WO2023036286A1 - 坡度估计方法、装置、电子设备以及存储介质 - Google Patents
坡度估计方法、装置、电子设备以及存储介质 Download PDFInfo
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- the embodiments of the present application relate to the field of computer technology, for example, to a slope estimation method, device, electronic equipment, and storage medium.
- Road slope estimation algorithms can be divided into methods based on kinematics models and methods based on dynamics models according to the different vehicle models used.
- the method based on the dynamic model uses the brake wheel cylinder pressure and the output torque of the power source as the input of the estimator. This method is very dependent on the vehicle model.
- the method based on the kinematics model uses a small number of sensors, based on the signal of the longitudinal acceleration sensor, the signal in the vehicle body coordinate system measured by the longitudinal acceleration sensor, It will be affected by the posture of the vehicle body, especially when the vehicle starts, accelerates, and decelerates at low speeds, the posture of the vehicle body is unstable, resulting in a certain deviation between the longitudinal acceleration of the vehicle measured by the longitudinal acceleration sensor and the longitudinal acceleration of the actual vehicle, which affects the slope estimation accuracy. have a certain impact.
- the present application provides a slope estimation method, device, electronic equipment and storage medium to solve the problem that when the vehicle starts, accelerates, and decelerates at low speed, the value of the acceleration sensor fluctuates seriously due to the instability of the vehicle itself, which eventually leads to unreliable slope estimation values. question.
- the embodiment of the present application provides a slope estimation method, including:
- the error covariance of the optimal estimated value of the slope of the vehicle at the previous moment is determined;
- the Kalman gain at the current moment the measured value of the slope at the current moment, the predicted value of the slope at the current moment, the optimal estimated value of the slope at the previous moment, the threshold value of the slope estimation, and the optimal slope at the previous moment.
- the embodiment of the present application also provides a slope estimation device, including:
- the slope prediction value determination module is configured to determine the slope prediction value of the vehicle at the current moment according to the vehicle's running acceleration at the current moment and the optimal estimated value of the slope of the vehicle at the previous moment;
- the error covariance determination module is configured to determine the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the vehicle's slope optimal estimate at the last moment;
- the Kalman gain determination module is configured to determine the Kalman gain at the current moment according to the error covariance of the vehicle's slope measurement value at the current moment and the error covariance of the vehicle's slope prediction value at the current moment;
- the slope estimation value determination module is set to be based on the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimate at the last moment, the slope estimation threshold, and the difference between the optimal estimated value of the slope at the previous moment and the predicted value of the slope at the current moment to determine the optimal estimated value of the slope of the vehicle at the current moment.
- the embodiment of the present application also provides an electronic device, including:
- processors one or more processors
- memory configured to store one or more programs
- the one or more processors implement the slope estimation method provided in any embodiment of the present application.
- the embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the slope estimation method provided in any embodiment of the present application is implemented.
- FIG. 1 is a flow chart of a slope estimation method provided in Embodiment 1 of the present application.
- FIG. 2A is a flowchart of a slope estimation method provided in Embodiment 2 of the present application.
- Fig. 2B is a schematic diagram of a vehicle traveling uphill according to Embodiment 2 of the present application.
- FIG. 3 is a schematic structural diagram of a slope estimation device provided in Embodiment 3 of the present application.
- FIG. 4 is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present application.
- a vehicle motion model based on the measurement results of the vehicle's longitudinal acceleration sensor and the road gradient is constructed.
- the vehicle motion model can be determined by the following formula:
- a x represents the measurement result of the longitudinal acceleration sensor of the vehicle
- g represents the gravitational acceleration
- i represents the road gradient.
- the vehicle motion model is transformed into a linear system state-space model.
- the time derivative can be approximately zero, and a differential equation can be obtained:
- v represents the vehicle speed
- i represents the road gradient
- z k represents the observed value of the slope estimation system
- w k is the process noise, which represents the deviation between the state space equation of the slope estimation system and the actual measurement process
- v k is the measurement noise, which represents the longitudinal acceleration sensor Accuracy error
- u k represents the measurement result of the longitudinal acceleration sensor at the current moment.
- the gradient estimation is performed based on the improved Kalman filter algorithm, and the state update link in the Kalman filter algorithm is mainly improved. For details, see the following embodiments.
- Fig. 1 is a flow chart of a slope estimation method provided by Embodiment 1 of the present application. This embodiment is applicable to the situation where a vehicle performs slope estimation when starting, accelerating or decelerating at a low speed.
- This method can be executed by a slope estimation device.
- the device can be realized by software and/or hardware, and can be integrated into an electronic device carrying a slope estimation function, such as a vehicle controller.
- the method may include:
- the vehicle's driving acceleration at the current moment and the optimal estimated value of the vehicle's gradient at the previous moment can be input into the gradient prediction model to obtain the gradient of the vehicle at the current moment Predictive value.
- the predicted slope value of the vehicle at the current moment can be determined by the following formula:
- A represents the state transition matrix
- B represents the input matrix of the slope estimation system
- uk represents the measurement result of the longitudinal acceleration sensor at the current moment, including the driving acceleration of the vehicle at the current moment.
- the error covariance of the vehicle's optimal slope estimation value at the previous moment is input into the error determination model to obtain the error covariance of the vehicle's slope prediction value at the current moment.
- the error covariance of the slope prediction value of the vehicle at the current moment can be determined by the following formula:
- the error covariance of the slope measurement value of the vehicle at the current moment and the error covariance of the slope prediction value of the vehicle at the current moment are input into the Kalman gain determination model, and the Kalman gain of the current moment is output.
- the Kalman gain at the current moment can be determined by the following formula:
- K k represents the Kalman gain at the current moment
- R is the measurement variance obtained from the long-term probability statistics of the measurement data of the longitudinal acceleration sensor
- H represents the error covariance of the slope measurement value of the vehicle at the current moment.
- the optimal estimated value of the slope at the previous moment is smaller than the predicted value of the slope at the current moment, and the absolute value of the difference between the optimal estimated value of the slope at the previous moment and the predicted value of the slope at the current moment is greater than the estimated slope threshold, then the The optimal estimated value of the slope at the last moment is summed with the estimated slope threshold, and the result of the sum is used as the optimal estimated value of the slope of the vehicle at the current moment.
- the optimal estimated value of the slope at the previous moment is greater than the predicted value of the slope at the current moment, and the absolute value of the difference between the optimal estimated value of the slope at the previous moment and the predicted value of the slope at the current moment is greater than the estimated slope threshold, then the The optimal estimated value of the slope at the last moment is subtracted from the estimated threshold value of the slope, and the result of the difference is taken as the optimal estimated value of the slope of the vehicle at the current moment.
- the optimal estimated value of the slope at the current moment is determined.
