CN114644001A - Vehicle load prediction method and device, storage medium and vehicle - Google Patents
Vehicle load prediction method and device, storage medium and vehicle Download PDFInfo
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Abstract
The application provides a vehicle load prediction method, a vehicle load prediction device, a storage medium and a vehicle, and relates to the technical field of vehicles. The method comprises the following steps: acquiring parameter information of a current vehicle, which belongs to a target category, and taking the parameter information of the current vehicle, which belongs to the target category, as characteristic information of the current vehicle; inputting the characteristic information into a load prediction model to obtain the load of the current vehicle output by the load prediction model; the load prediction model is obtained by training a preset model by taking the characteristic information of the test vehicle as a training sample. Because the characteristic information of the current vehicle is information related to the load of the current vehicle, when the load of the current vehicle is predicted by using the characteristic information of the current vehicle and the load prediction model, the prediction result has higher reliability and accuracy, and real-time prediction can be realized.
Description
Technical Field
The application relates to the technical field of vehicles, in particular to a vehicle load prediction method, a vehicle load prediction device, a storage medium and a vehicle.
Background
In order to ensure the safety of road transportation, the loading quality of vehicles is generally strictly regulated, and overweight not only has serious hidden danger on the safety of the vehicles, but also has great threat on the safety of human beings; especially, the cargo vehicles cause serious loss to the life and property safety of people due to the traffic accidents caused by overweight. At present, the following two schemes are generally adopted to measure the load of the vehicle.
According to the first scheme, a sensor is arranged at the position of a vehicle suspension, and the deformation of the suspension is detected through the sensor, so that the vehicle load is calculated.
And in the second scheme, the vehicle load is deduced by acquiring the information of the whole vehicle, utilizing the acceleration and the driving force of the whole vehicle and according to a Newton second law.
However, in the first scheme, a sensor needs to be added to a suspension, the hardware scheme is complex, the weight cost of the whole vehicle is increased, the complexity of the vehicle is increased, and the prior art cannot meet the requirement of mass production; the method is strongly related to the tuning of a suspension system, and if the suspension performance is adjusted, calibration needs to be carried out again, so that the consistency is influenced; when the vehicle runs on a bad road (such as a gravel road and a bumpy road); the displacement fluctuation of the suspension system is large, and a large error is generated. In the second scheme, the power loss of the whole vehicle is uncontrollable, so the driving force calculation error is large; and gradient, road surface unevenness and environment information cannot be obtained, the calculation data fluctuation of severe road conditions is large, and the calculation error is large.
Therefore, the two existing measurement schemes have the problems of large measurement error and incapability of mass production.
Disclosure of Invention
The application provides a vehicle load prediction method, a vehicle load prediction device, a storage medium and a vehicle, which are used for solving the problems that the existing measurement scheme is large in error and cannot be produced in mass.
In order to solve the above problem, in a first aspect, the present application discloses a vehicle load prediction method, including:
acquiring parameter information of a current vehicle, which belongs to a target category, and taking the parameter information of the current vehicle, which belongs to the target category, as characteristic information of the current vehicle;
inputting the characteristic information of the current vehicle into a load prediction model to obtain the load of the current vehicle output by the load prediction model;
the load prediction model is obtained by training a preset model by taking the characteristic information of the test vehicle as a training sample.
In an optional embodiment, the target category is determined based on a correlation coefficient between a parameter value corresponding to the parameter information of the test vehicle and a load of the test vehicle, and the method for determining the target category includes:
acquiring a parameter value corresponding to the parameter information of the test vehicle and the load of the test vehicle;
determining a correlation coefficient between a parameter value corresponding to the parameter information of the test vehicle and the load based on the parameter value corresponding to the parameter information of the test vehicle and the load of the test vehicle;
and screening the parameter information of the test vehicle according to the correlation coefficient between the parameter value corresponding to the parameter information of the test vehicle and the load, and determining the target category of the parameter information to be acquired.
In an optional embodiment, the parameter information of the target category at least includes: road grade, road surface irregularity, and steering information of the vehicle.
In an alternative embodiment, the road surface gradient is obtained by one or more of a sensor, an electronic map and satellite positioning data.
In an alternative embodiment, when the road surface gradient is obtained by the sensor, the road surface gradient information is calculated according to the equations (1), (2), (3):
axvRoadSlope axvSensorRA-axvRaw type (1)
axvSensorRA＝axvSensor+yawRate2ISPdxvRearAxis2Axsensor formula (2)
axvRaw ═ (vxvRef-vxvRefK1)/T formula (3)
Wherein, axvRoadSlope is road surface gradient, axvSensorRA is total acceleration of the vehicle, axvRaw is acceleration of tires, axvSensor is longitudinal acceleration of the vehicle, yawRate is yaw velocity of the vehicle, ISPdxvReaxis 2Axsensor is longitudinal distance from a vehicle test point to the yaw velocity of the vehicle, vxvRef isT1Tire speed at time, vxvRefK1 being T2Tire speed at the moment; t is T1Time and T2The time interval of the moment.
