CN111216736A - Driving condition-based self-adaptive adjustment method and system for auxiliary driving system - Google Patents
Driving condition-based self-adaptive adjustment method and system for auxiliary driving system Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0062—Adapting control system settings
- B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0062—Adapting control system settings
- B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
- B60W2050/0095—Automatic control mode change
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2556/00—Input parameters relating to data
- B60W2556/10—Historical data
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Abstract
The invention relates to a driving assistance system self-adaptive adjustment method and system based on driving conditions, which comprises the following steps: acquiring a plurality of historical kinematic short segments; determining a plurality of first characteristic parameters of a plurality of the historical kinematic short segments; processing the first characteristic parameter by adopting a principal component analysis method to obtain a processed characteristic parameter; classifying the processed characteristic parameters by adopting a K-Means clustering algorithm to obtain a classification result; acquiring a current kinematic short segment; determining a second characteristic parameter of the current kinematic short segment; determining the current working condition of the automobile according to the classification result and the second characteristic parameter; and adjusting the auxiliary driving system according to the current working condition of the automobile. The method of the invention carries out self-adaptive adjustment on the auxiliary driving system, solves the problems that the driver does not have enough energy or enough related knowledge to complete the operations of switching the driving mode, adjusting the driving parameters, opening and closing functions and the like, improves the driving safety of the automobile and reduces the operation load of the driver.
Description
Technical Field
The invention relates to the technical field of automobile driving, in particular to a driving assisting system self-adaptive adjusting method and system based on driving conditions.
Background
With the development of the scientific and technical level and the improvement of the living standard of people, the phenomena of high speed driving of automobiles, intensive traffic environment and vehicles, mixed flow of people and vehicles and non-occupation of drivers are increasingly serious, the intellectualization of automobile driving becomes a development trend, the automatic driving technology of automobiles is mature day by day, and a plurality of automobile manufacturers are compliant with the development trend and provide products with intelligent auxiliary driving functions. The intelligent automobile becomes the leading-edge and hot problem of the related research fields of international automobile engineering, even information science and the like, and aims to comprehensively improve the driving safety performance of the automobile, greatly reduce the operation load of a driver and effectively relieve traffic pressure.
As a pillar industry of national economy in China, automobiles are subject to transformation upgrading and leap-over development under the large background that technologies such as mobile interconnection, big data and cloud computing are taken as representatives in the world at present and the traditional manufacturing industry is promoted to be transformed and upgraded to intelligent manufacturing. In more than one hundred years from the birth of the automobile to the present, the automobile gradually develops from an initial simple mechanical structure to an electric and intelligent stage.
With the improvement of the intelligent degree of the automobile, the auxiliary driving function is more and more. However, at the present stage, the selection of the driving mode of the vehicle, the setting of the parameters, the on/off of various functions, and the like often require the driver to manually select and adjust the driving mode, or the driving mode is preset by the vehicle manufacturer when the vehicle manufacturer leaves the factory and cannot be changed. This obviously has the disadvantages of inconvenience and dumb use. On one hand, the driver needs to observe road conditions, communicate with the passengers or listen to music and broadcast and other auxiliary tasks while performing the driving task, and does not have enough energy to complete the switching of the driving mode, the adjustment of driving parameters and the task of opening and closing functions; on the other hand, some drivers only know the driving technique itself, and are not aware of the vehicle operation, and cannot operate the switching of the driving modes, the adjustment of the driving parameters, and the opening and closing of the functions.
Disclosure of Invention
The invention aims to provide a driving assistance system self-adaptive adjustment method and system based on a driving condition, which can carry out self-adaptive adjustment on the driving assistance system according to the current driving condition so as to solve the problem that a driver does not have enough energy or enough related knowledge to complete operations such as driving mode switching, driving parameter adjustment, function switching and the like.
