽车行驶过程中随时来感应周围的环境，收集数据，进行静态、动态物体的辨识、侦测与追踪，并结合导航地图数据，进行系统的运算与分析，从而预先让驾驶员察觉到可能发生的危险，有效增加汽车驾驶的舒适性和安全性。
下面对本申请实施例的系统架构和业务场景进行描述。需要说明的是，本申请描述的系统架构及业务场景是为了更加清楚的说明本申请的技术方案，并不构成对于本申请提供的技术方案的限定，本领域普通技术人员可知，随着系统架构的演变和新业务场景的出现，本申请提供的技术方案对于类似的技术问题，同样适用。
需要说明的是，高精度的车辆状态参数估计(简称车辆状态估计)是车辆动力学控制和自动驾驶运动控制的重要前提。随着ADAS和人工智能(artificial intelligence，AI)技术的普及，视觉、惯导、雷达、激光雷达等传感器在车上应用越来越多，因此也给车辆状态估计提供了新的机遇。通常而言，传感器数量的增加可降低车辆状态估计的次数，如质心侧偏角估计、路面附着系数估计等。示例性地，请参见图1，图1是本申请实施例提供的基于轮速传感器的车辆状态估计的示意图。如图1所示，当只有轮速传感器(即WSS)可用时，车辆质心侧偏角和路面附着系数的估计需要进行四次估计才能间接获得，且估计精度会随着每次估计不断降低，这是因为每次估计都会引入一定的估计误差。具体地，可基于WSS测量得到车辆的前左轮、前右轮、后左轮、后右轮的轮速等参数，分别记为ω
FL,ω
FR,ω
RL,ω
RR，为方便描述，简称WSS的测量值。在一次估计时，可基于WSS的测量值估计出纵向车速和纵向加速度等。在二次估计中，可基于纵向车速估计得到滑移率、横摆角速度，基于纵向车速和WSS的测量值估计得到侧向加速度，基于纵向加速度估计出主缸压力值等。在三次估计中，基于横摆角速度和侧向加速度估计出横向车速，基于侧向加速度分别估计得到垂向力和侧向力，基于侧向加速度和主缸压力值估计得到纵向力等。在四次估计中，基于一次估计得到的纵向车速和三次估计得到的横向车速估计得到侧偏角，基于三次估计得到的垂向力，侧向力和纵向力估计得到路面附着系数等。
示例性地，请参见图2，图2是本申请实施例提供的基于多传感器的车辆状态估计的示意图。如图2所示，当轮速传感器搭配方向盘转角传感器(即SAS)、惯性测量单元(即IMU)和主缸压力传感器(即MPS)使用时，车辆质心侧偏角和路面附着系数需要通过两次估计获得，其中，估计次数的降低可提高质心侧偏角和路面附着系数的估计精度。具体地，可基于WSS测量得到车辆的前左轮、前右轮、后左轮、后右轮的轮速等参数，分别记为ω
FL,ω
FR,ω
RL,ω
RR，为方便描述，简称WSS的测量值。基于SAS测量得到方向盘转角等参 数，为方便描述，简称SAS的测量值。基于IMU测量得到纵向加速度、横向加速度以及横摆角速度等参数，为方便描述，简称IMU的测量值。基于MPS测量得到主缸压力值等参数，为方便描述，简称MPS的测量值。在一次估计中，可基于WSS的测量值估计得到纵向车速，基于SAS和IMU的测量值估计得到横向车速，基于IMU的测量值分别估计得到路面倾斜角，坡度，垂向力和侧向力等，基于MPS的测量值估计得到纵向力等。在二次估计中，可基于一次估计得到的纵向车速计算出滑移率，基于纵向车速和路面倾斜角计算出侧偏角，基于垂向力、侧向力和纵向力估计得到路面附着系数等。
下面进一步介绍本申请实施例提供的车辆状态参数估计系统。
示例性地，请参见图3，图3是本申请实施例提供的车辆状态参数估计系统的架构示意图。如图3所示，该车辆状态参数估计系统主要包括三大模块：①传感器测量模块，②驾驶状态自适应模块以及③UKF车辆状态估计模块。其中，①传感器测量模块包括真实传感器测量以及虚拟传感器测量，其测量值作为UKF车辆状态估计模块的输入，主要用于其后验估计子模块。其中真实传感器测量主要包括由惯性测量单元(IMU)测量得到的车辆的加速度(例如车辆的横向加速度和纵向加速度)及横摆角速度等信息、由轮速传感器(WSS)测量得到的四个车轮的轮速等信息，在此不做限制。虚拟传感器测量主要是利用其它状态估计方法的结果作为虚拟测量值输入到UKF车辆状态估计模块中，其主要包括基于运动学的状态估计结果、基于神经网络的状态估计结果以及基于视觉的状态估计结果等，在此不做限制。
②驾驶状态自适应模块获取车辆的运动状态信息(如加速度、横摆角速度、轮速、方向盘转角、驱动力矩及制动力矩等)及相关环境信息(如路面附着系数等)等进行驾驶状态特征分析，并根据驾驶状态特征分析的结果确定过程协方差和测量协方差的自适应策略，即对过程协方差和测量协方差进行适应性调整。该模块可解决UKF对动力学模型精度的依赖，提高动力学UKF的估计精度和融合精度。
③UKF车辆状态估计模块采用无迹卡尔曼滤波方法对车辆状态进行全面的估计，例如车速、质心侧偏角、轮胎力、滑移率、轮胎侧偏角等等。该模块主要包括以下五个子模块：Sigma points生成、Sigma points无迹变换、先验估计、后验估计以及输出模型，上述五个子模块的功能参见下述图4所示流程中各个步骤的描述，在此不进行详述。
请参见图4，图4是本申请实施例提供的一种车辆状态参数估计方法的流程示意图。其中，该方法可以基于车辆状态参数估计装置来实现，该方法至少包括如下步骤S401～S404：
S401、获取车辆的驾驶状态数据、第一过程状态x和第一过程协方差Q。
在一些可行的实施方式中，若需要进行车辆状态参数的估计，则首先可获取车辆的驾驶状态数据、第一过程状态x以及第一过程协方差Q。其中，车辆驾驶状态数据可包括车辆运行时的环境信息和车辆的运动状态信息。示例性地，环境信息可包括路面附着系数μ等，车辆的运动状态信息可包括加速度(例如横向加速度a
y，纵向加速度a
x)、横摆角速度r、轮速(例如车辆的前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR)、方向盘转速
驱动力矩及制动力矩等，在此不做限制。需要说明的是，在本申请实施例中，横摆角速度r的导数即等于横摆角加速度，为方便描述，可将横摆角加速度表示为