- the optimal estimated value of the slope at the current moment can be determined by the following formula:
- ⁇ i max is represented as the slope estimation threshold
- K k is the Kalman gain at the current moment
- H is the observation matrix
- z k is expressed as the predicted value of the slope at the current moment.
- the error covariance of the optimal estimated value of the slope at the current moment can be determined by the following formula:
- P k represents the error covariance of the optimal estimated value of the slope at the current moment
- I represents the identity matrix
- H represents the observation matrix
- K k represents the Kalman gain at the current moment
- R is the measurement variance obtained from the long-term probability statistics of the measurement data of the longitudinal acceleration sensor.
- the vehicle's slope prediction value at the current moment is determined according to the vehicle's driving acceleration at the current moment, and the vehicle's slope optimal estimate value at the previous moment, and then according to the vehicle's slope at the previous moment.
- the error covariance of the estimated value determines the error covariance of the slope prediction value of the vehicle at the current moment, and then according to the error covariance of the slope measurement value of the vehicle at the current moment and the error covariance of the slope prediction value of the vehicle at the current moment, determine The Kalman gain at the current moment, and then based on the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal estimated value of the slope at the previous moment, the slope estimation threshold, and the slope at the previous moment. The difference between the optimal estimated value and the predicted value of the slope at the current moment determines the optimal estimated value of the slope of the vehicle at the current moment.
- FIG. 2A is a flowchart of a slope estimation method provided in Embodiment 2 of the present application
- FIG. 2B is a schematic diagram of a vehicle traveling uphill provided in Embodiment 2 of the present application.
- the method may include:
- the slope estimation threshold is determined in the following manner: determine the driving time required by the vehicle from the pre-uphill state to the initial uphill state; determine the slope change rate of the slope road where the vehicle is located according to the maximum slope of the road and the driving time; The slope change rate, the slope change proportional coefficient, and the time interval between two adjacent slope estimates determine the slope estimation threshold.
- the state of preparing to go uphill refers to the state in which the front wheels of the vehicle reach the bottom of the slope and are ready to go uphill, such as the state of the vehicle with the dotted line in Figure 2B; the so-called initial uphill state means that the rear wheels of the vehicle have just left the bottom of the slope and the vehicle is fully
- the state when it is on the slope of the ramp is the state of the vehicle with the solid line in Figure 2B.
- Twice the wheelbase of the vehicle is used as the driving path of the vehicle from the pre-uphill state to the initial uphill state, as shown in Figure 2B, where L represents the wheelbase of the vehicle, and v represents the vehicle speed. Then, according to the driving path and vehicle speed, determine the driving time required by the vehicle from the pre-uphill state to the initial uphill state. For example, the driving time can be determined by the following formula:
- t slope represents the driving time required by the vehicle from the preparatory uphill state to the initial uphill state
- L represents the wheelbase of the vehicle
- v represents the vehicle speed
- the slope change rate of the slope road where the vehicle is located is determined according to the maximum slope of the road and the driving time. For example, the maximum slope of the road and the driving time are quotiented, and the result of the quotient is used as the slope change rate of the sloping road where the vehicle is located. As shown in FIG. 2B , ⁇ max represents the maximum slope of the road.
- the slope estimation threshold After determining the slope change rate, determine the slope estimation threshold according to the slope change rate, slope change proportional coefficient, and the time interval between two adjacent slope estimates, which can be the slope change rate, slope change proportional coefficient, and two adjacent The result of multiplying the time intervals for slope estimation is used as the slope estimation threshold.
- the slope change proportional coefficient can be set by those skilled in the art according to actual conditions.
- the wheels When the vehicle starts, accelerates or decelerates at a low speed, the wheels will slip, which will lead to the unstable pitch of the vehicle body, and at the same time, the longitudinal speed of the vehicle based on the wheel speed estimation will be unstable, which will eventually lead to fluctuations in the slope estimation results.
- the slope change proportional coefficient is determined according to the slipping level of the wheels, and different slope change proportional coefficients correspond to different wheel slipping levels.
- the slipping level of the wheel can be determined in the following manner: determine the theoretical longitudinal acceleration according to the gradient resistance and the acceleration resistance; determine the acceleration error according to the theoretical longitudinal acceleration and the actual longitudinal acceleration; determine the acceleration error of the wheel according to the acceleration error and the slip judgment threshold. slip rating.
- the slip judgment threshold can be set by those skilled in the art according to actual conditions.
- the so-called slope resistance refers to the component force of the gravity of the vehicle along the slope of the slope when the wheels are running uphill, and the component force is expressed as a resistance to the running of the wheels.
- the slope resistance can be determined by the acceleration of gravity, the coefficient of rolling resistance and the weight of the vehicle.
- the result of multiplying the acceleration of gravity, the coefficient of rolling resistance and the weight of the vehicle can be used as the slope resistance.
- the so-called acceleration resistance refers to the inertial force that keeps the vehicle moving at a constant speed, which can be determined by the vehicle's driving acceleration and the vehicle's weight.
- the result of multiplying the vehicle's driving acceleration and the weight of the vehicle can be used as the acceleration resistance.
- the acceleration resistance can be determined by the following formula: Among them, F j represents the acceleration resistance, m represents the weight of the vehicle, Indicates the driving acceleration of the vehicle.
- the so-called theoretical longitudinal acceleration refers to the acceleration calculated theoretically when the vehicle is in the direction of the ramp slope.
- the so-called actual longitudinal acceleration refers to the longitudinal acceleration of the vehicle measured by the longitudinal acceleration sensor in the vehicle.
- the sum of the gradient resistance and the acceleration resistance is quotiented with the weight of the vehicle, and the result of the quotient is used as the theoretical longitudinal acceleration, and then the difference between the theoretical longitudinal acceleration and the actual longitudinal acceleration is regarded as the acceleration error, According to the acceleration error and the slip judgment threshold, the slip level of the wheel is determined.
- the acceleration error and the slipping judgment threshold determine the slipping level of the wheel, which may be, if the absolute value of the acceleration error is less than the first slipping judgment threshold, then determine the slipping level of the wheel as the first slipping level; if the acceleration error The absolute value is greater than the first slipping judgment threshold and less than the second slipping judgment threshold, or the absolute value of the acceleration error is greater than the third slipping judgment threshold, then it is determined that the slipping level of the wheel is the second slipping level; if the absolute value of the acceleration error is greater than the second If the slip judgment threshold is less than the third slip judgment threshold, then it is determined that the slipping level of the wheel is the third slipping level; wherein, the third slip judgment threshold is greater than the second slip judgment threshold, and the second slip judgment threshold is greater than the first slip judgment threshold.
- the third slip judgment threshold, the second slip judgment threshold and the first slip judgment threshold can be set by those skilled in the art according to actual conditions.
