CN112528568A - Road feel simulation method based on K-Means and BP neural network - Google Patents
Road feel simulation method based on K-Means and BP neural network Download PDFInfo
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Abstract
The invention discloses a road sense simulation method based on K-Means and BP neural networks, which comprises the following steps: carrying out a real vehicle test and collecting test data; processing the test data; clustering test data by using a K-Means clustering algorithm and dividing the test data into a training data set after clustering and a test data set after clustering; training a road feel model based on K-Means and BP neural networks by using a BP neural network algorithm; testing a road feel model based on K-Means and BP neural networks; and performing road feel simulation according to the obtained road feel simulation model based on the K-Means and the BP neural network. According to the method, the real vehicle is used for collecting test data, the road feel simulation model is modeled by adopting a data-driven modeling method based on a K-Means clustering algorithm and a BP neural network algorithm, the modeling time is short, the model calculation speed is high, and the method has the advantages of obvious high precision and real-time performance.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to a road feel simulation method based on K-Means and BP neural networks.
Background
The steering road feel, also called steering force feel and steering wheel feedback torque, refers to the reverse resistance torque felt by the driver through the steering wheel feedback torque. The road feel can enable a driver to know the road condition in real time, so that corresponding decisions can be made. Therefore, it is important for a vehicle or a driving simulator using a steer-by-wire system to generate a road feel similar to that of a conventional steering system using a motor or the like. However, the road sensing simulation system realized by the prior art has the defects of low precision, poor real-time performance and the like.
The utility model patent with the application number of CN201420478919.7 and the name of 'force feeling simulation system based on C-EPS structure' mainly simulates road feeling through a mechanism modeling method, and parameters needing to be adjusted are numerous, and the precision is difficult to guarantee.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a road feel simulation method based on data driving, which has high calculation speed and low modeling cost, establishes a road feel simulation model by real vehicle test data and a data driving algorithm, and solves the problems of complex model structure, low precision, difficult real-time property guarantee in the application process and the like in the traditional mechanism modeling.
In order to achieve the above object, the present invention provides a road sensing simulation method based on K-Means and BP neural networks, comprising the steps of:
step one, collecting test data: the method comprises the following steps that a driver performs a real vehicle test, a vehicle runs in a test road, and collected test data comprise vehicle speed, vehicle lateral acceleration, vehicle yaw velocity, vehicle vertical load, a driver steering wheel corner, steering wheel angular velocity and steering wheel moment;
step two, processing test data: carrying out normalization processing on the test data after removing abnormal points to obtain a normalized test data set;
step three, carrying out normalized test data classification: clustering the normalized test data by using a K-Means clustering algorithm, dividing the normalized test data into a plurality of data classes after clustering, and dividing the clustered test data into a clustered training data set and a clustered test data set;
step four, training a road feel model based on K-Means and BP neural networks: when a road feel simulation model based on K-Means and BP neural networks is trained by using a clustered training data set and a BP neural network algorithm, the input variables of the BP neural network model are vehicle speed, vehicle lateral acceleration, vehicle yaw velocity, vehicle vertical load, driver steering wheel turning angle and steering wheel angular velocity, the output variables are steering wheel moment, and the same number of road feel simulation models based on K-Means and BP neural networks as data are obtained through training;
step five, testing a road feel model based on K-Means and BP neural networks; testing the road feel simulation model based on the K-Means and BP neural networks by using the clustered test data set, and judging whether to perform the test again;
and sixthly, performing road feel simulation according to the obtained road feel simulation model based on the K-Means and the BP neural network.
Further, in the real vehicle test of the step one:
the test road types comprise urban roads, expressways, suburban roads and rural roads;
the vehicle running working conditions comprise straight running, backing, turning, pivot steering, uphill and downhill working conditions.
Further, in step two, the removed abnormal points include data points beyond the normal value range, data points with severely deviated distribution, and data points with a variation range beyond the normal range.
