CN116353605A - Road gradient calculation method and device, electronic equipment and storage medium - Google Patents
Road gradient calculation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a road gradient calculating method, a device, electronic equipment and a storage medium, wherein the road gradient calculating method comprises the following steps: monitoring the operation change working conditions of the vehicle, wherein the operation change working conditions comprise a sudden acceleration working condition and a sudden deceleration working condition; determining forgetting factor coefficients of the operation change working conditions according to the operation change working conditions; acquiring covariance and Kalman gain at the last moment; and determining the road gradient according to the covariance, the Kalman gain and the forgetting factor coefficient at the last moment. Therefore, under the condition of running change working conditions, noise of road gradient calculation caused by vehicle posture change is reduced, a Kalman filtering algorithm can be effectively adjusted, and further, more accurate road gradient can be obtained by correcting the road gradient, and stability and safety of the vehicle in the running process are improved.
Description
Technical Field
The present disclosure relates to the technical field of vehicle gradient calculation, and in particular, to a road gradient calculation method, a device, an electronic apparatus, and a storage medium.
Background
During the running of the vehicle, the road gradient of the vehicle is generally calculated by a standard kalman filtering algorithm, so that the road gradient of the vehicle can be determined. However, the vehicle body posture changes due to the change of the vehicle condition during the running of the vehicle, and the road gradient calculation result is noisy.
Disclosure of Invention
The embodiment of the application provides a road gradient calculating method, a road gradient calculating device, electronic equipment and a storage medium, so as to solve the problems in the related art, and the technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a road gradient calculation method, including:
monitoring the operation change working conditions of the vehicle, wherein the operation change working conditions comprise a sudden acceleration working condition and a sudden deceleration working condition;
determining forgetting factor coefficients of the operation change working conditions according to the operation change working conditions;
acquiring covariance and Kalman gain at the last moment;
and determining the road gradient according to the covariance, the Kalman gain and the forgetting factor coefficient at the last moment.
In a second aspect, embodiments of the present application provide a road gradient calculating device, including:
the first monitoring module is used for monitoring the operation change working conditions of the vehicle, wherein the operation change working conditions comprise a sudden acceleration working condition and a sudden deceleration working condition;
the first determining module is used for determining forgetting factor coefficients of the operation change working conditions according to the operation change working conditions;
the first acquisition module is used for acquiring covariance and Kalman gain at the last moment;
and the second determining module is used for determining the road gradient according to the covariance, the Kalman gain and the forgetting factor coefficient at the last moment.
In a third aspect, an embodiment of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the road slope calculation method described above.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing computer instructions that, when executed on a computer, perform a method according to any one of the above-described embodiments.
The advantages or beneficial effects in the technical scheme at least comprise:
the operation change working condition of the vehicle is monitored, namely under the condition that the operation change working condition of the vehicle is determined, at the moment, the noise of the road gradient calculated through the Kalman filtering algorithm is larger because the pose of the vehicle is changed, in the embodiment, the Kalman filtering algorithm is adjusted through the operation change working condition determination forgetting factor coefficient, and then the road gradient calculation is carried out through the adjusted Kalman filtering algorithm, so that the noise of the road gradient calculation caused by the change of the pose of the vehicle under the condition that the operation change working condition is generated can be reduced, the Kalman filtering algorithm can be effectively adjusted, further, the more accurate road gradient is obtained through correcting the road gradient, and the stability and the safety of the vehicle in the running process are improved.
The foregoing summary is for the purpose of the specification only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present application will become apparent by reference to the drawings and the following detailed description.
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In the drawings, the same reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily drawn to scale. It is appreciated that these drawings depict only some embodiments according to the disclosure and are not therefore to be considered limiting of its scope.