In an alternative embodiment, the road surface irregularities are obtained by means of image information collected by a camera, wherein the camera is mounted at the upper interior mirror of the windscreen of the vehicle, and/or,
the road surface unevenness is obtained by the acceleration and angular acceleration of X, Y, Z in three directions measured by the inertia sensor of the whole vehicle, and/or,
the road surface unevenness is obtained by the longitudinal distance from the wheel center to the wheel arch of the air suspension output.
In an optional embodiment, the load prediction model includes any one of a decision tree model, an XGBoost model, and a random forest model.
In a second aspect, the present application discloses a vehicle load prediction apparatus comprising:
the determining module is used for determining the target category of the parameter information to be acquired based on the correlation coefficient between the parameter value corresponding to the parameter information of the test vehicle and the load of the test vehicle; the parameter information of the test vehicle at least includes: information characterizing engine operating conditions and chassis operating conditions;
the acquisition module is used for acquiring the parameter information of the current vehicle, which belongs to the target category, and taking the parameter information of the current vehicle as the characteristic information of the current vehicle;
the obtaining module is used for inputting the characteristic information of the current vehicle into the load forecasting model and obtaining the load of the current vehicle output by the load forecasting model; the load prediction model is obtained by training a preset model by taking the characteristic information of the test vehicle as a training sample.
In a third aspect, the present application discloses a computer readable storage medium having a vehicle load prediction program stored thereon, wherein the vehicle load prediction program when executed by a processor implements the steps of the vehicle load prediction method of any one of the first aspect.
In a fourth aspect, the present application discloses a vehicle that predicts a load of the vehicle by the vehicle load prediction method of any one of the first aspects described above.
Compared with the prior art, the method has the following advantages:
by adopting the vehicle load prediction method provided by the embodiment of the application, the load prediction model is obtained by training the preset model by taking the characteristic information of the test vehicle as a training sample. Because the characteristic information of the test vehicle and the load of the test vehicle are known, the preset model is trained by utilizing the characteristic information of the test vehicle and the load of the test vehicle, the obtained load prediction model is also a determined model, and the model can well reflect the change rule between the characteristic signal of the vehicle and the load of the vehicle. When the load of the current vehicle needs to be predicted, the load of the current vehicle can be predicted only by inputting the characteristic information of the current vehicle into the load prediction model. Because the characteristic information of the test vehicle is information related to the load of the test vehicle, the accuracy of the load prediction model can be improved when the model is trained by utilizing the characteristic information. Compared with the existing measuring scheme, when the load of the current vehicle is predicted by adopting the load prediction model, the prediction result has higher reliability and accuracy, and real-time prediction can be realized.
Drawings
FIG. 1 is a flow chart of a vehicle load prediction method according to an embodiment of the present application;
FIG. 2 is a block diagram of a configuration for obtaining feature information of a current vehicle according to an embodiment of the present application;
FIG. 3 is a flow chart of determining a target category of parameter information according to an embodiment of the present application;
FIG. 4 is a block diagram of a test vehicle parameter information acquisition system according to an embodiment of the present disclosure;
FIG. 5 is a flow chart of screening a load prediction model according to an embodiment of the present application;
fig. 6 is a block diagram showing a configuration of a vehicle load prediction device according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.
Referring to fig. 1, fig. 1 is a flowchart of a vehicle load prediction method according to an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
s101, acquiring parameter information of the current vehicle, which belongs to a target category, and taking the parameter information which belongs to the target category as characteristic information of the current vehicle;
specifically, in the embodiment of the present invention, the carrier (hardware capable of performing data operation, such as a vehicle-end controller, a cloud server, and the like) acquires the parameter information of the current vehicle belonging to the target category, and uses the parameter information belonging to the target category as the feature information of the current vehicle. The characteristic information of the current vehicle refers to parameter information of the current vehicle belonging to a target category, and the information irrelevant to load is removed from the parameter information of the current vehicle, and the information relevant to load is screened out; the parameter information of the current vehicle is information representing the engine working condition and the chassis working condition of the current vehicle, and the characteristic information of the current vehicle is partial information in the parameter information of the current vehicle.