The data points beyond the normal value range are defined as: and (3) acquiring a certain data point in a certain real vehicle test, wherein the numerical value of one or more variables exceeds the actual normal value range of the corresponding variable of the real vehicle test. If the maximum vehicle speed is only 50km/h in a certain test, the data points with the vehicle speed value more than 50km/h in the data collected in the test are all out of range points. For another example, if the steering wheel angle range is [ -300 °,300 ° ] in a certain test, the points of the steering wheel angle values exceeding [ -300 °,300 ° ] in the data collected in the test are all out of the normal range.
The heavily distributed data points are defined as: and calculating the standard deviation of each variable of the test data acquired in a certain real vehicle test, and if the numerical value of one or more variables of a certain data point is more than 2.5 times of the standard deviation of the corresponding variable or less than minus 2.5 times of the standard deviation of the corresponding variable, determining that the distribution of the data point is seriously deviated.
The data points with the variation amplitude exceeding the normal range are defined as follows: the maximum instantaneous change amplitude of each variable under the normal condition is preset, and if the absolute value of the difference value of one or more variable values of a certain data point relative to the corresponding variable value of the previous data point in the actual test data set is larger than the maximum instantaneous change amplitude of the related variable, the maximum instantaneous change amplitude of each variable exceeds the normal range. For example, when a high-speed driving test is performed using a small passenger car, the engineer confirms that the maximum instantaneous change amplitude of the steering wheel torque is 0.3N, and when the absolute value of the difference between the steering wheel torque value and the previous data point in all the data points is greater than 0.3N, the change amplitude is regarded as a point beyond the normal range.
Further, in the second step, the method further includes filtering the abnormal points of the test data by using a filter, and the method for deleting the abnormal points by using the filter includes: and inputting the test data into a low-pass filter, a high-pass filter or a band-pass filter at one time according to the original acquisition sequence to obtain the filtered test data.
Further, in step two, the test data is normalized according to the following formula:
wherein i is a data number, j is a variable number, and xi,jDenotes the j variable, X, in the non-normalized i group of datajRepresents the set of variable data values corresponding to all j, min represents the minimum value,max represents the maximum value.
Further, in the second step, the normalized test data set is divided into a training data set and a testing data set by a random division method, wherein the random division method comprises the following steps: randomly selecting a certain proportion of data points from the normalized test data set as a training data set, and taking the other data points as test data sets.
Preferably, in step three, when the K-Means clustering algorithm is used for clustering the normalized test data, the specific step of clustering the test data by using the K-Means clustering algorithm includes:
1) setting the number K of types to be divided;
2) clustering the normalized test data by using a K mean value clustering algorithm to obtain K category central points;
3) and calculating Euclidean distances between a certain newly input data point and K category center points, wherein the type of the center point corresponding to the minimum Euclidean distance value is the type of the newly input data point.
In a specific embodiment, the number of clusters is set to be 3, 3 cluster center points are obtained after clustering, and the normalized test data is divided into 3 data classes according to the coordinates of the 3 cluster center points.
Taking the clustered training data set as a clustered training data set; and aggregating the clustered test data set as the clustered test data.
Preferably, in step four, when the road sensing simulation model based on the K-Means and the BP neural network is trained, the BP neural network model has 11 hidden layers, each hidden layer has 20 nodes, 1 input layer and 1 output layer; all the activation functions of all the nodes are Sigmoid functions and are all fully connected; the learning function uses the learngdm function, and the iteration upper limit is 1000 generations.
When a road feel simulation model of the BP neural network is trained, the model obtained by training the same type of training data points is related to the type of the data points, namely the road feel simulation model corresponding to a certain type of training data points can only be used for predicting the steering wheel torque of the type of data points. After the training data points of multiple types are trained, multiple corresponding road feel simulation models are obtained.
The related parameter determining steps for training the BP neural network are as follows:
1) determining input parameters and output parameters of a BP neural network;
2) determining the number of hidden layers of the BP neural network;
3) determining the number of nodes of the hidden layer;
4) determining an activation function and an integral learning function of each node;
5) training a BP neural network model using the training data;
6) and judging whether to return to the step 2) according to the model test result. And if the training result meets the requirement, directly carrying out the next step without returning, otherwise, returning to the step 2).