- the wheel slipping situation can be accurately identified in time, so as to accurately determine the slope change proportional coefficient, thereby improving the accuracy of the slope estimation threshold, thereby improving the accuracy of the slope estimation.
- the vehicle's slope prediction value at the current moment is determined according to the vehicle's driving acceleration at the current moment, and the vehicle's slope optimal estimate value at the previous moment, and then according to the vehicle's slope at the previous moment.
- the error covariance of the estimated value determines the error covariance of the slope prediction value of the vehicle at the current moment, and then according to the error covariance of the slope measurement value of the vehicle at the current moment and the error covariance of the slope prediction value of the vehicle at the current moment, determine The Kalman gain at the current moment, and then based on the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal estimated value of the slope at the previous moment, the slope estimation threshold, and the slope at the previous moment. The difference between the optimal estimated value and the predicted value of the slope at the current moment determines the optimal estimated value of the slope of the vehicle at the current moment.
- Fig. 3 is a schematic structural diagram of a slope estimation device provided in Embodiment 3 of the present application; this embodiment is applicable to the situation where a vehicle performs slope estimation when starting, accelerating or decelerating at a low speed, and the device can be implemented by software and/or hardware Realized, and can be integrated in the electronic equipment carrying the slope estimation function, such as in the vehicle controller.
- the device includes a slope prediction value determination module 310, an error covariance determination module 320, a Kalman gain determination module 330 and a slope estimation value determination module 340, wherein the slope prediction value determination module 310 is configured to The driving acceleration at the current moment, and the optimal estimated value of the slope of the vehicle at the previous moment determine the predicted value of the slope of the vehicle at the current moment; the error covariance determination module 320 is configured to be based on the optimal estimated value of the slope of the vehicle at the previous moment The error covariance of the error covariance of the slope prediction value of the vehicle at the current moment is determined; the Kalman gain determination module 330 is set to the error covariance of the slope measurement value of the vehicle at the current moment and the slope prediction value of the vehicle at the current moment The error covariance of the error to determine the Kalman gain at the current moment; the slope estimation value determination module 340 is set to be based on the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment;
- the vehicle's slope prediction value at the current moment is determined according to the vehicle's driving acceleration at the current moment, and the vehicle's slope optimal estimate value at the previous moment, and then according to the vehicle's slope at the previous moment.
- the error covariance of the estimated value determines the error covariance of the slope prediction value of the vehicle at the current moment, and then according to the error covariance of the slope measurement value of the vehicle at the current moment and the error covariance of the slope prediction value of the vehicle at the current moment, determine The Kalman gain at the current moment, and then based on the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal estimated value of the slope at the previous moment, the slope estimation threshold, and the slope at the previous moment. The difference between the optimal estimated value and the predicted value of the slope at the current moment determines the optimal estimated value of the slope of the vehicle at the current moment.
- the slope estimated value determining module 340 is set to: if the absolute value of the difference between the optimal estimated value of the slope at the last moment and the predicted value of the slope at the current moment is greater than the slope estimation threshold, then according to the slope estimation threshold and the previous moment
- the optimal estimated value of the slope determines the optimal estimated value of the slope at the current moment; if the absolute value of the difference between the optimal estimated value of the slope at the previous moment and the predicted value of the slope at the current moment is smaller than the estimated slope threshold, then according to the current moment
- the Kalman gain, the measured value of the slope at the current moment, and the predicted value of the slope at the current moment determine the optimal estimated value of the slope at the current moment.
- the slope estimation value determination module 340 includes a slope estimation threshold determination unit, which is configured to: determine the required driving time of the vehicle from the prepared uphill state to the initial uphill state; determine the vehicle according to the maximum slope of the road and the driving time. The slope change rate of the slope road where it is located; the slope estimation threshold is determined according to the slope change rate, the slope change proportional coefficient, and the time interval between two adjacent slope estimates.
- the slope change proportional factor is determined according to the degree of slippage of the wheels.
- the slope estimation value determination module 340 also includes a theoretical longitudinal acceleration determination unit, an acceleration error determination unit and a wheel slipping level determination unit, wherein the theoretical longitudinal acceleration determination unit is configured to determine the theoretical longitudinal acceleration according to the gradient resistance and the acceleration resistance; the acceleration error determination The unit is configured to determine the acceleration error according to the theoretical longitudinal acceleration and the actual longitudinal acceleration; the wheel slip level determination unit is configured to determine the wheel slip level according to the acceleration error and the slip judgment threshold.
- the wheel slipping level determination unit is set to: if the absolute value of the acceleration error is less than the first slipping judgment threshold, then determine the slipping level of the wheel as the first slipping level; if the absolute value of the acceleration error is greater than the first slipping judgment threshold and less than the second slipping judgment threshold Slip judgment threshold, or the absolute value of the acceleration error is greater than the third slip judgment threshold, then determine that the slipping level of the wheel is the second slipping level; if the absolute value of the acceleration error is greater than the second slip judgment threshold and less than the third slip judgment threshold, then Determine the slipping level of the wheel as the third slipping level; wherein, the third slipping judgment threshold is greater than the second slipping judgment threshold, and the second slipping judgment threshold is greater than the first slipping judgment threshold.
- the aforementioned slope estimation device can execute the slope estimation method provided by any embodiment of the present application, and has corresponding functional modules and effects for executing the method.
- Fig. 4 is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present application, and Fig. 4 shows a block diagram of an exemplary device suitable for implementing the implementation manner of the embodiment of the present application.
- the device shown in FIG. 4 is only an example, and should not limit the functions and scope of use of this embodiment of the present application.
- electronic device 12 takes the form of a general-purpose computing device.
- Components of electronic device 12 may include, but are not limited to, one or more processors or processing units 16, system memory 28, bus 18 connecting various system components including system memory 28 and processing unit 16.
- Bus 18 represents one or more of a variety of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures.
- bus structures include but are not limited to Industry Standard Architecture (Industry Subversive Alliance, ISA) bus, Micro Channel Architecture (Micro Channel Architecture, MCA) bus, enhanced ISA bus, Video Electronics Standards Association (Video Electronics Standards Association, VESA) local bus and peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.
- Electronic device 12 includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12 and include both volatile and nonvolatile media, removable and non-removable media.
- System memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory (cache 32).
- Electronic device 12 may include other removable/non-removable, volatile/nonvolatile computer system storage media.
- storage system 34 may be configured to read and write to non-removable, non-volatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard drive”).
- the present application may provide a disk drive configured to read and write to a removable non-volatile disk (such as a "floppy disk”), and to read-only a removable non-volatile disk (such as a portable compact disk).
- CD-ROM Compact Disc Read-Only Memory
- DVD-ROM Digital Video Disc
- each drive may be connected to bus 18 via one or more data media interfaces.