When the load of the current vehicle is obtained by adopting the load prediction model, if the quantity of parameter information input into the model is too large, the operation efficiency of the model is low. In order to improve the operating efficiency of the model, before calculation, it is necessary to eliminate parameter information that is not related to the load from the parameter information of the current vehicle and use the parameter information related to the load as feature information. The load of the current vehicle is unknown, so that the parameter information of the current vehicle cannot be utilized to screen the characteristic information; since the load of the test vehicle and the parameter information of the test vehicle are known, the load and the parameter information of the test vehicle can be used to screen the parameter information related to the load from the parameter information of the test vehicle, and the category to which the parameter information related to the load belongs can be used as the target category of the parameter information. The target type of the parameter information of the test vehicle is the same as that of the parameter information of the current vehicle, and when the load of the current vehicle is obtained by adopting the load prediction model, the parameter information of the current vehicle, which belongs to the target type, only needs to be input into the model.
It should be noted that the test vehicle in the embodiment of the present application refers to a vehicle participating in parameter information screening and preset model training, and the parameter information and the load of the test vehicle are known; the current vehicle refers to a vehicle for which a load needs to be predicted, parameter information of the current vehicle is known, the load is unknown, and the load of the current vehicle can be predicted using the parameter information (feature information) of the current vehicle. The test vehicle and the current vehicle may be the same vehicle or may be the same type of vehicle.
S102, inputting the characteristic information of the current vehicle into a load prediction model to obtain the load of the current vehicle output by the load prediction model; the load forecasting model is obtained by taking characteristic information of a test vehicle as a training sample and training a preset model.
Specifically, in the embodiment of the invention, the load prediction model is a function representing a change rule between characteristic information of a vehicle and a load of the vehicle, the load prediction model is obtained by training a preset model by taking the characteristic information of a test vehicle as a training sample, the preset model is any one of a decision tree model, an XGboost (extreme Gradient boosting) model and a random forest model, and the load prediction model is the trained preset model. The characteristic information of the test vehicle here refers to parameter information of the test vehicle belonging to the target category, and the characteristic information of the test vehicle is partial information of the parameter information of the test vehicle. The characteristic information of the current vehicle is input into the load prediction model, and the load of the current vehicle can be obtained through the load prediction model.
By adopting the vehicle load prediction method provided by the embodiment of the application, the load prediction model is obtained by training the preset model by taking the characteristic information of the test vehicle as a training sample. Because the characteristic information of the test vehicle and the load of the test vehicle are known, the preset model is trained by utilizing the characteristic information of the test vehicle and the load of the test vehicle, the obtained load prediction model is also a determined model, and the model can well reflect the change rule between the characteristic signal of the vehicle and the load of the vehicle. When the load of the current vehicle needs to be predicted, the load of the current vehicle can be predicted only by inputting the characteristic information of the current vehicle into the load prediction model. Because the characteristic information of the test vehicle is information related to the load of the test vehicle, the accuracy of the load prediction model can be improved when the model is trained by utilizing the characteristic information. Compared with the existing measuring scheme, when the load of the current vehicle is predicted by adopting the load prediction model, the prediction result has higher reliability and accuracy, and real-time prediction can be realized. The real-time prediction of the current vehicle load is beneficial to determining the load grade of the vehicle under the condition that the vehicle load changes, so that a driver can conveniently select a more fuel-saving driving mode according to the load, and meanwhile, the real-time prediction of the current vehicle load is beneficial to the examination and supervision of a supervision department, and the overload is effectively avoided; and on the other hand, the management of the motorcade is facilitated, and the motorcade manager can directly and effectively judge the loading condition of the vehicles in the motorcade according to the real-time uploaded vehicle loading information, so that the statistics and the supervision are facilitated.
In an optional embodiment, when the carrier obtains the feature information of the current vehicle, the feature information of the current vehicle may be obtained directly or indirectly, and the feature information of the current vehicle includes: longitudinal acceleration of the vehicle, lateral acceleration of the vehicle, yaw rate, rotational speed of the engine, state of the engine, vehicle speed, brake pressure, accelerator pedal position, brake pedal position, instantaneous fuel consumption, remaining fuel, engine output torque, engine loss torque, four wheel speed value, transmission input torque, steering wheel angle direction, steering wheel angle speed direction, driver steering torque, gear ratio, wind resistance coefficient, road gradient, and road unevenness.