Further, when testing the road sensing simulation model based on the K-Means and BP neural networks, the mean square error, i.e., MSE value, can be used, but is not limited to the use thereof, as the criterion of the model quality. When the road sensing simulation model based on the K-Means and BP neural network is tested by using a test data set, the steps are as follows:
1) taking out a test data point in the test data set, and inputting the value of an input variable corresponding to the test data point into a road feel simulation model corresponding to the class to which the test data point belongs to obtain a predicted steering wheel torque value;
2) step 1) is carried out iteratively until all the test data points are predicted by using a road feel simulation model;
3) calculating a Mean Square Error (MSE) value between a predicted steering wheel moment value and a real steering wheel moment value through a model at a test data point of the whole test data set obtained through calculation;
4) judging whether to carry out the test again: and if the MSE value is smaller than a preset threshold value alpha, the road feel simulation model based on the K-Means and the BP neural network obtained by training is acceptable, and the modeling is successful. Otherwise it is not acceptable. The threshold value alpha is determined empirically by an expert.
Due to the adoption of the technical scheme, the invention achieves the following technical effects: the method is based on data acquired by a real vehicle test, adopts a BP neural network algorithm to carry out modeling after clustering through a K-Means clustering algorithm to obtain a road feel simulation model for predicting steering wheel feedback torque, and carries out simulation of the steering wheel feedback torque according to the obtained road feel simulation model, so that the problems that the model of the traditional mechanism modeling is low in precision, difficult to guarantee real-time performance in the application process and the like are solved; compared with the prior art, the road feel simulation model has the advantages of convenient data acquisition, high calculation speed and low modeling cost.
Drawings
FIG. 1 is a flow chart of modeling steps in a road feel simulation method based on K-Means and BP neural networks according to the invention.
FIG. 2 is a portion of steering wheel moment data collected in an embodiment in accordance with the invention.
FIG. 3 is a partial graph of a model test curve for performing a model test according to an embodiment of the present invention.
Detailed Description
In order to make the technical solution of the embodiments of the present invention better understood, the technical solution of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by equivalent changes and modifications by one skilled in the art based on the embodiments of the present invention, shall fall within the scope of the present invention.
Example one
Referring to fig. 1 to 3, the present embodiment provides a road feel simulation method based on K-Means and BP neural networks, including real vehicle testing and modeling steps S1-S5, and model application step S6. Steps S1-S5 of the modeling process are described in detail below in conjunction with FIG. 1.
S1, carrying out a real vehicle test and collecting test data:
and selecting a driver to carry out a real vehicle test, and driving the vehicle to run in the test road. The test road types include, but are not limited to, urban roads, expressways, suburban roads, rural roads, and the like; vehicle driving conditions include, but are not limited to, straight ahead, reverse, turning, pivot steering, uphill, downhill, and the like.
The selected driver had a driving age of four years and was driven no less than 2 hours per week in the last year. The data acquisition frequency was 100 Hz.
The collected test data includes vehicle speed, vehicle lateral acceleration, vehicle yaw rate, vehicle vertical load, driver steering wheel angle, steering wheel angular velocity, and steering wheel moment. As shown in fig. 2, the steering wheel torque data collected in the test of this example is represented by an actual steering wheel torque-time curve (Real).
S2, processing test data:
processing the test data includes removing outliers and normalizing the data. The removed abnormal points include data points outside the normal value range, data points with severely deviated distribution and data points with the variation amplitude exceeding the normal range. The embodiment further includes filtering the abnormal points of the test data by using a filter, and the method for deleting the abnormal points by using the filter includes: and inputting the test data into a low-pass filter, a high-pass filter or a band-pass filter at one time according to the original acquisition sequence to obtain the filtered test data.
In this embodiment, the collected test data is normalized according to the following formula:
wherein i is a data number, j is a variable number, and xi,jDenotes the j variable, X, in the non-normalized i group of datajRepresents the set of variable data values corresponding to all j, min represents the minimum value, and max represents the maximum value.