基于SAS测量得到的方向盘转角δ的导数即等于方向盘转速，为方便描述，可将方向盘转速表示为
其中，横摆角加速度
与纵向速度v
x的乘积即等于侧向加速度变化率，为方便描述，本申请中可将侧向加速度变化率表示为
其中，V＝v
x。因此，本申请实施例中所涉及的驾驶状态数据可以包括以下参数中的一项或多项：侧向加速度变化率
方向盘转速
横向加速度a
y、路面附着系数μ、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL或后右轮的轮速ω
RR。
其中，第一过程状态x可包括以下参数中的一项或多项：纵向速度v
x、横向速度v
y、横摆 角速度r、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR或路面附着系数μ等，在此不做限制。也就是说，本申请实施例中的第一过程状态可定义为如下矩阵x：
x＝[v
x,v
y,r,ω
FL,ω
FR,ω
RL,ω
RR,μ]
相应地，第一过程协方差Q(k)可定义为对角矩阵Q1，
即：
其中，
代表纵向速度对应的过程协方差系数，
代表横向速度对应的过程协方差系数，q
r代表横摆角速度对应的过程协方差系数，
代表前左轮的轮速对应的过程协方差系数，
代表前右轮的轮速对应的过程协方差系数，
代表后左轮的轮速对应的过程协方差系数，
代表前右轮的轮速对应的过程协方差系数，q
μ代表路面附着系数对应的过程协方差系数。
S402、根据车辆的驾驶状态数据确定第二过程协方差Q(k)和第一测量协方差R(k)。
在一些可行的实施方式中，根据车辆的驾驶状态数据确定第二过程协方差Q(k)和第一测量协方差R(k)。具体地，上述根据车辆的驾驶状态数据确定第二过程协方差Q(k)，可理解为：当路面附着系数μ大于或者等于第一预设路面附着系数阈值μ
TH，且侧向加速度变化率的绝对值
大于预设侧向加速度变化率阈值
和/或，方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第一预设过程协方差矩阵作为第二过程协方差Q(k)。或者，当路面附着系数μ大于或者等于第一预设路面附着系数阈值μ
TH，且侧向加速度变化率的绝对值
小于或者等于预设侧向加速度变化率阈值
以及方向盘转速的绝对值
小于或者等于预设方向盘转速阈值
时，根据横向加速度a
y确定第二过程协方差Q(k)。或者，当路面附着系数μ小于第一预设路面附着系数阈值μ
TH，且侧向加速度变化率的绝对值
大于预设侧向加速度变化率阈值
和/或，方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第二预设过程协方差矩阵作为第二过程协方差Q(k)。或者，当路面附着系数μ小于第一预设路面附着系数阈值μ
TH，且侧向加速度变化率的绝对值
小于或者等于预设侧向加速度变化率阈值
以及方向盘转速的绝对值
小于或者等于预设方向盘转速阈值
时，获取第三预设过程协方差矩阵作为第二过程协方差Q(k)。
示例性地，请参见图5，图5是本申请实施例提供的基于驾驶状态数据确定第二过程协方差Q(k)的示意图。如图5所示，当车辆行驶在路面附着系数μ大于或者等于给定的路面附着系数阈值(即第一预设路面附着系数阈值μ
TH)的路面上时，若侧向加速度变化率的绝对值
大于给定的侧向加速度变化率的阈值(即预设侧向加速度变化率阈值
)，和/或，方向盘转速的绝对值
大于给定的方向盘转速的阈值(即预设方向盘转速阈值
)，则第二过程协方差Q(k)可以取第一预设过程协方差矩阵。例如，第一预设过程协方差矩阵可以为瞬态工况下的最优过程协方差矩阵。示例性地，如图5中的State0，第一预设过程协方差矩阵中
q
r＝0.1215，
(即
)；若侧向加速度变化率的绝对值
小于或者等于预设侧向加速度变化率阈值
且方向盘转速的绝对值
小于或者等于预设方向盘转速阈值
则可进一步根据车辆的横向加速度
y的绝对值范围，对第二过程协方差Q(k)进行进一步的自适应调整，如下图8中的State1至State5。具体地，如图5中的State1，当满足|a
y|≤2m/s2的进入条件时，第二过程协方差Q(k)取Q1(k)，示例性地，Q1(k)中
q
r＝0.6197，
(即
)，当满足|a
y|＞2.5m/s2的退出条件时，第二过程协方差Q(k)不再取Q1(k)。也就是说，在第二过程协方差Q(k)取Q1(k)期间，当|a
y|在(2,2.5m/s
2]区间波动时，Q(k)仍然可以等于Q1(k)，这是因为在车辆实际运行过程中，车辆的横向加速度a
y可能是持续性波动的，因此，为避免第二过程协方差Q(k)频繁变化，需要设置如图5中的退出条件，使得在Q(k)取Q1(k)期间，当满足|a
y|＞2.5m/s
2的退出条件时，Q(k)才不再取Q1(k)。如图5中的State2，当满足2＜|a
y|≤4m/s
2的进入条件时，第二过程协方差Q(k)取Q2(k)，示例性地，Q2(k)中
q
r＝1.305，
(即
)，当满足|a
y|≤2或者|a
y|＞4.5m/s
2的退出条件时，第二过程协方差Q(k)不再取Q2(k)。如图5中的State3，当满足4＜|a
y|≤6m/s
2的进入条件时，第二过程协方差Q(k)取Q3(k)，示例性地，Q3(k)中
q
r＝0.8629，
(即
)，当满足|a
y|≤4或者|a
y|＞6.5m/s
2的退出条件时，第二过程协方差Q(k)不再取Q3(k)。如图5中的State4，当满足6＜|a
y|≤8m/s
2的进入条件时，第二过程协方差Q(k)取Q4(k)，示例性地，Q4(k)中
q
r＝1.491，
(即
)，当满足|a
y|≤6m/s
2或者|a
y|＞8.5m/s
2的退出条件时，第二过程协方差Q(k)不再取Q4(k)。如图5中的State5，当满足|a
y|＞8m/s
2的进入条件时，第二过程协方差Q(k)取Q5(k)，示例性地，Q5(k)中
q
r＝3.013，
(即
)，当满足|a
y|≤8m/s
2的退出条件时，第二过程协方差Q(k)不再取Q5(k)。