FIG. 1 illustrates a flow chart of a road grade calculation method according to an embodiment of the present application;
FIG. 2 illustrates a flow chart of a road grade calculation method according to another embodiment of the present application;
FIG. 3 illustrates a flow chart of a road grade calculation method according to another embodiment of the present application;
FIG. 4 illustrates a block diagram of a road grade calculation device according to an embodiment of the present application;
fig. 5 shows a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Hereinafter, only certain exemplary embodiments are briefly described. As will be recognized by those of skill in the pertinent art, the described embodiments may be modified in various different ways without departing from the spirit or scope of the present application. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.
Related description:
kalman filtering (Kalman filtering) is an algorithm that utilizes a linear system state equation to optimally estimate the system state by inputting and outputting observed data through the system. The optimal estimate can also be seen as a filtering process, since the observed data includes the effects of noise and interference in the system.
Data filtering is a data processing technique that removes noise to recover real data, and Kalman filtering enables estimation of the state of a dynamic system from a series of data where measurement noise is present, with measurement variances known. Because it is easy to realize computer programming, and can update and process the data collected in site in real time.
In the related art, the road gradient of the vehicle is calculated through a standard Kalman filtering algorithm, so that the road gradient of the vehicle can be determined.
Fig. 1 shows a flowchart of a road gradient calculation method according to an embodiment of the present application. As shown in fig. 1, the road gradient calculation method may include:
s110: the operation change working condition of the vehicle is monitored, wherein the operation change working condition comprises a sudden acceleration working condition and a sudden deceleration working condition.
S120: and determining a forgetting factor coefficient of the operation change working condition according to the operation change working condition.
S130: and acquiring covariance and Kalman gain of the last moment.
S140: and determining the road gradient according to the covariance, the Kalman gain and the forgetting factor coefficient at the last moment.
The road gradient calculating method in this embodiment may be executed by an Electronic Control Unit (ECU) of the vehicle, or may be executed by other domain controllers with computing capabilities on the vehicle, or may be executed by a domain controller or an electronic control unit that combines a server side with the vehicle, monitors an operation change condition of the vehicle by the domain controller or the electronic control unit of the vehicle, executes a forgetting factor coefficient that determines the operation change condition according to the operation change condition by the server side, adjusts a kalman filter algorithm according to the forgetting factor coefficient, determines a road gradient according to the adjusted kalman filter algorithm, or may be another execution subject that can implement the road gradient calculating method.
In step S110, an operation change condition of the vehicle is monitored, where the operation change condition includes a rapid acceleration condition and a rapid deceleration condition.
The operation change working condition of the vehicle can be monitored through the acquisition device of the vehicle or directly from the electronic control unit of the vehicle, the operation change working condition of the vehicle comprises a sudden acceleration working condition and a sudden deceleration working condition, the sudden acceleration working condition is that the vehicle speed suddenly rises, the judgment can be carried out through setting the judgment condition, for example, the vehicle speed rises by 20km/h within three seconds, or the vehicle speed rises to exceed the specified speed within specified time, namely, the acceleration of the vehicle is larger than the specified acceleration, and the like, and the vehicle can be identified to be in the sudden acceleration working condition. Similarly, the sudden deceleration condition is that the vehicle speed of the vehicle suddenly drops, and the vehicle can be judged by setting a judging condition, for example, the vehicle speed drops by 20km/h in three seconds, or the vehicle speed drops by more than a specified speed in a specified time, namely, the deceleration of the vehicle is larger than the specified deceleration, the deceleration of the vehicle can be represented by the negative value of the acceleration, and the vehicle can be identified to be in the sudden deceleration condition.
Under the condition of a sudden acceleration working condition or a sudden deceleration working condition, the pose of the vehicle can be changed, for example: under the condition of a sudden acceleration working condition, the vehicle has an upward trend of the head of the vehicle, so that the pose of the vehicle is the head of the vehicle, noise appears when the current road gradient of the vehicle is calculated through a standard Kalman filtering algorithm, and the measured road gradient is inaccurate. Similarly, under the condition of a sudden deceleration working condition, the vehicle has a downward trend of the head of the vehicle, so that the pose of the vehicle is the head of the vehicle downward, noise is generated when the current road gradient of the vehicle is calculated through a standard Kalman filtering algorithm, and the measured road gradient is inaccurate.