In order to achieve the purpose, the invention provides the following scheme:
a driving assistance system self-adaptive adjustment method based on driving conditions comprises the following steps:
acquiring a plurality of historical kinematic short segments when an automobile is in an acceleration state or a deceleration state in the running process in the past; the historical kinematic short segment is the speed of the automobile at each moment from the idle state to the next idle state;
determining a plurality of first characteristic parameters of a plurality of the historical kinematic short segments; the first characteristic parameters comprise segment time, average speed, maximum acceleration, minimum deceleration, average acceleration, average deceleration, idle time ratio, uniform speed time ratio, acceleration time ratio, deceleration time ratio, speed standard deviation and acceleration standard deviation;
processing the first characteristic parameter by adopting a principal component analysis method to obtain a processed characteristic parameter;
classifying the processed characteristic parameters by adopting a K-Means clustering algorithm to obtain a classification result; the classification result comprises a plurality of working conditions and a plurality of first characteristic parameters under each working condition;
acquiring a current kinematic short segment in the current automobile driving process;
determining a second characteristic parameter of the current kinematic short segment; the second characteristic parameters comprise current segment time, current average speed, current maximum acceleration, current minimum deceleration, current average acceleration, current average deceleration, current idle-slow time ratio, current uniform speed time ratio, current acceleration time ratio, current deceleration time ratio, current speed standard deviation and current acceleration standard deviation;
determining the current working condition of the automobile according to the classification result and the second characteristic parameter;
and adjusting the auxiliary driving system according to the current working condition of the automobile.
Optionally, the determining a plurality of first feature parameters of a plurality of the historical kinematic short segments specifically includes:
using a formula
Determining the speed standard deviation; wherein S isvRepresents the standard deviation of velocity, i-1, 2,3.. N, N represents the number of data in the historical kinematic short segment, v represents the number of data in the historical kinematic short segmentiIndicates the vehicle speed at the i-th time, vaThe average speed is indicated.
Optionally, the determining a plurality of first feature parameters of a plurality of the historical kinematic short segments further includes:
using a formula
Determining the acceleration standard deviation; wherein S isaRepresents the standard deviation of acceleration, i is 1,2,3iRepresents the acceleration at the ith time, aaThe average acceleration is indicated.
Optionally, the processing the first characteristic parameter by using a principal component analysis method to obtain a processed characteristic parameter specifically includes:
carrying out standardization processing on the first characteristic parameters to obtain a standardized matrix;
determining a correlation coefficient matrix of the standardized matrix according to the standardized matrix;
determining an eigenvector and an eigenvalue of a correlation coefficient matrix according to the correlation coefficient matrix;
sorting the eigenvalues according to the numerical values to obtain sorted eigenvalues;
determining the contribution rate and the accumulated contribution rate of each principal component in the first characteristic parameter according to the sorted characteristic values;
selecting principal components to be analyzed according to the accumulated contribution rate;
and determining the processed characteristic parameters according to the principal component to be analyzed and the characteristic vector.
Optionally, determining the current working condition of the automobile according to the classification result and the second characteristic parameter specifically includes:
calculating the mean value of the first characteristic parameters under each working condition in the classification result to obtain mean value data corresponding to each working condition;
judging whether the second characteristic parameter is smaller than a preset characteristic parameter threshold value or not, and obtaining a first judgment result;
if the first judgment result is that the second characteristic parameter is smaller than a preset characteristic parameter threshold value, selecting a working condition corresponding to the mean value data closest to the second characteristic parameter as the current working condition of the automobile;
and if the judgment result is that the second characteristic parameter is greater than or equal to a preset characteristic parameter threshold value, returning to the step of acquiring the plurality of historical kinematic short segments in the acceleration state or the deceleration state in the previous automobile driving process.