The rotating speed of the engine refers to the number of turns of a crankshaft in unit time, the state of the engine refers to the starting, stopping (shutdown) and running states of the engine under various loads, the position of an accelerator pedal refers to the strength of a point accelerator of a driver, and when the accelerator pedal is in a zero position, the current vehicle is not in an acceleration running state; the position of a brake pedal refers to the braking force of a driver, when the zero position of the brake pedal indicates that the current vehicle is not in a deceleration running state, the instantaneous fuel consumption refers to the fuel consumption of an engine at a certain moment, the residual fuel refers to the residual fuel in an oil tank, the output torque of the engine refers to the torque output by the engine from a crankshaft end, and the loss torque of the engine refers to the torque lost in the process of outputting the torque by the engine. Vehicle speed refers to the speed at which the vehicle is traveling and is generally derived from the four wheel speed values, brake pressure referring to the pressure applied by the master cylinder to the brake disc, and the four wheel speed values referring to the rotational speed of the tires. The transmission input torque refers to the torque input from the engine end, and the gears refer to P gear, N gear, D gear, R gear and M gear of the transmission. The steering wheel angle refers to the angle of rotation of the steering wheel, the steering wheel angle direction refers to the direction of rotation of the steering wheel, the steering wheel angular velocity refers to the speed of rotation of the steering wheel, the steering wheel angular velocity direction refers to the current direction of the steering wheel, and the driver steering torque refers to the torque applied to the steering wheel by the driver.
In this embodiment, the steering wheel angle direction, the steering wheel angular velocity direction, and the driver steering torque are collectively referred to as the steering information of the vehicle, and the road surface unevenness and the road surface gradient are collectively referred to as the road condition information. Because the steering information and the road condition information of the vehicle are introduced when the load of the current vehicle is obtained, the calculation structure is more accurate, and the gravity change caused by the vehicle going up and down a slope can be eliminated if the slope is introduced.
When the load of the current vehicle is obtained using the feature information of the current vehicle, it is necessary to pay attention to the validity of the feature information. When the characteristic information is invalid (e.g., when the ESP is faulty, the EPS is faulty, the engine is faulty, and the transmission is faulty, the characteristic information is caused to be invalid), the load prediction model outputs a load with a flag, which is an invalid load and should be discarded. The current vehicle load here refers to the weight carried by the current vehicle, usually the sum of the weight of personnel, the weight of cargo and the weight of remaining fuel, and the introduction of the weight of remaining fuel can prevent errors caused by fuel consumption during long-time driving.
Referring to fig. 2, a block diagram of a structure for acquiring characteristic information of a current vehicle according to an embodiment of the present application is shown. In the embodiment of the application, the characteristic information of the current vehicle is acquired through an ABM, an ESP, an EPS, a transmission, an engine, an inertial sensor, an air suspension, a camera and the like, and the characteristic signal of the current vehicle is sensed into a carrier through a CAN bus (Controller Area network).
Specifically, the longitudinal acceleration, the lateral acceleration and the yaw velocity of the vehicle are collected by an ABM and transmitted to a carrier through a CAN bus;
the rotating speed of the engine, the state of the engine, the position of an accelerator pedal, the position of a brake pedal, the instantaneous fuel consumption, the residual fuel, the output torque of the engine and the loss torque of the engine are collected by the engine and transmitted to a carrier through a CAN bus.
The vehicle speed, the brake pressure and the four-wheel speed are collected by an ESP and transmitted to a carrier through a CAN bus.
The input torque and the gear of the transmission are collected by the transmission and transmitted to the carrier through the CAN bus. The transmission input torque refers to the torque input from the engine end of the transmission, and the gears refer to P gear, N gear, D gear, R gear and M gear of the transmission.
The steering wheel angle, the steering wheel angle direction, the steering wheel angular speed direction and the steering torque of a driver are collected by the EPS and transmitted to a carrier through the CAN bus.
The road surface gradient is obtained by one or more of a sensor, an electronic map and satellite positioning data. The road surface gradient of the target road section can be obtained through the electronic map, the satellite positioning data and the sensor on the vehicle, and the road surface gradient of the target road section can also be determined jointly through the electronic map, the satellite positioning data and the sensor data on the vehicle. It should be noted that the slope in the embodiment of the present invention refers to a longitudinal slope.
In one embodiment, when the road surface gradient is obtained by the sensor, the method includes: calculating the road surface gradient according to the formulas (1), (2) and (3):
axvRoadSlope axvSensorRA-axvRaw type (1)
axvSensorRA＝axvSensor+yawRate2ISPdxvRearAxis2Axsensor formula (2)
axvRaw ═ (vxvRef-vxvRefK1)/T formula (3)
Wherein, axvRoadSlope is road surface gradient, axvSensorRA is total acceleration of the vehicle, axvRaw is acceleration of tires, axvSensor is longitudinal acceleration of the vehicle, yawRate is yaw velocity of the vehicle, ISPdxvReaxis 2Axsensor is longitudinal distance from a vehicle test point to the yaw velocity of the vehicle, vxvRef is T1Tire speed at time, vxvRefK1 being T2Tire speed at the moment; t is T1Time and T2The time interval of the moment.