In step S120, a forgetting factor coefficient of the operation change condition is determined according to the operation change condition.
Forgetting factor means: the influence of the state of the last steps on the later steps is reduced in the estimation, for example, in the case of encountering an operation change working condition, the adaptability to the operation change working condition is achieved by reducing the weight before the operation change working condition occurs and weighting the operation change working condition of more than a few steps.
For Kalman filtering, if a larger value is selected for the forgetting factor coefficient, the historical data forgets faster, which indicates that the current data reflects the change of the current system, namely the weight of the current data is larger; if the forgetting factor coefficient is smaller, the influence of the historical data on the current system is larger, namely the weight of the historical data is larger. The forgetting factor coefficient can be represented by a, wherein the value range of the reciprocal of a is between 0.95 and 1, namely, the value range of 1 is between 0.95 and 1.
a
In an example, the forgetting factor in the kalman filtering is regarded as 1 (here, a is used to represent the forgetting factor), that is, the historically observed data is not forgotten, but in the case of encountering an operation change condition, the response speed of the historically observed data to the real state at the current moment is poor, so in order to improve the response speed and the precision of parameter identification, the historically observed data needs to be forgotten to a certain extent at the moment of the operation change condition, so that the noise of the road gradient calculation result is reduced, and the accuracy of the kalman filtering on the road gradient calculation is improved.
The mapping relation between the sudden acceleration working condition and the forgetting factor coefficient can be established for the operation change working condition, for example, if the acceleration in three seconds exceeds 10km/h, the sudden acceleration working condition is determined, and meanwhile, the reciprocal of the corresponding forgetting factor coefficient is 0.96. Similarly, a mapping relation can be established between the operation change working condition and the forgetting factor coefficient through the sudden deceleration working condition, for example, if the acceleration in three seconds exceeds-10 km/h, the sudden acceleration working condition is determined, meanwhile, the reciprocal of the corresponding forgetting factor coefficient is 0.98, and the forgetting factor coefficient is determined through establishing the mapping relation and the acceleration in the appointed time. In addition, the neural network can be trained through a certain amount of data of the operation change working condition and the forgetting factor coefficient, so that the trained neural network is obtained, for example, the neural network is trained through data of acceleration and the forgetting factor coefficient in unit time obtained through experiments, and the trained neural network is obtained. And determining the acceleration in unit time through the operation change working condition, inputting the acceleration in unit time into the neural network, and determining the forgetting factor coefficient. In addition to the above, the forgetting factor coefficient may be determined by other modes, such as setting a mapping relation table, etc., through operation change conditions.
In step S130, the covariance and the kalman gain at the previous time are acquired.
And in step S140, determining the road gradient according to the covariance, the kalman gain and the forgetting factor coefficient at the previous moment.
The Kalman filtering algorithm is adjusted through forgetting factor coefficients, and the forgetting factor coefficients are mainly used for adjusting covariance of the Kalman filtering algorithm to achieve adjustment of the Kalman filtering algorithm. Specifically, the following formula (1):
P(kk)＝(I-Kg(k)H)*P(kk-1)*a(1)
wherein: p (kk) is the covariance of the current moment, P (kk-1) is the covariance of the historical moment or the covariance of the last moment, I is a matrix of 1, H is a parameter of an observation system, kg is Kalman gain, and a is a forgetting factor coefficient.
As can be seen from the above formula (1), in this embodiment, the forgetting factor coefficient is introduced by adjusting the weight of the covariance at the previous moment to adjust the covariance at the current moment, thereby adjusting the kalman filtering algorithm to reduce the gradient calculation noise caused by the vehicle posture change.