如图5中当车辆行驶在路面附着系数μ小于第一预设路面附着系数阈值μ
TH的路面上时，若侧向加速度变化率的绝对值
大于预设侧向加速度变化率阈值
和/或，方向盘转速的绝对值
大于预设方向盘转速阈值
则第二过程协方差Q(k)可以取第二预设过程协方差矩阵。其中，第一预设过程协方差矩阵和第二预设过程协方差矩阵可以相同，也可以不同，在此不做限制。示例性地，如图5中的State6，第二预设过程协方差矩阵中
q
r＝0.1215，
(即
)；若侧向加 速度变化率的绝对值
小于或者等于预设侧向加速度变化率阈值
且方向盘转速的绝对值
小于或者等于预设方向盘转速阈值
则第二过程协方差Q(k)为第二预设过程协方差矩阵，如图5中的State7，第三预设过程协方差矩阵中
q
r＝29.994，
(即
)。
需要说明的是，如图5中的state0～state7中的q
μ的取值可以相同，也可以不同，该q
μ可以是一个预设值，或者也可以基于车辆实际行驶路面的路面附着系数计算出的值，在此不做限制。
在一些可行的实施方式中，上述根据车辆的驾驶状态数据确定第一测量协方差R(k)，可理解为：当不同车轮的轮速差的绝对值的最大值max|Δω
i|大于预设轮速差阈值Δω
TH，和/或，不同车轮的轮速的绝对值的最小值min|ω
i|小于预设轮速阈值ω
TH时，获取第一预设测量协方差矩阵作为第一测量协方差R(k)；当不同车轮的轮速差的绝对值的最大值max|Δω
i|小于或者等于预设轮速差阈值Δω
TH，且不同车轮的轮速的绝对值的最小值min|ω
i|大于或者等于预设轮速阈值ω
TH时，根据路面附着系数μ确定第一测量协方差R(k)。其中，上述根据路面附着系数μ确定第一测量协方差R(k)，可以理解为：当路面附着系数μ大于或者等于第二预设路面附着系数阈值μ′
TH时，获取第二预设测量协方差矩阵作为第一测量协方差R(k)；当路面附着系数μ小于第二预设路面附着系数阈值μ′
TH时，获取第三预设测量协方差矩阵作为第一测量协方差R(k)。
在一种实现中，第一测量协方差R(k)可定义为对角矩阵R1，
即：
其中，
r
r分别代表纵向加速度测量对应的测量协方差系数、横向加速度测量对应的测量协方差系数及横摆角速度测量对应的测量协方差系数，
分别代表前左轮、前右轮、后左轮、后右轮的轮速测量对应的测量协方差系数。
可选的，第一测量协方差R(k)也可以定义为对角矩阵R2，
即：
其中，
r
r分别代表纵向加速度对应的测量协方差系数、横向加速度对应的测量协方差系数及横摆角速度对应的测量协方差系数，
分别代表前左轮、前右轮、 后左轮、后右轮的轮速对应的测量协方差系数，
分别代表基于运动学估计得到的车辆质心侧偏角对应的测量协方差系数、基于神经网络估计得到的车辆质心侧偏角对应的测量协方差系数以及基于视觉估计得到的车辆质心侧偏角对应的测量协方差系数。
示例性地，请参见图6，图6是本申请实施例提供的基于驾驶状态数据确定第一测量协方差R(k)的示意图。如图6所示，当不同车轮的轮速差的绝对值的最大值max|Δω
i|大于给定的轮速差阈值(即预设轮速阈值ω
TH)，和/或，不同车轮的轮速的绝对值的最小值min|ω
i|小于给定的轮速的阈值(即预设轮速阈值ω
TH)时，第一测量协方差R(k)取如图6中state0对应的第一预设测量协方差矩阵，例如，第二预设测量协方差矩阵可以为车轮抱死或打滑下的最优测量协方差矩阵；当不同车轮的轮速差的绝对值的最大值max|Δω
i|小于或者等于预设轮速阈值ω
TH，且不同车轮的轮速的绝对值的最小值min|ω
i|大于或者等于的预设轮速阈值ω
TH时，测量协方差再根据路面附着系数进行进一步的自适应调整：当路面附着系数μ大于第二预设路面附着系数阈值μ′
TH时，第一测量协方差R(k)取如图6中state1对应的第二预设测量协方差矩阵，例如，第二预设测量协方差矩阵可以为高附工况下的最优过程协方差矩阵；当路面附着系数μ小于或者等于第二预设路面附着系数阈值μ′
TH时，第一测量协方差R(k)取如图6中state2对应的第三预设测量协方差矩阵，例如，第三预设测量协方差矩阵可以为低附工况下的最优过程协方差矩阵。
S403、获取车辆传感器的第一测量值y
h。
在一些可行的实施方式中，获取车辆传感器的第一测量值y
h。该车辆传感器的第一测量值y
h可以包括车辆真实传感器测得的测量值，例如，第一测量值y
h可包括通过IMU测量得到的纵向加速度a
x、横向加速度a
y以及横摆角速度r，通过WSS测量得到的前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR等，在此不做限制。也就是说，y
h＝[a
x,a
y,r,ω
FL,ω
RR,ω
RL,ω
RR]，相应地，第一测量协方差
可选的，第一测量值y
h也可以包括由车辆真实传感器测得的测量值和基于车辆虚拟传感器获取的测量值。其中，车辆虚拟传感器的测量值可包括通过神经网络估计得到的测量值，和/或基于运动学的状态估计得到的测量值，和/或基于视觉的状态估计得到的测量值等，在此不做限制。例如，基于运动学的状态估计得到车辆质心侧偏角β
Kinematic，基于神经网络的状态估计得到车辆质心侧偏角β
NN，以及基于视觉的状态估计得到车辆质心侧偏角β
Camera，因此，第一测量值y
h＝[a
x,a
y,r,ω
FL,ω
FR,ω
RL,ω
RR,β
Kinematic,β
NN,β
Cmmera]，相应地，第一测量协方差
等，具体根据实际场景确定，在此不做限制。为方便理解，本申请实施例中所涉及的第一测量值皆以包括由车辆真实传感器测得的测量值和基于车辆虚拟传感器获取的测量值，第一测量协方差包括车辆真实传感器的测量值对应的测量协方差系数和车辆虚拟传感器的测量值对应的测量协方差系数为例进行示意性说明。
需要说明的是，本申请通过对车辆真实传感器的测量值和测量协方差进行扩展，即第一测量值y
h除了包括车辆真实传感器测得的测量值，还包括基于车辆虚拟传感器获取的测量值，相应地，第一测量协方差除了包括车辆真实传感器的测量值对应的测量协方差系数，还包括车辆虚拟传感器的测量值对应的测量协方差系数，可以提高对车辆状态参数的估计精度。