S3, carrying out normalized test data clustering and dividing a training data set and a test data set
And (5) carrying out normalized test data clustering by using a K-Means clustering algorithm, wherein the clustering number is set to be 3. And 3 clustering center points are obtained after clustering, and the normalized test data is divided into 3 data classes according to the coordinates of the 3 clustering center points. After clustering, dividing according to a random division method according to the data point quantity ratio of 4:1 to obtain a training data set after clustering and a test data set after clustering.
S4, training a road feel model based on K-Means and BP neural networks:
and training by using the clustered training data set and the BP neural network algorithm to obtain the road feel simulation model based on the K-Means and the BP neural network, wherein the quantity of the road feel simulation model is the same as that of the data sets. The input variables of the BP neural network model comprise vehicle speed, vehicle lateral acceleration, vehicle yaw velocity, steering wheel turning angle, steering wheel angular velocity and vehicle vertical load; the output variable is the steering wheel torque. When the model is trained, the model obtained by training the same type of modeling data points is related to the related type, namely, the data points of a certain type of model can only be used for predicting the data points of the type. The number of the models obtained by training the training data points is the same as the number of the data point types, and the number of the road sense simulation models based on the K-Means and the BP neural network obtained in the embodiment is 3.
1) Determining input parameters and output parameters of the BP neural network: the input variables comprise vehicle speed, vehicle lateral acceleration, vehicle yaw rate, steering wheel angle and steering wheel angular speed; the output variable is steering wheel torque;
2) determining the number of hidden layers of the BP neural network: selecting 11 hidden layers;
3) determining the number of nodes of the hidden layer: each hidden layer node is determined to be 20;
4) determining an activation function and a whole learning function of each node: the activation function is determined as a Sigmoid function, and the learning function uses a learngdm function;
5) training a BP neural network model using the training data;
6) and judging whether to return to the step 2) according to the model test result. And if the training result meets the requirement, directly carrying out the next step without returning, otherwise, returning to the step 2).
In this embodiment, the BP neural network model has 11 hidden layers, each hidden layer has 20 nodes, 1 input layer and 1 output layer; all the activation functions of all the nodes are Sigmoid functions and are all fully connected; the learning function uses the learngdm function, and the iteration upper limit is 1000 generations. The Hewlett packard Z1G6 workstation was used for training in this example, and the total training time of 3 road feel simulation models was 2 hours and 8 minutes.
S5, testing a road feel model based on K-Means and BP neural networks, and judging whether to perform the real vehicle test again:
when testing the road feel simulation model based on the K-Means and BP neural network, the step of testing the obtained road feel simulation model based on the K-Means and BP neural network by using the test data set comprises the following steps:
1) taking out data points in the test data set, and inputting input variables corresponding to the data points into road feel simulation models of corresponding types to obtain a predicted steering wheel torque value;
2) and calculating to obtain an MSE value 0.083 for measuring the quality of the road feel simulation model. As shown in fig. 3, which shows a part of the model test curve, the steering wheel torque-time curve (Predict) of the test data point predicted by the model almost coincides with the actual steering wheel torque-time curve (Real), and the MSE value is 0.083174.
3) Judging whether the modeling is successful or not
The model is used for test data prediction, and the obtained MSE value, namely 0.083 is smaller than the threshold value alpha preset by an expert, namely 0.15, and the obtained model is acceptable.
After the modeling is completed, the road feel simulation method further comprises the step of predicting the steering wheel moment by using the obtained 3 road feel simulation models, namely road feel simulation. Inputting the obtained 3 road feel simulation models into a driving simulator, acquiring running state parameters such as the speed, the lateral acceleration, the yaw angular velocity, the steering wheel angle, the steering wheel angular velocity, the vertical load and the like of a simulated vehicle in real time when a simulated driving test is carried out on the driving simulator, inputting the running state parameters into the road feel simulation models as input variables, determining the data types of the running state parameters according to the correlation degree of the running state parameters and the clustering center coordinates, calculating through the road feel simulation models corresponding to the data types to obtain the steering wheel moment value, and controlling the steering wheel in real time according to the steering wheel moment value, so that more vivid road feel is simulated.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention; also, the above description should be understood as being readily apparent to those skilled in the relevant art and can be implemented, and therefore, other equivalent changes and modifications without departing from the concept disclosed herein are intended to be included within the scope of the present invention.