Specifically, the longitudinal acceleration of the vehicle is acquired by a vehicle longitudinal acceleration sensor mounted on an airbag, the yaw rate of the vehicle is acquired by a yaw rate sensor mounted on the airbag, and T is acquired by a velocity sensor mounted on the airbag1And T2The tire speed at the moment, while the longitudinal distance of the vehicle test point to the yaw rate of the vehicle can be understood as the distance of the rear axle center to the airbag, usually a fixed value.
Note that the structure calculated by equation (3) is an approximate value, that is, the difference between the total acceleration of the vehicle and the acceleration of the tire is considered to be approximately equal to the road surface gradient. The road surface gradient is a change value of the height of the vehicle every 1m, for example, the gradient is 0.25, which represents that the height is changed by 25m when the vehicle runs for 100 m.
Illustratively, when the longitudinal acceleration of the vehicle is 0.25m/s2The yaw rate was 1rad/s, the distance from the center of the rear axle to the airbag was 0.5m, and the total acceleration of the vehicle was 0.75m/s as seen from the formula (2)2(ii) a When T is1The tire speed at the time is 30Km/h and T1T with time interval of 0.5min2The tire speed at that time was 84Km/h, and the acceleration of the tire was 0.5m/s as shown in the formula (3)2(ii) a The total acceleration of the vehicle and the acceleration of the tire are calculated by substituting the formula (1), and the gradient of the road surface is known to be0.25。
The road surface unevenness is obtained in the following manner: the image information is acquired through a camera, wherein the camera is arranged at the upper inner rear-view mirror of the automobile windshield glass, and/or the information of the acceleration and the angular acceleration of X, Y, Z in three directions measured by an inertia sensor of the whole automobile is acquired, and/or the information of the longitudinal distance from the wheel center to the wheel arch output by the air suspension is acquired.
Specifically, the road unevenness is obtained by the following methods: (1) the camera arranged at the inner rear-view mirror at the upper part of the automobile windshield glass collects the current road surface information of the automobile, calculates the unevenness of the road surface by using an image processing technology and inputs the calculated unevenness of the road surface into a carrier; (2) the vehicle inertial sensor can measure the acceleration and the angular speed of the vehicle X, Y, Z in three directions, and the road surface unevenness is obtained by using the acceleration and the angular speed change; (3) the air suspension can output the longitudinal distance from the wheel center to the wheel arch, and the road surface unevenness is obtained by utilizing the longitudinal distance change from the wheel center to the wheel arch; (4) fusing the obtained road surface unevenness of the scheme (1) and the scheme (2) to obtain the road surface unevenness, for example, averaging the results of the scheme (1) and the scheme (2), and taking the average value as the road surface unevenness; (5) fusing the road surface unevenness obtained by the scheme (1) and the scheme (3) to obtain the road surface unevenness, and averaging the results of the scheme (1) and the scheme (3) to obtain the average value as the road surface unevenness; (6) fusing the road surface unevenness obtained by the scheme (2) and the scheme (3) to obtain the road surface unevenness, and taking the average value as the road surface unevenness, for example, averaging the results of the scheme (2) and the scheme (3); (7) and fusing the road surface unevenness obtained by the scheme (1), the scheme (2) and the scheme (3) to obtain the road surface unevenness, for example, averaging the results of the scheme (1), the scheme (2) and the scheme (3), and taking the average value as the road surface unevenness.
Fig. 3 is a flowchart of determining a target category of parameter information according to an embodiment of the present application. As shown in fig. 3, in one embodiment, in step S101, the target category is determined based on a correlation coefficient between a parameter value corresponding to the parameter information of the test vehicle and a load of the test vehicle, and the method for determining the target category includes:
s201, obtaining a parameter value corresponding to the parameter information of the test vehicle and the load of the test vehicle;
referring to fig. 4, in the embodiment of the present application, parameter information of the test vehicle is obtained through an ABM (Air Bag Module), an ESP (Electronic Stability Program), an EPS (Electric Power Steering), a transmission, an engine, an inertial sensor, an Air suspension, a camera, and the like, and the parameter information of the test vehicle at least includes: information characterizing engine operating conditions and chassis operating conditions.