S404、根据第一测量值y
h、第一过程状态x、第一过程协方差Q、第二过程协方差Q(k)和第一测量协方差R(k)确定车辆的车辆状态参数。
在一些可行的实施方式中，根据第一测量值y
h、第一过程状态x、第一过程协方差Q、第二过程协方差Q(k)和第一测量协方差R(k)可确定车辆的车辆状态参数。具体地，上述根据第一测量值y
h、第一过程状态x、第一过程协方差Q、第二过程协方差Q(k)和第一测量协方差R(k)确定车辆的车辆状态参数，可理解为：根据第一过程状态x和第一过程协方差Q确定第一关键点数据χ
i(k+1|k)和第二测量值γ
i(k)，第一关键点数据χ
i(k+1|k)和第二测量值γ
i(k)皆满足高斯分布；根据第一关键点数据χ
i(k+1|k)，第二测量值γ
i(k)，第二过程协方差Q(k)，第一测量协方差R(k)以及第一测量值y
h确定车辆的车辆状态参数。其中，上述根据第一关键点数据χ
i(k+1|k)，第二测量值γ
i(k)，第二过程协方差Q(k)，第一测量协方差R(k)、第一测量值y
h以及控制变量状态u(k)确定车辆的车辆状态参数，可理解为：根据第一关键点数据χ
i(k+1|k)和第二过程协方差Q(k)确定先验过程状态
和先验过程协方差
根据第二测量值γ
i(k)和第一测量协方差R(k)确定测量估计值
和测量估计协方差
根据第一关键点数据χ
i(k+1|k)、第二测量值γ
i(k)、先验过程状态
测量估计值
以及测量估计协方差
确定卡尔曼反馈增益矩阵K(k+1|k)；根据卡尔曼反馈增益矩阵K(k+1|k)、先验过程协方差
测量估计协方差
先验过程状态
测量估计值
以及第一测量值y
h(k)确定后验过程状态
和后验过程协方差
根据后验过程状态
确定车辆的车辆状态参数。
示例性地，请参见图7，图7是本申请实施例提供的另一种车辆状态参数估计方法的流程示意图。如图7所示，该方法主要包括以下步骤S701～S705：
S701、根据第一过程状态和第一过程协方差生成Sigma points。
在一种实现方式中，获取k时刻下的第一过程状态x和第一过程协方差Q，进而可根据第一过程状态x和第一过程协方差Q生成Sigma points。具体地，可以根据k时刻下的第一过程状态x的采样值
和第一过程协方差Q的采样值
生成k时刻下的Sigma pointsχ
i(k)。其中，所生成的Sigma pointsχ
i(k)满足：
其中，χ
i(k)表示k时刻下的Sigma points，
表示k时刻下第一过程状态x的采样值，
表示k时刻下第一过程协方差Q的采样值，n表示第一过程状态x的维度(例如n＝8)，λ表示用于计算权重的预设系数。需要说明的是，Sigma points是从原始高斯分布(即基于第一过程状态均值和第一过程协方差的高斯分布)中提取出的包括均值点在内的一系列代表点，它们分布在第一过程状态均值的周围且代表了整个高斯分布，通常来说，从原始高斯分布中提取的点的数目越多，UKF对非线性模型的近似就越精确。
S702、对生成的Sigma points进行无迹变换得到第一关键点数据和第二测量值。
在一种实现方式中，通过对生成的Sigma points进行无迹变换，可得到第一关键点数据χ
i(k+1|k)和第二测量值γ
i(k)。具体地，可以基于7自由度(degree of freedom，DOF)非线性车辆动力学模型和测量模型，分别对生成的Sigma pointsχ
i(k)进行无迹变换，得到在k时刻预测得到的在k+1时刻的新的Sigma points，即第一关键点数据χ
i(k+1|k)，以及根据χ
i(k)得到第二测量值γ
i(k)。需要说明的是，变换后得到的新的Sigma points(即第一关键点数据χ
i(k+1|k))和第二测量值也可近似成新的高斯分布。具体地，第一关键点数据χ
i(k+1|k)和第二测量值γ
i(k)分别满足：
χ
i(k+1|k)＝f(χ
i(k),u(k))+w
γ
i(k)＝h(χ
i(k),u(k))+v
其中，f表示状态转移函数，u(k)表示控制变量状态，w表示过程噪声，h表示测量函数，v表示测量噪声。
S703、根据第一关键点数据和第二过程协方差确定先验过程状态和先验过程协方差，根据第二测量值和第一测量协方差确定测量估计值和测量估计协方差，以及根据第一关键点数据、第二测量值、先验过程状态、测量估计值以及测量估计协方差确定卡尔曼反馈增益矩阵。
在一种实现方式中，首先，可以根据第一关键点数据χ
i(k+1|k)以及第二过程协方差Q(k)确定先验过程状态
和先验过程协方差
即可以根据第一关键点数据χ
i(k+1|k)以及第二过程协方差Q(k)，对过程状态和过程协方差进行先验估计，得到先验过程状态
和先验过程协方差
具体地，先验过程状态
和先验过程协方差
分别满足：
其中，
表示先验过程状态，即在k时刻预测得到的k+1时刻的过程状态，
表示先验过程协方差，即在k时刻预测得到的k+1时刻的过程协方差，W
i
(m)表示权重系数。
然后，根据第二测量值γ
i(k)和第一测量协方差R(k)确定测量估计值
和测量估计协方差
即根据第二测量值γ
i(k)以及第一测量协方差R(k)，对测量估计值
及测量估计协方差
进行估计，具体地，测量估计值
及测量估计协方差
分别满足：
进一步地，根据γ
i(k)以及测量估计值
第一关键点数据χ
i(k+1|k)以及先验过程状态
计算交叉协方差矩阵
具体地，交叉协方差矩阵
满足：
进一步地，根据测量估计协方差
以及交叉协方差矩阵
计算得到卡尔曼反馈增益矩阵K(k+1|k)。具体地，卡尔曼反馈增益矩阵K(k+1|k)满足：
S704、根据卡尔曼反馈增益矩阵、先验过程协方差、测量估计协方差、先验过程状态、测量估计值以及第一测量值确定后验过程状态和后验过程协方差。
在一种实现方式中，可以根据卡尔曼反馈增益矩阵K(k+1|k)、先验过程协方差
测量估计协方差
先验过程状态
测量估计值
以及第一测量值y
h(k)确定后验过程状态