According to the above formula (1), the covariance of the previous moment needs to be obtained according to real time, the covariance can be obtained by reading the Kalman filter through the whole vehicle controller or the electronic control unit of the vehicle, the Kalman gain can be obtained through the Kalman filter in real time, and the matrix I is 1 and the parameter H is the parameter of the observation system can be directly determined, so that the covariance and the Kalman gain of the previous moment are obtained, and the Kalman filtering algorithm is adjusted and calculated according to the covariance, the Kalman gain and the forgetting factor coefficient of the previous moment. Therefore, the Kalman filtering algorithm can be adjusted by introducing the forgetting factor coefficient, and further, the road gradient is corrected to obtain a more accurate road gradient, so that the stability and the safety of the vehicle in the running process are improved.
That is, the forgetting factor coefficient can adjust the covariance of the historical time or the covariance of the previous time, that is, adjust the weight of the covariance of the historical time or the covariance of the previous time. If a larger value is selected for the forgetting factor coefficient, the historical data forgets faster, which indicates that the current data reflects the change of the current system, namely the weight of the current data is larger; if the forgetting factor coefficient is smaller, the influence of the historical data on the current system is larger, namely the weight of the historical data is larger. The mode of reflecting the current system change is realized by adjusting the covariance of the historical moment or the covariance of the last moment, so that the current real-time situation of the vehicle, namely the covariance of the historical moment or the covariance of the last moment in the Kalman filtering can be adjusted in real time under the condition of the running change working condition, the covariance of the current moment is adjusted, and the noise of the running change working condition on the Kalman filtering when calculating the road gradient can be reduced.
Through the steps, an adjusted Kalman filtering algorithm can be obtained, the forgetting factor coefficient is determined based on the actual operation change working condition, the Kalman filtering algorithm is adjusted based on the forgetting factor coefficient, and the adjusted Kalman filtering algorithm is determined, so that correction during road ramp calculation through the adjusted Kalman filtering is realized, and the more accurate road gradient is obtained.
The operation change working condition of the vehicle is monitored, namely under the condition that the operation change working condition of the vehicle is determined, at the moment, the noise of the road gradient calculated through the Kalman filtering algorithm is larger because the pose of the vehicle is changed, in the embodiment, the Kalman filtering algorithm is adjusted through the operation change working condition determination forgetting factor coefficient, and then the road gradient calculation is carried out through the adjusted Kalman filtering algorithm, so that the noise of the road gradient calculation caused by the change of the pose of the vehicle under the condition that the operation change working condition is generated can be reduced, the Kalman filtering algorithm can be effectively adjusted, the road gradient can be corrected, the more accurate road gradient can be obtained, and the stability and the safety of the vehicle in the running process are improved.
As shown in fig. 2, in one embodiment, monitoring the operating change condition of the vehicle includes:
s210: monitoring that the vehicle speed is smaller than a first designated speed and the opening of the accelerator pedal is larger than the first designated opening;
s220: and determining the operation change working condition of the vehicle as a sudden acceleration working condition.
In this embodiment, the operation change condition of the vehicle is usually a rapid acceleration condition in the case where the vehicle is rapidly accelerated or a condition in the case where the vehicle is rapidly decelerated.
The general acceleration or deceleration of the vehicle under normal conditions is basically the conventional state of the vehicle, and the road gradient can be directly calculated by the Kalman filtering algorithm, and although the calculated road gradient may have a certain error, the error is within an acceptable range, that is, the general acceleration and deceleration also have an influence on the pose of the vehicle, so that the noise of the road gradient calculation is caused, but the influence of the noise on the road gradient is still within an acceptable error range. In the embodiment, under the working condition of operation change, a forgetting factor coefficient is introduced to adjust a Kalman filtering algorithm. Then a sudden acceleration condition under the operating change condition needs to be determined.