Claims (10)
1. A road sense simulation method based on K-Means and BP neural networks is characterized by comprising the following steps:
step one, collecting test data: the method comprises the following steps that a driver performs a real vehicle test, a vehicle runs in a test road, and collected test data comprise vehicle speed, vehicle lateral acceleration, vehicle yaw velocity, vehicle vertical load, a driver steering wheel corner, steering wheel angular velocity and steering wheel moment;
step two, processing test data: carrying out normalization processing on the test data after removing abnormal points to obtain a normalized test data set;
step three, carrying out normalized test data classification: clustering the normalized test data by using a K-Means clustering algorithm, dividing the normalized test data into a plurality of data classes after clustering, and dividing the clustered test data into a clustered training data set and a clustered test data set;
step four, training a road feel model based on K-Means and BP neural networks: when a road feel simulation model based on K-Means and BP neural networks is trained by using a clustered training data set and a BP neural network algorithm, the input variables of the BP neural network model are vehicle speed, vehicle lateral acceleration, vehicle yaw velocity, vehicle vertical load, driver steering wheel turning angle and steering wheel angular velocity, the output variables are steering wheel moment, and the same number of road feel simulation models based on K-Means and BP neural networks as data are obtained through training;
step five, testing a road feel model based on K-Means and BP neural networks; testing the road feel simulation model based on the K-Means and BP neural networks by using the clustered test data set, and judging whether to perform the test again;
and sixthly, performing road feel simulation according to the obtained road feel simulation model based on the K-Means and the BP neural network.
2. The road feel simulation method based on K-Means and BP neural networks according to claim 1, characterized in that in the real vehicle test of step one:
the test road types comprise urban roads, expressways, suburban roads and rural roads;
the vehicle running working conditions comprise straight running, backing, turning, pivot steering, uphill and downhill working conditions.
3. The road feel simulation method based on K-Means and BP neural networks according to claim 1, characterized in that in the second step, the removed abnormal points include data points beyond the normal value range, data points with severely deviated distribution and data points with the variation amplitude beyond the normal range.
4. The road feel simulation method based on K-Means and BP neural network according to claim 1, characterized in that in step two, the filter is used to filter the abnormal points of the test data, and the method for deleting the abnormal points by using the filter is: and inputting the test data into a low-pass filter, a high-pass filter or a band-pass filter at one time according to the original acquisition sequence to obtain the filtered test data.
5. The road feel simulation method based on K-Means and BP neural network according to claim 1, characterized in that in step two, the test data is normalized according to the following formula:
wherein i is a data number, j is a variable number, and xi,jDenotes the j variable, X, in the non-normalized i group of datajRepresents the set of variable data values corresponding to all j, min represents the minimum value, and max represents the maximum valueThe value is obtained.
6. The road feel simulation method based on K-Means and BP neural networks as claimed in claim 1, wherein in step two, a certain proportion of data points are randomly selected from the normalized test data set as a training data set, and other data points are selected as a test data set.
7. The road feel simulation method based on K-Means and BP neural network according to any one of claims 1-6, characterized in that in the third step, when clustering is performed on the normalized test data by using K-Means clustering algorithm, the number of clusters is set to 3, 3 cluster center coordinates are obtained after clustering, and the normalized test data is divided into 3 data classes according to the 3 cluster center coordinates.
8. The road sensing simulation method based on K-Means and BP neural network according to any one of claims 1-6, characterized in that, in step four, when training the road sensing simulation model based on K-Means and BP neural network, the BP neural network model has 11 hidden layers, each hidden layer has 20 nodes, 1 input layer and 1 output layer; all the activation functions of all the nodes are Sigmoid functions and are all fully connected; the learning function uses the learngdm function, and the iteration upper limit is 1000 generations.