和后验过程协方差
即根据卡尔曼反馈增益矩阵K(k+1|k)、先验过程协方差
测量估计协方差
先验过程状态
测量估计值
以及第一测量值y
h(k)，对过程状态和过程协方差进行后验估计，得到后验过程状态
和后验过程协方差
具体地，后验过程协方差
和后验过程状态
分别满足：
需要说明的是，后验过程协方差
即预测得到的k+1时刻的过程协方差，后验过程状态
即预测得到的k+1时刻的过程状态。
S705、根据后验过程状态确定车辆的车辆状态参数。
在一种实现方式中，根据后验过程状态
确定k+1时刻下的车辆状态参数
具体地，可以根据后验过程状态
和控制变量状态u(k)，对k+1时刻下的车辆状态参数进行全面的估计：
其中，
表示k+1时刻下的车辆状态参数，g表示输出函数。
需要说明的是，后验过程协方差
和后验过程状态
可以作为k+2时刻下的车辆状态参数估计的输入，即可将后验过程协方差
作为新的过程协方差的采样值，将后验过程状态
作为新的过程状态的采样值，以用于当前时刻的下一时刻的车辆状态估计，或者理解为本申请实施例可以通过当前时刻的上一时刻的车辆状态估计当前时刻的车辆状态参数。也就是说，可基于后验估计生成的新的过程状态及过程协方差，循环进行前述步骤S701至S704。
本申请在车辆状态参数的估计过程中，基于驾驶状态数据自适应调整过程协方差和测量协方差，进而将调整后的过程协方差和测量协方差用于车辆状态估计，可提高车辆状态参数的估计精度。此外，本申请通过引入其他估计方法(例如基于运动学的状态估计、基于神经网络的状态估计以及基于视觉的状态估计)获取的结果作为“虚拟传感器”的测量值，对基于真实传感器测量得到的测量值和计算出的测量协方差进行扩展，进而将扩展后的测量值和测量协方差用于车辆状态估计，可进一步提高对车辆状态的估计精度，有利于提升车辆动力学控制算法的性能。
下面将结合图8～图10对本申请提供的车辆状态参数估计装置进行详细说明。
请参见图8，图8是本申请实施例提供的一种车辆状态参数估计装置的结构示意图。图8所示的车辆状态参数估计装置可以用于执行上述图4～图7所描述的方法实施例中的部分或全部功能。该装置可以是终端，例如车载终端，也可以是终端中的装置，或者是能够和终端匹配使用的装置。其中，该车辆状态参数估计装置还可以为芯片系统。图8所示的车辆状态参数估计装置可以包括收发单元801和处理单元802。其中，处理单元802，用于进行数据处理。收发单元801集成有接收单元和发送单元。收发单元801也可以称为通信单元。或者，也可将收发单元801拆分为接收单元和发送单元。下文的处理单元802和收发单元801同理，下文不再赘述。其中：
收发单元801，用于获取车辆的驾驶状态数据、第一过程状态x和第一过程协方差Q；
处理单元802，用于根据所述车辆的驾驶状态数据确定第二过程协方差Q(k)和第一测量协方差R(k)；
所述收发单元801，用于获取车辆传感器的第一测量值y
h；
所述处理单元802，用于根据所述第一测量值y
h、所述第一过程状态x、所述第一过程协方差Q、所述第二过程协方差Q(k)和所述第一测量协方差R(k)确定所述车辆的车辆状态参数。
在一种可能的实现中，所述驾驶状态数据包括以下一项或多项：侧向加速度变化率
方向盘转速
横向加速度a
y、路面附着系数μ、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL或后右轮的轮速ω
RR。
在一种可能的实现中，所述处理单元802用于：
当所述路面附着系数μ大于或者等于第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
大于预设侧向加速度变化率阈值
和/或，所述方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第一预设过程协方差矩阵作为所述第二过程协方差Q(k)；或者
当所述路面附着系数μ大于或者等于第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
小于或者等于所述预设侧向加速度变化率阈值
以及所述方向盘转速的绝对值
小于或者等于所述预设方向盘转速阈值
时，根据所述横向加速度a
y确定所述第二过程协方差Q(k)；或者
当所述路面附着系数μ小于所述第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
大于预设侧向加速度变化率阈值
和/或，所述方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第二预设过程协方差矩阵作为所述第二过程协方差Q(k)；或者
当所述路面附着系数μ小于所述第一预设路面附着系数阈值μ
TH，且所述侧向加速度变化率的绝对值
小于或者等于所述预设侧向加速度变化率阈值
以及所述方向盘转速的绝对值
小于或者等于所述预设方向盘转速阈值
时，获取第三预设过程协方差矩阵作为所述第二过程协方差Q(k)。
在一种可能的实现中，所述处理单元802用于：
当不同车轮的轮速差的绝对值的最大值max|Δω
i|大于预设轮速差阈值Δω
TH，和/或，不同车轮的轮速的绝对值的最小值min|ω
i|小于预设轮速阈值ω
TH时，获取第一预设测量协方差矩阵作为所述第一测量协方差R(k)；
当不同车轮的轮速差的绝对值的最大值max|Δω
i|小于或者等于所述预设轮速差阈值Δω
TH，且不同车轮的轮速的绝对值的最小值min|ω
i|大于或者等于所述预设轮速阈值ω
TH时，根据所述路面附着系数μ确定所述第一测量协方差R(k)。
在一种可能的实现中，所述处理单元802用于：
当所述路面附着系数μ大于或者等于第二预设路面附着系数阈值μ′
TH时，获取第二预设测量协方差矩阵作为所述第一测量协方差R(k)；
当所述路面附着系数μ小于所述第二预设路面附着系数阈值μ′
TH时，获取第三预设测量协方差矩阵作为所述第一测量协方差R(k)。
在一种可能的实现中，所述车辆传感器的所述第一测量值y