In the present embodiment, the determination of the rapid acceleration condition is achieved by monitoring the vehicle speed and the accelerator pedal opening. For example, the whole vehicle controller collects the current opening of an accelerator pedal through a hard wire and obtains the current vehicle speed through a CAN bus.
By monitoring that the vehicle speed is less than the first specified speed, i.e. the current vehicle speed is below a certain speed, e.g. the current vehicle speed is less than 10km/h, i.e. the vehicle is in a low speed operation. When the vehicle speed is lower than the first prescribed speed, if the accelerator pedal opening is larger than the prescribed opening, for example, if the accelerator pedal opening is larger than 60%, the operation change condition is determined to be a rapid acceleration condition when it is determined that rapid acceleration of the vehicle is required. The judgment by the accelerator pedal opening is more delayed, that is, the accelerator is determined by the vehicle speed before acceleration and the vehicle speed after acceleration, than the judgment by the acceleration, which is the information that can be obtained before the vehicle accelerates suddenly. That is, by monitoring that the vehicle speed is smaller than the first specified speed and the accelerator pedal opening is larger than the first specified opening in the present embodiment, the rapid acceleration condition can be recognized more quickly and accurately.
The acceleration of the vehicle can be determined according to the sudden acceleration working condition, and the corresponding acceleration forgetting factor coefficient a1 is determined according to the acceleration of the vehicle.
In addition, by means of the embodiment, the Kalman filtering algorithm is not required to be adjusted under the condition that acceleration occurs, but is adjusted by introducing the forgetting factor coefficient under the condition that the sudden acceleration working condition is determined, so that the operation resources of the vehicle can be reduced.
As shown in fig. 3, in one embodiment, monitoring the operating change condition of the vehicle includes:
s310: monitoring a brake signal and the brake master cylinder pressure of the vehicle being greater than a specified pressure;
s320: and determining the operation change working condition of the vehicle as a sudden deceleration working condition.
The method is similar to the method for determining the sudden acceleration condition in the above embodiment, in the state where the vehicle is decelerating, not all conditions need to be adjusted by the kalman filtering algorithm, but only in the case where the vehicle is determined to be suddenly decelerating, the kalman filtering algorithm is adjusted by introducing the forgetting factor coefficient, so that the operation resources of the vehicle can be reduced.
In this embodiment, the determination of the rapid acceleration condition is achieved by monitoring the brake signal and the brake master cylinder pressure of the vehicle. For example: the whole vehicle controller obtains a braking signal and the pressure of a braking master cylinder through a CAN bus.
By monitoring the brake signal, i.e. the current brake pedal is depressed, or the brake signal is received by the vehicle control unit of the vehicle, the electronic control unit of the vehicle, etc., for example, the brake signal is 1. When the brake signal is monitored, if the brake master cylinder pressure is larger than the specified pressure, for example, the brake master cylinder pressure is larger than 15bar, the operation change working condition is determined to be a rapid deceleration working condition when the control vehicle is required to be rapidly decelerated. The judgment by the brake signal and the brake master cylinder pressure is more delayed than the judgment by the acceleration, and the judgment by the acceleration is more delayed than the judgment by the acceleration, namely, the accelerator can be determined by the vehicle speed before acceleration and the vehicle speed after acceleration, and the brake master cylinder is the information which can be known before the vehicle is rapidly decelerated. That is, by monitoring the brake signal and the brake master cylinder pressure of the vehicle being greater than the specified pressure in the present embodiment, the rapid deceleration condition can be recognized more quickly and accurately.
The acceleration of the vehicle can be determined according to the sudden acceleration working condition, and the corresponding acceleration forgetting factor coefficient a2 is determined according to the acceleration of the vehicle.
In one embodiment, determining the forgetting factor coefficient for the change regime based on the operating change regime includes:
according to the operation change working condition, determining the acceleration and gradient change value of the vehicle;
and determining the forgetting factor coefficient according to the acceleration and gradient change values.