Specifically, the motion information of the test vehicle (such as the lateral acceleration of the vehicle, the yaw velocity of the vehicle, the longitudinal acceleration of the vehicle, etc.) is acquired by using the ABM, the brake and tire information of the test vehicle (such as the brake pressure, the velocity of the tire, the acceleration of the tire, the tire cornering power, the vehicle speed, the tire temperature, the tire pressure, etc.) is acquired by using the ESP, the transmission information (such as the gear position, the transmission output torque, etc.) of the test vehicle is acquired by using the transmission, the power information (such as the engine torque, the engine rotational speed, the remaining oil amount, the accelerator pedal position, etc.) of the test vehicle is acquired by using the engine, the steering information (such as the steering wheel angle, the steering wheel angle direction, the steering wheel angle velocity direction, the driver steering torque, etc.) of the test vehicle is acquired by using the EPS, and the camera at the rear-view mirror in the upper portion of the windshield of the vehicle is utilized, Acquiring the road surface unevenness by one or more combinations of an inertial sensor and an air suspension; and acquiring the road surface gradient by one or more modes of a sensor, an electronic map and satellite positioning data. The wind resistance coefficient, the wind resistance area and the transmission ratio are fixed parameters of the whole train speed, and can be input into a carrier; the road unevenness and the road gradient are indirectly obtained by other means, and therefore the manner of obtaining the wind resistance coefficient, the wind resistance area, the gear ratio, the road unevenness, and the road gradient is not shown in fig. 4.
S202, determining a correlation coefficient between a parameter value corresponding to the parameter information of the test vehicle and the load based on the parameter value corresponding to the parameter information of the test vehicle and the load of the test vehicle;
specifically, in the embodiment of the present invention, based on the parameter value corresponding to the parameter information of the test vehicle and the load of the test vehicle, the correlation coefficient between the parameter value corresponding to the parameter information of the test vehicle and the load is determined by using the pearson correlation coefficient method. According to the characteristics of the Pearson correlation coefficient method, when the variance of two variables is not zero, the Pearson correlation coefficient is meaningful, and the value range of the Pearson correlation coefficient is [ -1,1 ]. The pearson correlation coefficient describes the degree of linear correlation between two variables. If the Pearson correlation coefficient is larger than zero, the positive correlation of the two variables is represented, namely the larger the value of one variable is, the larger the value of the other variable is; if the pearson correlation coefficient is less than zero, it indicates that the two variables are negatively correlated, i.e. if one variable value is larger, the other variable value is smaller, the larger the absolute value of the pearson correlation coefficient is, the stronger the correlation is, and if the pearson correlation coefficient is zero, it indicates that there is not a linear relationship between the two variables, but there is a possibility of other ways of correlation.
S203, screening the parameter information of the test vehicle according to the correlation coefficient between the parameter value corresponding to the parameter information of the test vehicle and the load, and determining the target type of the parameter information to be acquired.
Specifically, in the embodiment of the present invention, after parameter information of a test vehicle is screened by a pearson correlation coefficient method, a category to which parameter information related to a load belongs is set as a target category of the parameter information. Such as deleting information of tire temperature, tire pressure, primary and secondary driving belt alarms, and the like, and using longitudinal acceleration of the vehicle, lateral acceleration of the vehicle, rotational speed of the engine, state of the engine, vehicle speed, brake pressure, accelerator pedal position, brake pedal position, instantaneous fuel consumption, remaining fuel, engine output torque, engine loss torque, quad wheel speed value, transmission input torque, steering wheel angle direction, steering wheel angle speed direction, driver steering torque, gear position, gear ratio, wind resistance coefficient, road surface gradient, road surface unevenness, and the like as target categories of parameter information.
In an optional embodiment, when the characteristic information of the test vehicle is used as a training sample and a preset model is trained to obtain a load prediction model, the load prediction model is an XGBoost model. The XGboost model is an optimal model screened out from a decision tree model, an XGboost model and a random forest model. FIG. 5 illustrates a flow chart for screening a load prediction model. As shown in fig. 5, in an embodiment, in step S102, the training of the preset model by using the characteristic information of the test vehicle as a training sample by the load prediction model includes:
s301, training a preset model by taking the characteristic information of the test vehicle as a training sample to obtain a plurality of candidate models, wherein the preset model comprises any one of a decision tree model, an XGboost (extreme Gradient boosting) model and a random forest model.