h包括车辆真实传感器测得的测量值和基于车辆虚拟传感器获取的测量值，所述车辆虚拟传感器的测量值通过神经网络得到。
在一种可能的实现中，所述第一过程状态x包括以下一项或多项：
纵向速度v
x、横向速度v
y、横摆角速度r、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR或路面附着系数μ。
在一种可能的实现中，所述第一测量值y
h包括以下一项或多项：
纵向加速度a
x、横向加速度a
y、横摆角速度r、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR、或质心侧偏角β。
在一种可能的实现中，所述车辆状态参数包括以下一项或多项：
纵向速度v
x、横向速度v
y、横摆角速度r、前左轮的轮速ω
FL、前右轮的轮速ω
FR、后左轮的轮速ω
RL、后右轮的轮速ω
RR、路面附着系数μ、纵向加速度a
x、横向加速度a
y、质心侧偏角β。
请参见图9，图9是本申请实施例提供的另一种车辆状态参数估计装置的结构示意图。如图9所示，该车辆状态参数估计装置包括：处理器901、通信接口902和存储器903。其中，处理器901、通信接口902和存储器903通过总线904耦合。
处理器901可以是一个或多个中央处理器(central processing unit，CPU)，在处理器901是一个CPU的情况下，该CPU可以是单核CPU，也可以是多核CPU。
处理器901用于读取存储器中存储的程序，与通信接口902配合执行本申请上述实施例中由车辆状态参数估计装置执行的方法的部分或全部步骤。
存储器903包括但不限于随机存储记忆体(random access memory，RAM)、可擦除可编程只读存储器(erasable programmable rom，EPROM)、只读存储器(read-only memory，ROM)或便携式只读存储器(compact disc read-only memory，CD-ROM)等等，该存储器903用于存储程序，处理器901可以读取存储器903中存储的程序，执行本申请上述实施例中图4～图7所示方法中的各个步骤，在此不再进行赘述。
请参见图10，图10是本申请实施例提供的一种芯片的结构示意图。如图10所示，芯片100可包括：处理器1001，以及耦合于处理器1001的一个或多个通信接口1002。其中：
处理器1001可用于读取和执行计算机可读指令。具体实现中，处理器1001可主要包括控制器、运算器和寄存器。其中，控制器主要负责指令译码，并为指令对应的操作发出控制信号。运算器主要负责执行定点或浮点算数运算操作、移位操作以及逻辑操作等，也可以执行地址运算和转换。寄存器主要负责保存指令执行过程中临时存放的寄存器操作数和中间操作结果等。具体实现中，处理器1001的硬件架构可以是专用集成电路(application specific integrated circuits，ASIC)架构、MIPS架构、ARM架构或者NP架构等等。处理器1001可以是单核的，也可以是多核的。
通信接口1002可用于输入待处理的数据至处理器1001，并且可以向外输出处理器1001的处理结果。例如，通信接口1002可以是通用输入输出(general purpose input output，GPIO)接口，可以和多个外围设备(如显示器(LCD)、摄像头(camera)、射频(radio frequency，RF)模块等等)连接。通信接口1002通过总线1003与处理器1001相连。
本申请中，处理器1001可用于从存储器中调用本申请的一个或多个实施例提供的车辆状态参数估计方法的实现程序，并执行该程序包含的指令。通信接口1002可用于输出处理器1001的执行结果。本申请中，通信接口1002可具体用于输出处理器1001的车辆状态参数估计结果。关于本申请的一个或多个实施例提供的车辆状态参数估计方法可参考前述图4～图7所示各个实施例，这里不再赘述。
需要说明的，处理器1001、通信接口1002各自对应的功能既可以通过硬件设计实现，也可以通过软件设计来实现，还可以通过软硬件结合的方式来实现，这里不作限制。
基于同一发明构思，本申请实施例中提供的车辆状态参数估计装置解决问题的原理与有益效果与本申请方法实施例中车辆状态参数估计方法解决问题的原理和有益效果相似，可以参见方法的实施的原理和有益效果，并且，各个相关模块所执行的各个步骤之间的关系亦可参考前述实施例中相关内容的描述，为简洁描述，在这里不再赘述。
本申请实施例中还提供了一种计算机存储介质，可以用于存储图4～图7所示实施例中车辆状态参数估计装置所用的计算机软件指令，其包含用于执行上述实施例中为车辆状态参数估计装置所设计的程序。该存储介质包括但不限于快闪存储器、硬盘、固态硬盘。
在本申请实施例中还提供了一种计算机程序产品，该计算机产品被车辆状态参数估计装置运行时，可以执行上述图4～图7所示实施例中为车辆状态参数估计装置所设计的车辆状态参数估计方法。
本申请实施例还提供一种传感器系统，用于为车辆提供车辆状态参数估计功能。其包含至少一个本申请上述实施例提到的车辆状态参数估计装置，以及，摄像头或雷达等其他传感 器中的至少一个，该系统内的至少一个传感器装置可以集成为一个整机或设备，或者该系统内的至少一个传感器装置也可以独立设置为元件或装置。
本申请实施例还提供一种系统，应用于无人驾驶或智能驾驶中，其包含至少一个本申请上述实施例提到的车辆状态参数估计装置、摄像头、雷达等传感器其他传感器中的至少一个，该系统内的至少一个装置可以集成为一个整机或设备，或者该系统内的至少一个装置也可以独立设置为元件或装置。
进一步，上述任一系统可以与车辆的中央控制器进行交互，为所述车辆驾驶的决策或控制提供车辆状态参数等信息。
本申请实施例还提供一种终端，所述终端包括至少一个本申请上述实施例提到的车辆状态参数估计装置或上述任一系统。例如，上述终端可包括车辆、摄像头或无人机等，在此不做限制。
可理解的，本申请方法实施例中的步骤可以根据实际需要进行顺序调整、合并和删减。