- the system memory 28 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of various embodiments of the embodiments of the present application.
- Program/utility 40 may be stored, for example, in system memory 28 as a set (at least one) of program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or a combination of these examples may include implementations of the network environment.
- the program module 42 generally executes the functions and/or methods in the embodiments described in the embodiments of this application.
- the electronic device 12 may also communicate with one or more external devices 14 (e.g., a keyboard, pointing device, display 24, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 12, and/or communicate with Any device (eg, network card, modem, etc.) that enables the electronic device 12 to communicate with one or more other computing devices. This communication can be performed through an input/output (Input/Output, I/O) interface 22 .
- the electronic device 12 can also communicate with one or more networks (such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN) and/or a public network, such as the Internet) through the network adapter 20.
- networks such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN) and/or a public network, such as the Internet
- network adapter 20 communicates with other modules of electronic device 12 via bus 18 .
- electronic device 12 may be used in conjunction with other hardware and/or software modules, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, Disk array (Redundant Arrays of Independent Disks, RAID) system, tape drive and data backup storage system, etc.
- the processing unit 16 executes a variety of functional applications and data processing by running the programs stored in the system memory 28 , such as implementing the slope estimation method provided by the embodiment of the present application.
- Embodiment 5 of the present application also provides a computer-readable storage medium, on which a computer program (or called computer-executable instructions) is stored, and when the program is executed by a processor, it is used to perform the slope estimation provided in the embodiment of the present application
- the method includes: determining the predicted value of the slope of the vehicle at the current moment according to the driving acceleration of the vehicle at the current moment and the optimal estimated value of the slope of the vehicle at the previous moment; Error covariance, determine the error covariance of the slope prediction value of the vehicle at the current moment; according to the error covariance of the slope measurement value of the vehicle at the current moment and the error covariance of the slope prediction value of the vehicle at the current moment, determine the current moment.
- the computer storage medium in the embodiments of the present application may use any combination of one or more computer-readable media.
- the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
- the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared semiconductor system, device, or device, or any combination thereof.
- Computer-readable storage media can include (a non-exhaustive list) electrical connections with one or more conductors, portable computer disks, hard disks, RAM, Read-Only Memory (ROM), erasable programmable Read-only memory (Erasable Programmable Read-Only Memory, EPROM) or flash memory, optical fiber, CD-ROM, optical storage device, magnetic storage device, or any combination of the above.
- a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
- a computer-readable signal medium may include a data signal in baseband or propagated as a carrier wave, and the computer-readable signal medium carries computer-readable program code thereon. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any combination of the foregoing.