In the present embodiment, the characteristic information of the test vehicle includes a longitudinal acceleration of the test vehicle, a lateral acceleration of the test vehicle, a rotational speed of the engine, a state of the engine, a vehicle speed, a brake pressure, an accelerator pedal position, a brake pedal position, an instantaneous fuel consumption, a remaining fuel, an engine output torque, an engine loss torque, a four-wheel speed value, a transmission input torque, a steering wheel angle direction, a steering wheel angle speed direction, a driver steering torque, a gear ratio, a wind resistance coefficient, a road surface gradient, and a road surface unevenness. And respectively inputting the data of the test vehicle into a decision tree model, an XGboost model and a random forest model for training to obtain a plurality of candidate models. The plurality of candidate models herein refers to: a trained decision tree model, a trained XGboost model and a trained random forest model.
The characteristic information of the test vehicle is the same as that of the current vehicle, but the specific numerical values corresponding to the characteristic information of the test vehicle and that of the current vehicle are not the same. The characteristic information of the current vehicle is mainly used for presetting the load of the current vehicle, so that the corresponding numerical value of the characteristic information of the current vehicle is only one, such as only one vehicle speed and one residual fuel value; the characteristic information of the test vehicle is mainly used for training a preset model, so that the characteristic information of the test vehicle has a plurality of values, such as M vehicle speed values and N residual fuel values.
S302, screening out the trained XGboost model from the candidate models to serve as a load prediction model according to the respective calculation accuracy and performance of the candidate models.
Specifically, the feature information of the test vehicle is input into each of the candidate models, the load corresponding to the feature information is calculated by using the candidate model, the calculated value is compared with the true value of the test vehicle, the candidate model with the smallest error is selected as the load prediction model of the present embodiment, and the trained XGBoost model is finally selected as the load prediction model of the present embodiment. Because the characteristic information of the test vehicle has a plurality of values, part of the characteristic information can participate in model training, and the other part of the characteristic information can be subjected to model verification; a plurality of numerical values can also participate in model training, and a part is selected for model verification. How many samples to choose to participate in the training is not limited in this implementation.
In addition, in the vehicle driving process, the vehicle can meet vehicle faults caused by the vehicle or environment, the collected driving data is invalid or abnormal, in order to eliminate errors caused by emergency situations for parameter information screening and subsequent preset model training, before parameter information screening is carried out, data cleaning needs to be carried out on the parameter information collected by the test vehicle, invalid signals and abnormal signals in the data are deleted according to data effectiveness and rationality, and effective data are screened out for parameter information screening and subsequent preset model training.
Based on the same inventive concept, fig. 6 is a block diagram of a vehicle load prediction apparatus according to an embodiment of the present application. As shown in fig. 6, the apparatus includes:
the acquisition module 101 is used for acquiring parameter information of the current vehicle load, which belongs to a target category, and taking the parameter information which belongs to the target category as the characteristic information of the current vehicle;
an obtaining module 102, configured to input the feature information into the load prediction model, and obtain a load of the current vehicle output by the load prediction model; the load prediction model is obtained by training a preset model by taking the characteristic information of the test vehicle as a training sample.
In an optional embodiment, the apparatus further comprises:
and the determining module is used for determining the target category based on the correlation coefficient between the parameter value corresponding to the parameter information of the test vehicle and the load of the test vehicle.
In an optional embodiment, the determining module comprises:
the acquisition submodule is used for acquiring a parameter value corresponding to the parameter information of the test vehicle and the load of the test vehicle;
the first determining submodule is used for determining a correlation coefficient between a parameter value corresponding to the parameter information of the test vehicle and the load based on the parameter value corresponding to the parameter information of the test vehicle and the load of the test vehicle;
and the second determining submodule is used for screening the parameter information of the test vehicle according to the correlation coefficient between the parameter value corresponding to the parameter information of the test vehicle and the load, and determining the target category of the parameter information to be acquired.
In an alternative embodiment, the vehicle load prediction apparatus further comprises:
and the training module is used for training the preset model by taking the characteristic information of the test vehicle as a training sample to obtain a load prediction model.
Based on the same inventive concept, embodiments of the present application provide a computer-readable storage medium having a vehicle load prediction program stored thereon, where the vehicle load prediction program, when executed by a processor, implements the steps of the vehicle load prediction method of any one of the above embodiments.
Based on the same inventive concept, embodiments of the present application provide a vehicle that predicts a vehicle load by the vehicle load prediction method of any one of the above embodiments.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The method for allocating resources and the device for allocating resources provided by the present invention are described in detail above, and the principle and the implementation of the present invention are explained in this document by applying specific examples, and the description of the above examples is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
The method, the system and the vehicle for predicting the vehicle load provided by the application are introduced in detail, specific examples are applied in the description to explain the principle and the implementation mode of the application, and the description of the embodiments is only used for helping to understand the method and the core idea of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A vehicle load prediction method, characterized in that the method comprises:
acquiring parameter information of a current vehicle, which belongs to a target category, and taking the parameter information of the current vehicle, which belongs to the target category, as characteristic information of the current vehicle;
inputting the characteristic information of the current vehicle into a load prediction model to obtain the load of the current vehicle output by the load prediction model;
the load prediction model is obtained by training a preset model by taking the characteristic information of the test vehicle as a training sample.