本申请装置实施例中的模块可以根据实际需要进行合并、划分和删减。
本领域普通技术人员可以理解，在本申请的各种实施例中，上述各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以所述权利要求的保护范围为准。
Claims (22)
- 一种车辆状态参数估计方法，其特征在于，所述方法包括：获取车辆的驾驶状态数据、第一过程状态x和第一过程协方差Q；根据所述车辆的驾驶状态数据确定第二过程协方差Q(k)和第一测量协方差R(k)；获取车辆传感器的第一测量值y h；根据所述第一测量值y h、所述第一过程状态x、所述第一过程协方差Q、所述第二过程协方差Q(k)和所述第一测量协方差R(k)确定所述车辆的车辆状态参数。
- 根据权利要求1所述的方法，其特征在于，所述驾驶状态数据包括以下一项或多项：侧向加速度变化率方向盘转速
横向加速度a y、路面附着系数μ、前左轮的轮速ω FL、前右轮的轮速ω FR、后左轮的轮速ω RL或后右轮的轮速ω RR。
- 根据权利要求2所述的方法，其特征在于，所述根据所述车辆的驾驶状态数据确定第二过程协方差Q(k)，包括：当所述路面附着系数μ大于或者等于第一预设路面附着系数阈值μ TH，且所述侧向加速度变化率的绝对值大于预设侧向加速度变化率阈值
和/或，所述方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第一预设过程协方差矩阵作为所述第二过程协方差Q(k)；或者
当所述路面附着系数μ大于或者等于第一预设路面附着系数阈值μ TH，且所述侧向加速度变化率的绝对值小于或者等于所述预设侧向加速度变化率阈值
以及所述方向盘转速的绝对值
小于或者等于所述预设方向盘转速阈值
时，根据所述横向加速度a y确定所述第二过程协方差Q(k)；或者
当所述路面附着系数μ小于所述第一预设路面附着系数阈值μ TH，且所述侧向加速度变化率的绝对值大于预设侧向加速度变化率阈值
和/或，所述方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第二预设过程协方差矩阵作为所述第二过程协方差Q(k)；或者
当所述路面附着系数μ小于所述第一预设路面附着系数阈值μ TH，且所述侧向加速度变化率的绝对值小于或者等于所述预设侧向加速度变化率阈值
以及所述方向盘转速的绝对值
小于或者等于所述预设方向盘转速阈值
时，获取第三预设过程协方差矩阵作为所述第二过程协方差Q(k)。
- 根据权利要求2所述的方法，其特征在于，所述根据所述车辆的驾驶状态数据确定第一测量协方差R(k)，包括：当不同车轮的轮速差的绝对值的最大值max|Δω i|大于预设轮速差阈值Δω TH，和/或，不同车轮的轮速的绝对值的最小值min|ω i|小于预设轮速阈值ω TH时，获取第一预设测量协方差矩阵作为所述第一测量协方差R(k)；当不同车轮的轮速差的绝对值的最大值max|Δω i|小于或者等于所述预设轮速差阈值Δω TH，且不同车轮的轮速的绝对值的最小值min|ω i|大于或者等于所述预设轮速阈值ω TH时，根据所述路面附着系数μ确定所述第一测量协方差R(k)。
- 根据权利要求4所述的方法，其特征在于，所述根据所述路面附着系数μ确定所述第一测量协方差R(k)，包括：当所述路面附着系数μ大于或者等于第二预设路面附着系数阈值μ′ TH时，获取第二预设测量协方差矩阵作为所述第一测量协方差R(k)；当所述路面附着系数μ小于所述第二预设路面附着系数阈值μ′ TH时，获取第三预设测量协方差矩阵作为所述第一测量协方差R(k)。
- 根据权利要求1-5任一项所述的方法，其特征在于，所述车辆传感器的所述第一测量值y h包括车辆真实传感器测得的测量值和基于车辆虚拟传感器获取的测量值，所述车辆虚拟传感器的测量值通过神经网络得到。
- 根据权利要求1-6任一项所述的方法，其特征在于，所述第一过程状态x包括以下一项或多项：纵向速度v x、横向速度v y、横摆角速度r、前左轮的轮速ω FL、前右轮的轮速ω FR、后左轮的轮速ω RL、后右轮的轮速ω RR或路面附着系数μ。
- 根据权利要求1-7任一项所述的方法，其特征在于，所述第一测量值y h包括以下一项或多项：纵向加速度a x、横向加速度a y、横摆角速度r、前左轮的轮速ω FL、前右轮的轮速ω FR、后左轮的轮速ω RL、后右轮的轮速ω RR、或质心侧偏角β。
- 根据权利要求1-8任一项所述的方法，其特征在于，所述车辆状态参数包括以下一项或多项：纵向速度v x、横向速度v y、横摆角速度r、前左轮的轮速ω FL、前右轮的轮速ω FR、后左轮的轮速ω RL、后右轮的轮速ω RR、路面附着系数μ、纵向加速度a x、横向加速度a y、质心侧偏角β。
- 一种车辆状态参数估计装置，其特征在于，所述装置包括：收发单元，用于获取车辆的驾驶状态数据、第一过程状态x和第一过程协方差Q；处理单元，用于根据所述车辆的驾驶状态数据确定第二过程协方差Q(k)和第一测量协方差R(k)；所述收发单元，用于获取车辆传感器的第一测量值y h；所述处理单元，用于根据所述第一测量值y h、所述第一过程状态x、所述第一过程协方差Q、所述第二过程协方差Q(k)和所述第一测量协方差R(k)确定所述车辆的车辆状态参数。
- 根据权利要求10所述的装置，其特征在于，所述驾驶状态数据包括以下一项或多项：侧向加速度变化率方向盘转速
横向加速度a y、路面附着系数μ、前左轮的轮速ω FL、前右轮的轮速ω FR、后左轮的轮速ω RL或后右轮的轮速ω RR。
- 根据权利要求11所述的装置，其特征在于，所述处理单元用于：当所述路面附着系数μ大于或者等于第一预设路面附着系数阈值μ TH，且所述侧向加速度变化率的绝对值大于预设侧向加速度变化率阈值
和/或，所述方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第一预设过程协方差矩阵作为所述第二过程协方差Q(k)；或者