- a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can be sent, propagated, or transported for use by or in conjunction with an instruction execution system, apparatus, or device. Programs used in conjunction with the device.
- the program code contained on the computer readable medium can be transmitted by any medium, including but not limited to wireless, electric wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any combination of the above.
- Computer program codes for performing the operations of the embodiments of the present application may be written in one or more programming languages or a combination thereof, the programming languages including object-oriented programming languages-such as Java, Smalltalk, C++, including A conventional procedural programming language such as the "C" language or similar programming language.
- the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
- the remote computer can be connected to the user computer via any kind of network, including a LAN or WAN, or alternatively, can be connected to an external computer (eg via the Internet using an Internet service provider).
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Abstract
本申请公开了一种坡度估计方法、装置、电子设备以及存储介质。该方法根据车辆在当前时刻的行驶加速度，以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值；确定车辆在当前时刻的坡度预测值的误差协方差；根据车辆在当前时刻的坡度测量值的误差协方差和所述车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益；根据所述当前时刻的卡尔曼增益、当前时刻的坡度测量值、所述当前时刻的坡度预测值、所述上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值。
Description
本申请要求在2021年09月10日提交中国专利局、申请号为202111060088.2的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
本申请实施例涉及计算机技术领域，例如涉及一种坡度估计方法、装置、电子设备以及存储介质。
道路坡度估计算法根据所使用的车辆模型不同可以分为基于运动学模型的方法和基于动力学模型的方法。基于动力学模型的方法，将制动轮缸压力和动力源输出力矩做为估计器的输入，这种方法十分依赖车辆模型，车辆模型中的多个参数受高频噪声影响大，而且受制动换挡等操作影响较大，估值不稳定；基于运动学模型的方法所采用的传感器数量较少，以纵向加速度传感器信号为基础，纵向加速度传感器测得的车体坐标系中的信号，会受到车体位姿的影响，尤其车辆在低速起步、加速、减速时，车体位姿不稳定，导致纵向加速度传感器测得的车辆的纵向加速度与实际车辆的纵向加速度存在一定偏差，对坡度估计精度带来一定影响。
发明内容
本申请提供一种坡度估计方法、装置、电子设备以及存储介质，以解决车辆在低速起步、加速、减速时，由于车辆自身不稳定导致的加速度传感器数值波动严重，最终造成坡度估计值不可靠的问题。
本申请实施例提供了一种坡度估计方法，包括：
根据车辆在当前时刻的行驶加速度，以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值；
根据车辆在上一时刻的坡度最优估计值的误差协方差，确定车辆在当前时刻的坡度预测值的误差协方差；
根据车辆在当前时刻的坡度测量值的误差协方差和所述车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益；
根据所述当前时刻的卡尔曼增益、当前时刻的坡度测量值、所述当前时刻的坡度预测值、所述上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值，确定车辆在当前时 刻的坡度最优估计值。
本申请实施例还提供了一种坡度估计装置，包括：
坡度预测值确定模块，设置为根据车辆在当前时刻的行驶加速度，以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值；
误差协方差确定模块，设置为根据车辆在上一时刻的坡度最优估计值的误差协方差，确定车辆在当前时刻的坡度预测值的误差协方差；
卡尔曼增益确定模块，设置为根据车辆在当前时刻的坡度测量值的误差协方差和所述车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益；
坡度估计值确定模块，设置为根据所述当前时刻的卡尔曼增益、当前时刻的坡度测量值、所述当前时刻的坡度预测值、所述上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值。
本申请实施例还提供了一种电子设备，包括：
一个或多个处理器；
存储器，设置为存储一个或多个程序；
当所述一个或多个程序被所述一个或多个处理器执行时，所述一个或多个处理器实现如本申请任一实施例所提供的坡度估计方法。
本申请实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如本申请任一实施例所提供的坡度估计方法。
图1是本申请实施例一提供的一种坡度估计方法的流程图；
图2A是本申请实施例二提供的一种坡度估计方法的流程图；
图2B是本申请实施例二提供的一种车辆上坡行驶示意图；
图3是本申请实施例三提供的一种坡度估计装置的结构示意图；
图4是本申请实施例四提供的一种电子设备的结构示意图。
下面结合附图和实施例对本申请作说明。可以理解的是，此处所描述的具体实施例仅仅用于解释本申请，而非对本申请的限定。另外还需要说明的是， 附图中仅示出了与本申请相关的部分而非全部结构。
在介绍本申请实施例之前，先对本申请的思路进行如下说明：
首先基于车辆的行驶加速度、车辆的纵向加速度传感器的测量结果、道路坡度、重力加速度等，构建车辆的纵向加速度传感器的测量结果与道路坡度的车辆运动模型。示例性的，可以通过如下公式确定车辆运动模型：
其中，a
x表示车辆的纵向加速度传感器的测量结果，
表示车辆的行驶加速度，g表示重力加速度，i表示道路坡度。
然后，将车辆运动模型转换为线性系统状态空间模型。示例性的，由于道路坡度相对车辆自身动力学状态变化缓慢，对时间导数可近似为零，可得出微分方程组：
将上式进行离散化，得到k时刻的坡度估计系统的状态空间方程，如下：
其中，
表示坡度估计系统的状态值，v表示车速，i表示道路坡度，
表示坡度估计系统的输入矩阵，
表示为观测矩阵，
表示为状态转移矩阵，z
k表示坡度估计系统的观测值，w
k为过程噪声，表示坡度估计系统的状态空间方程与实际测量过程之间的偏差，v
k为测量噪声，表示纵向加速度传感器的精度误差，u
k表示当前时刻的纵向加速度传感器的测量结果。
进而基于改进的卡尔曼滤波算法进行坡度估计，主要对卡尔曼滤波算法中的状态更新环节进行改进，具体阐述详见下述实施例。
实施例一
图1是本申请实施例一提供的一种坡度估计方法的流程图，本实施例可适用于车辆在低速起步、加速或减速时进行坡度估计的情况，该方法可以由坡度估计装置来执行，该装置可由软件和/或硬件的方式实现，并可集成于承载坡度估计功能的电子设备中，例如车辆控制器中。
如图1所示，该方法可以包括：
S110、根据车辆在当前时刻的行驶加速度，以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值。