2. The vehicle load prediction method according to claim 1, wherein the target category is determined based on a correlation coefficient between a parameter value corresponding to the parameter information of the test vehicle and a load of the test vehicle, and the method for determining the target category includes:
acquiring a parameter value corresponding to the parameter information of the test vehicle and the load of the test vehicle;
determining a correlation coefficient between a parameter value corresponding to the parameter information of the test vehicle and the load based on the parameter value corresponding to the parameter information of the test vehicle and the load of the test vehicle;
and screening the parameter information of the test vehicle according to the correlation coefficient between the parameter value corresponding to the parameter information of the test vehicle and the load, and determining the target category of the parameter information to be acquired.
3. The vehicle load prediction method according to claim 1, characterized in that the parameter information of the target category includes:
road grade, road surface irregularity, and steering information of the vehicle.
4. The vehicle weight prediction method of claim 3, wherein the road gradient is obtained by one or more of a sensor, an electronic map, and satellite positioning data.
5. The vehicle load prediction method according to claim 4, characterized in that the road surface gradient is calculated in accordance with equations (1), (2), (3) when the road surface gradient is obtained by the sensor:
axvRoadSlope axvSensorRA-axvRaw type (1)
axvSensorRA＝axvSensor+yawRate2ISPdxvRearAxis2Axsensor formula (2)
axvRaw ═ (vxvRef-vxvRefK1)/T formula (3)
Wherein, axvRoadSlope is road surface gradient, axvSensorRA is total acceleration of the vehicle, axvRaw is acceleration of tires, axvSensor is longitudinal acceleration of the vehicle, yawRate is yaw velocity of the vehicle, ISPdxvReaxis 2Axsensor is longitudinal distance from a vehicle test point to the yaw velocity of the vehicle, vxvRef is T1Tire speed at time, vxvRefK1 being T2Tire speed at the moment; t is T1Time and T2The time interval of the moment.
6. The vehicle load prediction method according to claim 3, wherein the road surface unevenness is obtained from image information collected by a camera mounted at an upper inside rear view mirror of a windshield of an automobile, and/or,
the road unevenness is obtained by the information of X, Y, Z acceleration and angular acceleration measured by the inertia sensor of the whole vehicle, and/or,
the road unevenness is obtained by the longitudinal distance from the wheel center to the wheel arch of the air suspension output.
7. The vehicle load prediction method according to any one of claims 1 to 6, characterized in that the load prediction model includes:
any one of a decision tree model, an XGboost model and a random forest model.
8. A vehicle load prediction apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring parameter information of a current vehicle, which belongs to a target category, and taking the parameter information of the current vehicle as feature information of the current vehicle;
the obtaining module is used for inputting the characteristic information of the current vehicle into a load forecasting model and obtaining the load of the current vehicle output by the load forecasting model; the load prediction model is obtained by training a preset model by taking the characteristic information of the test vehicle as a training sample.
9. A computer-readable storage medium, characterized in that a vehicle load prediction program is stored thereon, which when executed by a processor implements the steps of the vehicle load prediction method according to any one of claims 1 to 7.
10. A vehicle characterized in that the vehicle predicts a load of the vehicle by the vehicle load prediction method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116502565A (en) * | 2023-06-27 | 2023-07-28 | 江铃汽车股份有限公司 | Air dam performance test method, system, storage medium and equipment |
| CN117494585A (en) * | 2023-12-29 | 2024-02-02 | 中汽研汽车检验中心（天津）有限公司 | Commercial vehicle actual load prediction method based on mutual information and data blurring |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116502565A (en) * | 2023-06-27 | 2023-07-28 | 江铃汽车股份有限公司 | Air dam performance test method, system, storage medium and equipment |
| CN116502565B (en) * | 2023-06-27 | 2023-11-14 | 江铃汽车股份有限公司 | Air dam performance test method, system, storage medium and equipment |
| CN117494585A (en) * | 2023-12-29 | 2024-02-02 | 中汽研汽车检验中心（天津）有限公司 | Commercial vehicle actual load prediction method based on mutual information and data blurring |
| CN117494585B (en) * | 2023-12-29 | 2024-04-02 | 中汽研汽车检验中心（天津）有限公司 | Commercial vehicle actual load prediction method based on mutual information and data blurring |
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