当所述路面附着系数μ大于或者等于第一预设路面附着系数阈值μ TH，且所述侧向加速度变化率的绝对值小于或者等于所述预设侧向加速度变化率阈值
以及所述方向盘转速的绝对值
小于或者等于所述预设方向盘转速阈值
时，根据所述横向加速度a y确定所述第二过程协方差Q(k)；或者
当所述路面附着系数μ小于所述第一预设路面附着系数阈值μ TH，且所述侧向加速度变化率的绝对值大于预设侧向加速度变化率阈值
和/或，所述方向盘转速的绝对值
大于预设方向盘转速阈值
时，获取第二预设过程协方差矩阵作为所述第二过程协方差Q(k)；或者
当所述路面附着系数μ小于所述第一预设路面附着系数阈值μ TH，且所述侧向加速度变化率的绝对值小于或者等于所述预设侧向加速度变化率阈值
以及所述方向盘转速的 绝对值
小于或者等于所述预设方向盘转速阈值
时，获取第三预设过程协方差矩阵作为所述第二过程协方差Q(k)。
- 根据权利要求11所述的装置，其特征在于，所述处理单元用于：当不同车轮的轮速差的绝对值的最大值max|Δω i|大于预设轮速差阈值Δω TH，和/或，不同车轮的轮速的绝对值的最小值min|ω i|小于预设轮速阈值ω TH时，获取第一预设测量协方差矩阵作为所述第一测量协方差R(k)；当不同车轮的轮速差的绝对值的最大值max|Δω i|小于或者等于所述预设轮速差阈值Δω TH，且不同车轮的轮速的绝对值的最小值min|ω i|大于或者等于所述预设轮速阈值ω TH时，根据所述路面附着系数μ确定所述第一测量协方差R(k)。
- 根据权利要求13所述的装置，其特征在于，所述处理单元用于：当所述路面附着系数μ大于或者等于第二预设路面附着系数阈值μ′ TH时，获取第二预设测量协方差矩阵作为所述第一测量协方差R(k)；当所述路面附着系数μ小于所述第二预设路面附着系数阈值μ′ TH时，获取第三预设测量协方差矩阵作为所述第一测量协方差R(k)。
- 根据权利要求10-14任一项所述的装置，其特征在于，所述车辆传感器的所述第一测量值y h包括车辆真实传感器测得的测量值和基于车辆虚拟传感器获取的测量值，所述车辆虚拟传感器的测量值通过神经网络得到。
- 根据权利要求10-15任一项所述的装置，其特征在于，所述第一过程状态x包括以下一项或多项：纵向速度v x、横向速度v y、横摆角速度r、前左轮的轮速ω FL、前右轮的轮速ω FR、后左轮的轮速ω RL、后右轮的轮速ω RR或路面附着系数μ。
- 根据权利要求10-16任一项所述的装置，其特征在于，所述第一测量值y h包括以下一项或多项：纵向加速度a x、横向加速度a y、横摆角速度r、前左轮的轮速ω FL、前右轮的轮速ω FR、后左轮的轮速ω RL、右轮的轮速ω RR、或质心侧偏角β。
- 根据权利要求10-17任一项所述的装置，其特征在于，所述车辆状态参数包括以下一项或多项：纵向速度v x、横向速度v y、横摆角速度r、前左轮的轮速ω FL、前右轮的轮速ω FR、后左轮的轮速ω RL、后右轮的轮速ω RR、路面附着系数μ、纵向加速度a x、横向加速度a y、质心侧偏角β。
- 一种车辆状态参数估计装置，其特征在于，包括：处理器和存储器；所述存储器用于存储一个或多个程序，所述一个或多个程序包括计算机执行指令，当该装置运行时，所述处理器执行所述存储器存储的所述一个或多个程序以使该装置执行如权利要求1-9任一项所述的方法。
- 一种计算机存储介质，其特征在于，所述计算机存储介质上存储有计算机程序，当所述计算机程序在计算机上运行时，使得所述计算机执行如权利要求1-9任一项所述的方法。
- 一种芯片，其特征在于，所述芯片包括：处理器和通信接口，所述处理器用于从所述通信接口调用并运行指令，当所述处理器执行所述指令时，实现如权利要求1-9中任一项所述的方法。
- 一种终端，所述终端包括如权利要求10-18中任一项所述的装置。
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|---|---|---|---|---|
| US5094213A (en) * | 1991-02-12 | 1992-03-10 | General Motors Corporation | Method for predicting R-step ahead engine state measurements |
| CN105549003A (zh) * | 2015-12-02 | 2016-05-04 | 华域汽车系统股份有限公司 | 一种汽车雷达目标跟踪方法 |
| CN106515740A (zh) * | 2016-11-14 | 2017-03-22 | 江苏大学 | 基于icdkf的分布式电驱动汽车行驶状态参数估计算法 |
| CN108357498A (zh) * | 2018-02-07 | 2018-08-03 | 北京新能源汽车股份有限公司 | 一种车辆状态参数确定方法、装置及汽车 |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5094213A (en) * | 1991-02-12 | 1992-03-10 | General Motors Corporation | Method for predicting R-step ahead engine state measurements |
| CN105549003A (zh) * | 2015-12-02 | 2016-05-04 | 华域汽车系统股份有限公司 | 一种汽车雷达目标跟踪方法 |
| CN106515740A (zh) * | 2016-11-14 | 2017-03-22 | 江苏大学 | 基于icdkf的分布式电驱动汽车行驶状态参数估计算法 |
| CN108357498A (zh) * | 2018-02-07 | 2018-08-03 | 北京新能源汽车股份有限公司 | 一种车辆状态参数确定方法、装置及汽车 |
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