9. The road feel simulation method based on the K-Means and BP neural network as claimed in claim 1, wherein when testing the road feel simulation model based on the K-Means and BP neural network, the steps are:
1) taking out a test data point in the test data set, and inputting the value of an input variable corresponding to the test data point into a road feel simulation model corresponding to the class to which the test data point belongs to obtain a predicted steering wheel torque value;
2) step 1) is carried out iteratively until all the test data points are predicted by using a road feel simulation model;
3) calculating the MSE value between the predicted steering wheel moment value and the real steering wheel moment value through the model of the test data points of the whole test data set;
4) and if the MSE value is smaller than a preset threshold value alpha, the road feel simulation model based on the data driving obtained by training is considered to be acceptable, and the modeling is successful.
10. The K-Means and BP neural network-based road feel simulation method of claim 9, wherein the threshold α is 0.15.
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| Publication number | Priority date | Publication date | Assignee | Title |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007011976A2 (en) * | 2005-07-19 | 2007-01-25 | Toyota Engineering & Manufacturing North America, Inc. | Crash prediction network with visual input for vehicle |
| CN105083375A (en) * | 2015-09-16 | 2015-11-25 | 丹阳华磊电器有限公司 | Vehicle electric-control steering road-feel control method based on drive-by-wire technology |
| CN105426638A (en) * | 2015-12-24 | 2016-03-23 | 吉林大学 | Driver behavior characteristic identification device |
| CN109466558A (en) * | 2018-10-26 | 2019-03-15 | 重庆邮电大学 | A kind of coefficient of road adhesion estimation method based on EKF and BP neural network |
| CN109591878A (en) * | 2018-11-26 | 2019-04-09 | 吉林大学 | A kind of multi-purpose vehicle(MPV) steering system suitable for upper limb defect driver |
| CN110509983A (en) * | 2019-09-24 | 2019-11-29 | 吉林大学 | A kind of steering-by-wire road feel feedback device suitable for different drive demands |
| CN111581887A (en) * | 2020-05-16 | 2020-08-25 | 郑州轻工业大学 | Unmanned vehicle intelligent training method based on simulation learning in virtual environment |
-
2020
- 2020-12-26 CN CN202011570757.6A patent/CN112528568B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007011976A2 (en) * | 2005-07-19 | 2007-01-25 | Toyota Engineering & Manufacturing North America, Inc. | Crash prediction network with visual input for vehicle |
| CN105083375A (en) * | 2015-09-16 | 2015-11-25 | 丹阳华磊电器有限公司 | Vehicle electric-control steering road-feel control method based on drive-by-wire technology |
| CN105426638A (en) * | 2015-12-24 | 2016-03-23 | 吉林大学 | Driver behavior characteristic identification device |
| CN109466558A (en) * | 2018-10-26 | 2019-03-15 | 重庆邮电大学 | A kind of coefficient of road adhesion estimation method based on EKF and BP neural network |
| CN109591878A (en) * | 2018-11-26 | 2019-04-09 | 吉林大学 | A kind of multi-purpose vehicle(MPV) steering system suitable for upper limb defect driver |
| CN110509983A (en) * | 2019-09-24 | 2019-11-29 | 吉林大学 | A kind of steering-by-wire road feel feedback device suitable for different drive demands |
| CN111581887A (en) * | 2020-05-16 | 2020-08-25 | 郑州轻工业大学 | Unmanned vehicle intelligent training method based on simulation learning in virtual environment |
Non-Patent Citations (3)
| Title |
|---|
| DENIS ASHOK等: "Neural Network Based Approach for the Generation of Road Feel in a Steer-by-Wire System", 《ENGINEERING JOURNAL》 * |
| 朱冰等: "基于双dSPACE的汽车动力学集成控制快速原型试验", 《吉林大学学报(工学版)》 * |
| 罗士军等: "装载机线控转向路感控制策略", 《农业机械学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115098824A (en) * | 2022-06-28 | 2022-09-23 | 东莞灵虎智能科技有限公司 | Ultrasonic sensor sensitivity compensation curve construction method based on BP neural network |
| CN115098824B (en) * | 2022-06-28 | 2024-04-19 | 东莞灵虎智能科技有限公司 | BP neural network-based ultrasonic sensor sensitivity compensation curve construction method |
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