本实施例中，结合上述坡度估计系统的状态空间方程，可以将车辆在当前时刻的行驶加速度、车辆在上一时刻的坡度最优估计值输入至坡度预测模型中，得到车辆在当前时刻的坡度预测值。例如，可以通过如下公式确定车辆在当前时刻的坡度预测值：
其中，
表示当前时刻的坡度预测值，
表示为上一时刻的坡度最优估计值，A表示为状态转移矩阵，B表示为坡度估计系统的输入矩阵，uk表示当前时刻的纵向加速度传感器的测量结果，包含车辆在当前时刻的行驶加速度。
S120、根据车辆在上一时刻的坡度最优估计值的误差协方差，确定车辆在当前时刻的坡度预测值的误差协方差。
本实施例中，将车辆在上一时刻的坡度最优估计值的误差协方差输入至误差确定模型中，得到车辆在当前时刻的坡度预测值的误差协方差。例如，可以通过如下公式确定车辆在当前时刻的坡度预测值的误差协方差：
其中，
表示为当前时刻的坡度预测值的误差协方差，P
k-1表示为上一时刻的坡度最优估计值的误差协方差，Q表示为过程噪声协方差，Q可由本领域技术人员根据实际情况通过对比实验获得。
S130、根据车辆在当前时刻的坡度测量值的误差协方差和车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益。
本实施例中，将车辆在当前时刻的坡度测量值的误差协方差和车辆在当前时刻的坡度预测值的误差协方差输入至卡尔曼增益确定模型中，输出当前时刻的卡尔曼增益。例如，可以通过如下公式，确定当前时刻的卡尔曼增益：
其中，K
k表示为当前时刻的卡尔曼增益，
表示为当前时刻的坡度预测值的误差协方差，R是对纵向加速度传感器的测量数据经过长期的概率统计得出的测量方差，H表示车辆在当前时刻的坡度测量值的误差协方差。
S140、根据当前时刻的卡尔曼增益、当前时刻的坡度测量值、当前时刻的坡度预测值、上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值。
可选的，若上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值的绝对值大于坡度估计阈值，则根据坡度估计阈值和上一时刻的坡度最优估计值确定当前时刻的坡度最优估计值。若上一时刻的坡度最优估计值小于当前时刻的坡度预测值，且上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值的绝对值大于坡度估计阈值，则将上一时刻的坡度最优估计值与坡度估计阈值作和，将作和的结果作为车辆在当前时刻的坡度最优估计值。若上一时刻的坡度最优估计值大于当前时刻的坡度预测值，且上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值的绝对值大于坡度估计阈值，则将上一时刻的坡度最优估计值与坡度估计阈值作差，将作差的结果作为车辆在当前时刻的坡度最优估计值。
可选的，若上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值的绝对值小于坡度估计阈值，则根据当前时刻的卡尔曼增益、当前时刻的坡度测量值、以及当前时刻的坡度预测值，确定当前时刻的坡度最优估计值。
例如，可以通过如下公式确定当前时刻的坡度最优估计值：
其中，
表示为当前时刻的坡度最优估计值，
表示为上一时刻的坡度最优估计值，Δi
max表示为坡度估计阈值，
表示为当前时刻的坡度测量值、K
k表示为当前时刻的卡尔曼增益、H表示为观测矩阵、
表示为上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值、z
k表示为当前时刻的坡度预测值。
确定车辆在当前时刻的坡度最优估计值之后，根据当前时刻的卡尔曼增益，当前时刻的坡度预测值的误差协方差，确定当前时刻的坡度最优估计值的误差协方差，以便确定下一时刻的坡度最优估计值的误差协方差，即更新坡度最优估计值的误差协方差。示例性的，可以通过如下公式确定当前时刻的坡度最优估计值的误差协方差：
其中，P
k表示为当前时刻的坡度最优估计值的误差协方差，I为单位矩阵，H表示为观测矩阵，K
k表示为当前时刻的卡尔曼增益，
表示为当前时刻的坡度预测值的误差协方差，R是对纵向加速度传感器的测量数据经过长期的概率统计得出的测量方差。
本申请实施例的技术方案，根据车辆在当前时刻的行驶加速度，以及车辆 在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值，之后根据车辆在上一时刻的坡度最优估计值的误差协方差，确定车辆在当前时刻的坡度预测值的误差协方差，接着根据车辆在当前时刻的坡度测量值的误差协方差和车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益，进而根据当前时刻的卡尔曼增益、当前时刻的坡度测量值、当前时刻的坡度预测值、上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值。上述技术方案解决了车辆在低速起步、加速、减速时，由于车辆自身不稳定导致的加速度传感器数值波动严重，最终造成坡度估计值不可靠的问题，在不增加原有坡度估计算法复杂度以及传感器需求的情况下，提高了坡度估计算法在车身不稳定情况下的鲁棒性，同时为坡度估计提供了一种新思路。
实施例二
图2A是本申请实施例二提供的一种坡度估计方法的流程图；图2B是本申请实施例二提供的一种车辆上坡行驶示意图。在上述实施例的基础上，提供一种可选实施方案。
如图2A所示，该方法可以包括：
S210、根据车辆在当前时刻的行驶加速度，以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值。
S220、根据车辆在上一时刻的坡度最优估计值的误差协方差，确定车辆在当前时刻的坡度预测值的误差协方差。
S230、根据车辆在当前时刻的坡度测量值的误差协方差和车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益。
S240、根据当前时刻的卡尔曼增益、当前时刻的坡度测量值、当前时刻的坡度预测值、上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值。
可选的，坡度估计阈值通过如下方式确定：确定车辆从预备上坡状态到初始上坡状态所需的行驶时长；根据道路最大坡度和行驶时长，确定车辆所处斜坡道路的坡度变化率；根据坡度变化率、坡度变化比例系数、以及相邻两次坡度估计的时间间隔，确定坡度估计阈值。
预备上坡状态是指车辆的前车轮到达坡底，正准备上坡的状态，如图2B中虚线车辆所处的状态；所谓初始上坡状态是指车辆的后车轮刚离开坡底，车辆 完全处于坡道斜面时的状态，如图2B中实线车辆所处的状态。
将车辆的轴距的两倍，作为车辆从预备上坡状态到初始上坡状态的行驶路径，如图2B，其中L表示车辆的轴距，v表示车速。进而根据行驶路径和车速，确定车辆从预备上坡状态到初始上坡状态所需的行驶时长，例如可以通过如下公式确定行驶时长：
其中，t
slope表示车辆从预备上坡状态到初始上坡状态所需的行驶时长，L表示车辆的轴距，v表示车速。
在确定车辆从预备上坡状态到初始上坡状态所需的行驶时长后，根据道路最大坡度和行驶时长，确定车辆所处斜坡道路的坡度变化率。例如将道路最大坡度与行驶时长进行作商，将作商后的结果作为车辆所处斜坡道路的坡度变化率。如图2B中所示，θ
max表示道路最大坡度。
在确定坡度变化率后，根据坡度变化率、坡度变化比例系数、以及相邻两次坡度估计的时间间隔，确定坡度估计阈值，可以是将坡度变化率、坡度变化比例系数、以及相邻两次坡度估计的时间间隔相乘的结果，作为坡度估计阈值。
可选的，坡度变化比例系数可以由本领域技术人员根据实际情况设定。由于车辆在低速起步、加速或减速时，会出现车轮打滑的情况，进而导致车身俯仰不稳定的同时，造成基于轮速估计的车辆的纵向车速不稳定，最终导致坡度估计结果波动。为了保证坡度估计的稳定性和准确性，作为本申请实施例的一种可选方式，坡度变化比例系数根据车轮的打滑等级确定，不同的坡度变化比例系数对应不同的车轮的打滑等级。
示例性的，可以通过如下方式确定车轮的打滑等级：根据坡度阻力和加速阻力，确定理论纵向加速度；根据理论纵向加速度和实际纵向加速度，确定加速误差；根据加速误差和打滑判断阈值，确定车轮的打滑等级。其中，打滑判断阈值可以由本领域技术人员根据实际情况设定。
所谓坡度阻力是指当车轮上坡行驶时，车辆的重力沿坡道斜面的分力，该分力表现为对车轮行驶的一种阻力。坡度阻力可以通过重力加速度、滚动阻力系数和车辆的重量确定，可选的，可以将重力加速度、滚动阻力系数和车辆的重量相乘的结果作为坡道阻力。例如可以通过如下公式确定坡道阻力：F
f＝mgf，其中，F
f表示坡度阻力，m表示车辆的重量，g表示重力加速度、f表示滚动阻力系数。
所谓加速阻力是指车辆行驶时保持匀速运动的惯性力，可以通过车辆的行 驶加速度和车辆的重量确定，可选的，可以将车辆的行驶加速度和车辆的重量相乘的结果作为加速阻力。例如可以通过如下公式确定加速阻力：
其中，F
j表示加速阻力，m表示车辆的重量，
表示车辆的行驶加速度。
所谓理论纵向加速度是指车辆在坡道斜面方向上时理论计算得到的加速度。
所谓实际纵向加速度是指车辆中纵向加速度传感器测得的车辆的纵向加速度。