As can be seen from the above embodiments, the operation change conditions include a rapid acceleration condition and a rapid deceleration condition, where the rapid acceleration condition and the rapid deceleration condition can be determined by the change of the acceleration, and the difference is only that the acceleration is positive or negative, that is, the acceleration of the vehicle can be determined by the operation change condition, and the change value of the gradient where the vehicle is currently located can be measured and calculated by, for example, a gyroscope of the vehicle itself or other acquisition device of the vehicle, and the road gradient of the vehicle on the side of the gyroscope of the vehicle itself or other acquisition device of the vehicle belongs to the original value, and the gradient can be corrected by kalman filtering. The forgetting factor coefficient is related to two factors of the acceleration and the gradient change value of the vehicle, namely, when the two factors are changed, the forgetting factor coefficient is also changed, so that the forgetting factor coefficient can be determined by determining the acceleration and the gradient change value of the vehicle.
Under the condition of a sudden acceleration working condition, the acceleration and gradient change value of the vehicle can determine the corresponding forgetting factor coefficient a1.
Under the condition of the sudden deceleration working condition, the acceleration and gradient change values of the vehicle can determine the corresponding forgetting factor coefficient a2.
In one embodiment, said determining the road grade based on the covariance of the last time instant, the kalman gain, and the forgetting factor coefficient comprises:
according to the covariance, kalman gain and forgetting factor coefficient of the last moment, adjusting a Kalman filtering algorithm;
and determining the road gradient according to the adjusted Kalman filtering algorithm.
According to the above formula (1), the covariance of the previous moment and the kalman gain are parameters which are used as the kalman filtering algorithm and can change along with the running condition of the vehicle, and the kalman filtering algorithm needs to be adjusted through the covariance of the previous moment, the kalman gain and the forgetting factor coefficient, so that the adjusted kalman filtering algorithm can more accurately predict the road gradient.
In the embodiment, the forgetting factor coefficient is introduced by adjusting the weight of the covariance of the previous moment to realize the adjustment of the covariance of the current moment, so that the adjustment of the Kalman filtering algorithm is realized to reduce the gradient calculation noise caused by the change of the vehicle posture.
According to the above formula (1), the covariance of the previous moment needs to be obtained according to real time, the covariance can be obtained by reading the Kalman filter through the whole vehicle controller or the electronic control unit of the vehicle, the Kalman gain can be obtained through the Kalman filter in real time, and the matrix I is 1 and the parameter H is the parameter of the observation system can be directly determined, so that the covariance and the Kalman gain of the previous moment are obtained, and the Kalman filtering algorithm is adjusted and calculated according to the covariance, the Kalman gain and the forgetting factor coefficient of the previous moment. Therefore, the Kalman filtering algorithm can be adjusted by introducing the forgetting factor coefficient, and further, the road gradient is corrected to obtain a more accurate road gradient, so that the stability and the safety of the vehicle in the running process are improved.
In a second aspect, FIG. 4 illustrates a block diagram of a road grade calculation device, according to an embodiment of the present application. As shown in fig. 4, an embodiment of the present application provides a road gradient calculating device, which may include:
the first monitoring module 410 is configured to monitor an operation change condition of the vehicle, where the operation change condition includes a sudden acceleration condition and a sudden deceleration condition;
a first determining module 420, configured to determine a forgetting factor coefficient of the operation change condition according to the operation change condition;
a first adjustment module 430, configured to obtain covariance and kalman gain at a previous time;
a second determination module 440 for determining a road grade based on the covariance, the kalman gain, and the forgetting factor coefficient at the previous time.