可选的，将坡度阻力和加速阻力的和，与车辆的重量作商，将作商的结果，作为理论纵向加速度，之后，将理论纵向加速度和实际纵向加速度之间的差值作为加速误差，根据加速误差和打滑判断阈值，确定车轮的打滑等级。
示例性的，根据加速误差和打滑判断阈值，确定车轮的打滑等级，可以是，若加速误差的绝对值小于第一打滑判断阈值，则确定车轮的打滑等级为第一打滑等级；若加速误差的绝对值大于第一打滑判断阈值且小于第二打滑判断阈值，或加速误差的绝对值大于第三打滑判断阈值，则确定车轮的打滑等级为第二打滑等级；若加速误差的绝对值大于第二打滑判断阈值且小于第三打滑判断阈值，则确定车轮的打滑等级为第三打滑等级；其中，第三打滑判断阈值大于第二打滑判断阈值，第二打滑判断阈值大于第一打滑判断阈值。第三打滑判断阈值、第二打滑判断阈值和第一打滑判断阈值可由本领域技术人员根据实际情况设定。
可以理解是的，通过引入车轮的打滑等级，可以及时准确的辨别出车轮的打滑情况，以便于准确确定坡度变化比例系数，进而提高坡度估计阈值的准确性，从而提高坡度估计的准确性。
本申请实施例的技术方案，根据车辆在当前时刻的行驶加速度，以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值，之后根据车辆在上一时刻的坡度最优估计值的误差协方差，确定车辆在当前时刻的坡度预测值的误差协方差，接着根据车辆在当前时刻的坡度测量值的误差协方差和车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益，进而根据当前时刻的卡尔曼增益、当前时刻的坡度测量值、当前时刻的坡度预测值、上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值。上述技术方案解决了车辆在低速起步、加速、减速时，由于车辆自身不稳定导致的加速度传感器数值波动严重，最终造成坡度估计值不可靠的问题，在不增加原有坡度估计算法复杂度以及传感器需求的情况下，提高了坡度估计算法在车身不稳定情况下的鲁棒性，同时为坡度估计提供了一种新思路。
实施例三
图3是本申请实施例三提供的一种坡度估计装置的结构示意图；本实施例可适用于车辆在低速起步、加速或减速时进行坡度估计的情况，该装置可由软件和/或硬件的方式实现，并可集成于承载坡度估计功能的电子设备中，例如车辆控制器中。
如图3所示，该装置包括坡度预测值确定模块310、误差协方差确定模块320、卡尔曼增益确定模块330和坡度估计值确定模块340，其中，坡度预测值确定模块310，设置为根据车辆在当前时刻的行驶加速度，以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值；误差协方差确定模块320，设置为根据车辆在上一时刻的坡度最优估计值的误差协方差，确定车辆在当前时刻的坡度预测值的误差协方差；卡尔曼增益确定模块330，设置为根据车辆在当前时刻的坡度测量值的误差协方差和车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益；坡度估计值确定模块340，设置为根据当前时刻的卡尔曼增益、当前时刻的坡度测量值、当前时刻的坡度预测值、上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值。
本申请实施例的技术方案，根据车辆在当前时刻的行驶加速度，以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值，之后根据车辆在上一时刻的坡度最优估计值的误差协方差，确定车辆在当前时刻的坡度预测值的误差协方差，接着根据车辆在当前时刻的坡度测量值的误差协方差和车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益，进而根据当前时刻的卡尔曼增益、当前时刻的坡度测量值、当前时刻的坡度预测值、上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值。上述技术方案解决了车辆在低速起步、加速、减速时，由于车辆自身不稳定导致的加速度传感器数值波动严重，最终造成坡度估计值不可靠的问题，在不增加原有坡度估计算法复杂度以及传感器需求的情况下，提高了坡度估计算法在车身不稳定情况下的鲁棒性，同时为坡度估计提供了一种新思路。
坡度估计值确定模块340是设置为：若上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值的绝对值大于坡度估计阈值，则根据坡度估计阈值和上一时刻的坡度最优估计值确定当前时刻的坡度最优估计值；若上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值的绝对值小于坡度估计阈值，则根据当前时刻的卡尔曼增益、当前时刻的坡度测量值、以及当前时刻的坡度预测值，确定当前时刻的坡度最优估计值。
坡度估计值确定模块340包括坡度估计阈值确定单元，该坡度估计阈值确定单元设置为：确定车辆从预备上坡状态到初始上坡状态所需的行驶时长；根据道路最大坡度和行驶时长，确定车辆所处斜坡道路的坡度变化率；根据坡度变化率、坡度变化比例系数、以及相邻两次坡度估计的时间间隔，确定坡度估计阈值。
坡度变化比例系数根据车轮的打滑等级确定。
坡度估计值确定模块340还包括理论纵向加速度确定单元、加速误差确定单元和车轮打滑等级确定单元，其中，理论纵向加速度确定单元，设置为根据坡度阻力和加速阻力，确定理论纵向加速度；加速误差确定单元，设置为根据理论纵向加速度和实际纵向加速度，确定加速误差；车轮打滑等级确定单元，设置为根据加速误差和打滑判断阈值，确定车轮的打滑等级。
车轮打滑等级确定单元是设置为：若加速误差的绝对值小于第一打滑判断阈值，则确定车轮的打滑等级为第一打滑等级；若加速误差的绝对值大于第一打滑判断阈值且小于第二打滑判断阈值，或加速误差的绝对值大于第三打滑判断阈值，则确定车轮的打滑等级为第二打滑等级；若加速误差的绝对值大于第二打滑判断阈值且小于第三打滑判断阈值，则确定车轮的打滑等级为第三打滑等级；其中，第三打滑判断阈值大于第二打滑判断阈值，第二打滑判断阈值大于第一打滑判断阈值。
上述坡度估计装置可执行本申请任意实施例所提供的坡度估计方法，具备执行方法相应的功能模块和效果。
实施例四
图4是本申请实施例四提供的一种电子设备的结构示意图，图4示出了适于用来实现本申请实施例实施方式的示例性设备的框图。图4显示的设备仅仅是一个示例，不应对本申请实施例的功能和使用范围带来任何限制。
如图4所示，电子设备12以通用计算设备的形式表现。电子设备12的组件可以包括但不限于：一个或者多个处理器或者处理单元16，系统存储器28，连接不同系统组件(包括系统存储器28和处理单元16)的总线18。
总线18表示多类总线结构中的一种或多种，包括存储器总线或者存储器控制器，外围总线，图形加速端口，处理器或者使用多种总线结构中的任意总线结构的局域总线。举例来说，这些总线结构包括但不限于工业标准体系结构(Industry Subversive Alliance，ISA)总线，微通道体系结构(Micro Channel Architecture，MCA)总线，增强型ISA总线、视频电子标准协会(Video Electronics Standards Association，VESA)局域总线以及外围组件互连(Peripheral Component Interconnect，PCI)总线。
电子设备12包括多种计算机系统可读介质。这些介质可以是任何能够被电子设备12访问的可用介质，包括易失性和非易失性介质，可移动的和不可移动的介质。
系统存储器28可以包括易失性存储器形式的计算机系统可读介质，例如随机存取存储器(Random Access Memory，RAM)30和/或高速缓存存储器(高速缓存32)。电子设备12可以包括其它可移动/不可移动的、易失性/非易失性计算机系统存储介质。仅作为举例，存储系统34可以设置为读写不可移动的、非易失性磁介质(图4未显示，通常称为“硬盘驱动器”)。尽管图4中未示出，本申请可以提供设置为对可移动非易失性磁盘(例如“软盘”)读写的磁盘驱动器，以及对可移动非易失性光盘(例如便携式紧凑磁盘只读存储器(Compact Disc Read-Only Memory，CD-ROM)，数字视盘(Digital Video Disc-Read Only Memory，DVD-ROM)或者其它光介质)读写的光盘驱动器。在这些情况下，每个驱动器可以通过一个或者多个数据介质接口与总线18相连。系统存储器28可以包括至少一个程序产品，该程序产品具有一组(例如至少一个)程序模块，这些程序模块被配置以执行本申请实施例多个实施例的功能。
具有一组(至少一个)程序模块42的程序/实用工具40，可以存储在例如系统存储器28中，这样的程序模块42包括但不限于操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或一种组合中可能包括网络环境的实现。程序模块42通常执行本申请实施例所描述的实施例中的功能和/或方法。