The operation change working condition of the vehicle is monitored, namely under the condition that the operation change working condition of the vehicle is determined, at the moment, the noise of the road gradient calculated through the Kalman filtering algorithm is larger because the pose of the vehicle is changed, in the embodiment, the Kalman filtering algorithm is adjusted through the operation change working condition determination forgetting factor coefficient, and then the road gradient calculation is carried out through the adjusted Kalman filtering algorithm, so that the noise of the road gradient calculation caused by the change of the pose of the vehicle under the condition that the operation change working condition is generated can be reduced, the Kalman filtering algorithm can be effectively adjusted, further, the more accurate road gradient is obtained through correcting the road gradient, and the stability and the safety of the vehicle in the running process are improved.
In one embodiment, the first monitoring module includes:
the first monitoring unit is used for monitoring that the vehicle speed is smaller than a first designated speed and the opening of the accelerator pedal is larger than the first designated opening;
the first determining unit is used for determining that the operation change working condition of the vehicle is a sudden acceleration working condition.
In one embodiment, the first monitoring module includes:
a second monitoring unit for monitoring a brake signal and a brake master cylinder pressure of the vehicle being greater than a designated pressure;
and the second determining unit is used for determining that the operation change working condition of the vehicle is a sudden deceleration working condition.
In one embodiment, the first determination module includes:
the third determining unit is used for determining the acceleration and gradient change value of the vehicle according to the operation change working condition;
and the fourth determining unit is used for determining the forgetting factor coefficient according to the acceleration and the gradient change value.
In one embodiment, the first adjustment module includes:
the first adjusting unit is used for adjusting a Kalman filtering algorithm according to the covariance, the Kalman gain and the forgetting factor coefficient at the last moment;
and a fifth determining unit that determines the road gradient according to the adjusted kalman filter algorithm.
The functions of each module in each apparatus of the embodiments of the present application may be referred to the corresponding descriptions in the above methods, which are not described herein again.
Fig. 5 shows a block diagram of an electronic device according to an embodiment of the present application. As shown in fig. 5, the electronic device includes: memory 510 and processor 520, and instructions executable on processor 520 are stored in memory 510. The processor 520, when executing the instructions, implements the road gradient calculation method of the above-described embodiment. The number of memories 510 and processors 520 may be one or more. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the application described and/or claimed herein.
The electronic device may further include a communication interface 530 for communicating with external devices for data interactive transmission. The various devices are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor 520 may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input/output device, such as a display device coupled to an interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 510, the processor 520, and the communication interface 530 are integrated on a chip, the memory 510, the processor 520, and the communication interface 530 may communicate with each other through internal interfaces.
It is to be appreciated that the processor described above can be a central processing unit (CentralProcessingUnit, CPU), but can also be other general purpose processors, digital signal processors (DigitalSignalProcessing, DSP), application specific integrated circuits (ApplicationSpecificIntegratedCircuit, ASIC), field programmable gate arrays (FieldProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or any conventional processor or the like. It is noted that the processor may be a processor supporting an advanced reduced instruction set machine (AdvancedRISCMachines, ARM) architecture.
The present embodiments provide a computer readable storage medium (such as the memory 510 described above) storing computer instructions that when executed by a processor implement the methods provided in the embodiments of the present application.
Alternatively, the memory 510 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functions; the storage data area may store data created according to the use of the electronic device, etc. In addition, memory 510 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 510 may optionally include memory located remotely from processor 520, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Any process or method description in a flowchart or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more (two or more) executable instructions for implementing specific logical functions or steps of the process. And the scope of the preferred embodiments of the present application includes additional implementations in which functions may be performed in a substantially simultaneous manner or in an opposite order from that shown or discussed, including in accordance with the functions that are involved.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the methods of the embodiments described above may be performed by a program that, when executed, comprises one or a combination of the steps of the method embodiments, instructs the associated hardware to perform the method.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules described above, if implemented in the form of software functional modules and sold or used as a stand-alone product, may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The foregoing is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various changes or substitutions within the technical scope of the present application, and these should be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (12)
1. A road gradient calculating method, characterized by comprising:
monitoring operation change working conditions of the vehicle, wherein the operation change working conditions comprise a sudden acceleration working condition and a sudden deceleration working condition;
determining a forgetting factor coefficient of the operation change working condition according to the operation change working condition;
acquiring covariance and Kalman gain at the last moment;
and determining the road gradient according to the covariance of the last moment, the Kalman gain and the forgetting factor coefficient.