电子设备12也可以与一个或多个外部设备14(例如键盘、指向设备、显示器24等)通信，还可与一个或者多个使得用户能与该电子设备12交互的设备通信，和/或与使得该电子设备12能与一个或多个其它计算设备进行通信的任何设备(例如网卡，调制解调器等等)通信。这种通信可以通过输入/输出(Input/Output，I/O)接口22进行。并且，电子设备12还可以通过网络适配器20与一个或者多个网络(例如局域网(Local Area Network，LAN)，广域网(Wide Area Network，WAN)和/或公共网络，例如因特网)通信。如图所示，网络适配器20通过总线18与电子设备12的其它模块通信。尽管图中未示出，可以将电子设备12与其它硬件和/或软件模块结合使用，硬件和/或软件模块包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、磁盘阵列(Redundant Arrays of Independent Disks，RAID)系统、磁带驱动器以及数据备份存储系统等。
处理单元16通过运行存储在系统存储器28中的程序，执行多种功能应用以及数据处理，例如实现本申请实施例所提供的坡度估计方法。
实施例五
本申请实施例五还提供一种计算机可读存储介质，其上存储有计算机程序(或称为计算机可执行指令)，该程序被处理器执行时用于执行本申请实施例所提供的坡度估计方法，该方法包括：根据车辆在当前时刻的行驶加速度，以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值；根据车辆在上一时刻的坡度最优估计值的误差协方差，确定车辆在当前时刻的坡度预测值的误差协方差；根据车辆在当前时刻的坡度测量值的误差协方差和车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益；根据当前时刻的卡尔曼增益、当前时刻的坡度测量值、当前时刻的坡度预测值、上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值。
本申请实施例的计算机存储介质，可以采用一个或多个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是但不限于电、磁、光、电磁、红外线半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质可以(非穷举的列表)包括：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、RAM、只读存储器(Read-Only Memory，ROM)、可擦式可编程只读存储器(Erasable Programmable Read-Only Memory，EPROM)或闪存、光纤、CD-ROM、光存储器件、磁存储器件、或者上述的任意组合。在本文件中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与指令执行系统、装置或者器件结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波传播的数据信号，计算机可读的信号介质中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输由指令执行系统、装置或者器件使用或者与指令执行系统、装置或者器件结合使用的程序。
计算机可读介质上包含的程序代码可以用任何介质传输，介质包括但不限于无线、电线、光缆、射频(Radio Frequency，RF)等等，或者上述的任意组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本申请实施例操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言-诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、一部分在用户计算机上执行一部分在远程计算机上执行或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络包括LAN或WAN连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
Claims (10)
- 一种坡度估计方法，包括：根据车辆在当前时刻的行驶加速度，以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值；根据车辆在上一时刻的坡度最优估计值的误差协方差，确定车辆在当前时刻的坡度预测值的误差协方差；根据车辆在当前时刻的坡度测量值的误差协方差和所述车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益；根据所述当前时刻的卡尔曼增益、当前时刻的坡度测量值、所述当前时刻的坡度预测值、所述上一时刻的坡度最优估计值、坡度估计阈值、以及所述上一时刻的坡度最优估计值与所述当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值。
- 根据权利要求1所述的方法，其中，所述根据所述当前时刻的卡尔曼增益、当前时刻的坡度测量值、所述当前时刻的坡度预测值、所述上一时刻的坡度最优估计值、坡度估计阈值、以及所述上一时刻的坡度最优估计值与所述当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值，包括：在所述上一时刻的坡度最优估计值与所述当前时刻的坡度预测值之间的差值的绝对值大于所述坡度估计阈值的情况下，根据坡度估计阈值和所述上一时刻的坡度最优估计值确定当前时刻的坡度最优估计值；在所述上一时刻的坡度最优估计值与所述当前时刻的坡度预测值之间的差值的绝对值小于所述坡度估计阈值的情况下，根据所述当前时刻的卡尔曼增益、当前时刻的坡度测量值、以及所述当前时刻的坡度预测值，确定当前时刻的坡度最优估计值。
- 根据权利要求2所述的方法，其中，所述坡度估计阈值通过如下方式确定：确定车辆从预备上坡状态到初始上坡状态所需的行驶时长；根据道路最大坡度和所述行驶时长，确定车辆所处坡度的坡度变化率；根据所述坡度变化率、坡度变化比例系数、以及相邻两次坡度估计的时间间隔，确定所述坡度估计阈值。
- 根据权利要求3所述的方法，其中，所述坡度变化比例系数根据车轮的打滑等级确定。
- 根据权利要求4所述的方法，其中，所述车轮的打滑等级通过如下方式确定：根据坡度阻力和加速阻力，确定理论纵向加速度；根据所述理论纵向加速度和实际纵向加速度，确定加速误差；根据所述加速误差和打滑判断阈值，确定车轮的打滑等级。
- 根据权利要求5所述的方法，其中，所述根据所述加速误差和打滑判断阈值，确定车轮的打滑等级，包括：在所述加速误差的绝对值小于第一打滑判断阈值的情况下，确定车轮的打滑等级为第一打滑等级；在所述加速误差的绝对值大于第一打滑判断阈值且小于第二打滑判断阈值的情况下，或在所述加速误差的绝对值大于第三打滑判断阈值的情况下，确定车轮的打滑等级为第二打滑等级；在所述加速误差的绝对值大于第二打滑判断阈值且小于第三打滑判断阈值的情况下，确定车轮的打滑等级为第三打滑等级；其中，所述第三打滑判断阈值大于所述第二打滑判断阈值，所述第二打滑判断阈值大于所述第一打滑判断 阈值。
- 一种坡度估计装置，包括：坡度预测值确定模块，设置为根据车辆在当前时刻的行驶加速度，以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值；误差协方差确定模块，设置为根据车辆在上一时刻的坡度最优估计值的误差协方差，确定车辆在当前时刻的坡度预测值的误差协方差；卡尔曼增益确定模块，设置为根据车辆在当前时刻的坡度测量值的误差协方差和所述车辆在当前时刻的坡度预测值的误差协方差，确定当前时刻的卡尔曼增益；坡度估计值确定模块，设置为根据所述当前时刻的卡尔曼增益、当前时刻的坡度测量值、所述当前时刻的坡度预测值、所述上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值，确定车辆在当前时刻的坡度最优估计值。
- 根据权利要求7所述的装置，其中，所述坡度估计值确定模块是设置为：在所述上一时刻的坡度最优估计值与所述当前时刻的坡度预测值之间的差值的绝对值大于所述坡度估计阈值的情况下，根据坡度估计阈值和所述上一时刻的坡度最优估计值确定当前时刻的坡度最优估计值；在所述上一时刻的坡度最优估计值与所述当前时刻的坡度预测值之间的差值的绝对值小于所述坡度估计阈值的情况下，根据所述当前时刻的卡尔曼增益、当前时刻的坡度测量值、以及所述当前时刻的坡度预测值，确定当前时刻的坡度最优估计值。
- 一种电子设备，包括：至少一个处理器；存储器，设置为存储至少一个程序；当所述至少一个程序被所述至少一个处理器执行时，所述至少一个处理器实现如权利要求1-6中任一项所述的坡度估计方法。
- 一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如权利要求1-6中任一项所述的坡度估计方法。
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