2. The method of claim 1, wherein the monitoring of the operating change condition of the vehicle comprises:
monitoring that the vehicle speed is smaller than a first designated speed and the opening of the accelerator pedal is larger than the first designated opening;
and determining the operation change working condition of the vehicle as a sudden acceleration working condition.
3. The method of claim 1, wherein the monitoring of the operating change condition of the vehicle comprises:
monitoring a brake signal and the brake master cylinder pressure of the vehicle being greater than a specified pressure;
and determining the operation change working condition of the vehicle as a sudden deceleration working condition.
4. A method according to any one of claims 1-3, wherein said determining a forgetting factor coefficient for a change condition based on said change condition of operation comprises:
according to the operation change working condition, determining the acceleration and gradient change value of the vehicle;
and determining a forgetting factor coefficient according to the acceleration and the gradient change value.
5. The method of claim 1, wherein said determining a road grade based on said last time covariance, said kalman gain, and said forgetting factor coefficient comprises:
according to the covariance of the last moment, the Kalman gain and the forgetting factor coefficient, a Kalman filtering algorithm is adjusted;
and determining the road gradient according to the adjusted Kalman filtering algorithm.
6. A road gradient calculating device, characterized by comprising:
the first monitoring module is used for monitoring the operation change working conditions of the vehicle, wherein the operation change working conditions comprise a sudden acceleration working condition and a sudden deceleration working condition;
the first determining module is used for determining forgetting factor coefficients of the operation change working conditions according to the operation change working conditions;
the first acquisition module is used for acquiring covariance and Kalman gain at the last moment;
and the second determining module is used for determining the road gradient according to the covariance of the last moment, the Kalman gain and the forgetting factor coefficient.
7. The apparatus of claim 6, wherein the first monitoring module comprises:
the first monitoring unit is used for monitoring that the vehicle speed is smaller than a first designated speed and the opening of the accelerator pedal is larger than the first designated opening;
the first determining unit is used for determining that the operation change working condition of the vehicle is a sudden acceleration working condition.
8. The apparatus of claim 6, wherein the first monitoring module comprises:
a second monitoring unit for monitoring a brake signal and a brake master cylinder pressure of the vehicle being greater than a designated pressure;
and the second determining unit is used for determining that the operation change working condition of the vehicle is a sudden deceleration working condition.
9. The apparatus of any one of claims 6-8, wherein the first determining module comprises:
the third determining unit is used for determining the acceleration and gradient change value of the vehicle according to the operation change working condition;
and the fourth determining unit is used for determining a forgetting factor coefficient according to the acceleration and the gradient change value.
10. The apparatus of claim 6, wherein the first determining module comprises:
the first adjusting unit is used for adjusting a Kalman filtering algorithm according to the covariance of the last moment, the Kalman gain and the forgetting factor coefficient;
and a fifth determining unit for determining the road gradient according to the adjusted Kalman filtering algorithm.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A computer readable storage medium having stored therein computer instructions which, when executed by a processor, implement the method of any of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310422060.1A CN116353605A (en) | 2023-04-19 | 2023-04-19 | Road gradient calculation method and device, electronic equipment and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310422060.1A CN116353605A (en) | 2023-04-19 | 2023-04-19 | Road gradient calculation method and device, electronic equipment and storage medium |
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| CN116353605A true CN116353605A (en) | 2023-06-30 |
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| CN202310422060.1A Pending CN116353605A (en) | 2023-04-19 | 2023-04-19 | Road gradient calculation method and device, electronic equipment and storage medium |
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| Country | Link |
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| CN (1) | CN116353605A (en) |
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