A driving assistance system self-adaptive adjustment system based on driving conditions comprises:
the historical kinematic short segment acquisition module is used for acquiring a plurality of historical kinematic short segments when the automobile is in an acceleration state or a deceleration state in the running process in the past; the historical kinematic short segment is the speed of the automobile at each moment from the idle state to the next idle state;
a first feature parameter determination module for determining a plurality of first feature parameters of a plurality of the historical kinematic short segments; the first characteristic parameters comprise segment time, average speed, maximum acceleration, minimum deceleration, average acceleration, average deceleration, idle time ratio, uniform speed time ratio, acceleration time ratio, deceleration time ratio, speed standard deviation and acceleration standard deviation;
the processed characteristic parameter acquisition module is used for processing the first characteristic parameter by adopting a principal component analysis method to obtain a processed characteristic parameter;
the classification result determining module is used for classifying the processed characteristic parameters by adopting a K-Means clustering algorithm to obtain a classification result; the classification result comprises a plurality of working conditions and a plurality of first characteristic parameters under each working condition;
the current kinematics short segment acquisition module is used for acquiring a current kinematics short segment in the current automobile driving process;
a second feature parameter determination module, configured to determine a second feature parameter of the current kinematic short segment; the second characteristic parameters comprise current segment time, current average speed, current maximum acceleration, current minimum deceleration, current average acceleration, current average deceleration, current idle-slow time ratio, current uniform speed time ratio, current acceleration time ratio, current deceleration time ratio, current speed standard deviation and current acceleration standard deviation;
the current working condition determining module of the automobile is used for determining the current working condition of the automobile according to the classification result and the second characteristic parameter;
and the auxiliary driving system adjusting module is used for adjusting the auxiliary driving system according to the current working condition of the automobile.
Optionally, the first characteristic parameter determining module specifically includes:
a speed standard deviation determining unit for employing a formula
Determining the speed standard deviation; wherein S isvRepresents the standard deviation of velocity, i-1, 2,3.. N, N represents the number of data in the historical kinematic short segment, v represents the number of data in the historical kinematic short segmentiIndicates the vehicle speed at the i-th time, vaThe average speed is indicated.
Optionally, the first characteristic parameter determining module further includes:
an acceleration standard deviation determining unit for employing a formula
Determining the acceleration standard deviation; wherein S isaRepresents the standard deviation of acceleration, i is 1,2,3iIndicating the ith timeAcceleration, aaThe average acceleration is indicated.
Optionally, the processed feature parameter obtaining module specifically includes:
the standardized matrix determining unit is used for carrying out standardized processing on the first characteristic parameter to obtain a standardized matrix;
a correlation coefficient matrix determining unit configured to determine a correlation coefficient matrix of the normalized matrix according to the normalized matrix;
the characteristic vector and characteristic value determining unit is used for determining the characteristic vector and the characteristic value of the correlation coefficient matrix according to the correlation coefficient matrix;
the sorted eigenvalue determining unit is used for sorting the eigenvalues according to the numerical value to obtain sorted eigenvalues;
the contribution rate determining unit is used for determining the contribution rate and the accumulated contribution rate of each principal component in the first characteristic parameter according to the sorted characteristic values;
the principal component determining unit is used for selecting principal components needing to be analyzed according to the accumulated contribution rate;
and the processed characteristic parameter determining unit is used for determining the processed characteristic parameters according to the principal component needing to be analyzed and the characteristic vector.
Optionally, the module for determining the current operating condition of the vehicle specifically includes:
the mean value data determining unit is used for calculating the mean value of the first characteristic parameters under each working condition in the classification result to obtain mean value data corresponding to each working condition;
the first judgment result acquisition unit is used for judging whether the second characteristic parameter is smaller than a preset characteristic parameter threshold value or not to acquire a first judgment result;
the current working condition determining unit of the automobile is used for selecting the working condition corresponding to the mean value data closest to the second characteristic parameter as the current working condition of the automobile if the first judgment result shows that the second characteristic parameter is smaller than a preset characteristic parameter threshold value;
and the returning unit is used for returning to the step of acquiring the plurality of historical kinematic short segments in the acceleration state or the deceleration state in the past automobile driving process if the judgment result is that the second characteristic parameter is greater than or equal to the preset characteristic parameter threshold value.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to a driving assistance system self-adaptive adjustment method and system based on driving conditions, which comprises the following steps: acquiring a plurality of historical kinematic short segments; determining a plurality of first characteristic parameters of a plurality of the historical kinematic short segments; processing the first characteristic parameter by adopting a principal component analysis method to obtain a processed characteristic parameter; classifying the processed characteristic parameters by adopting a K-Means clustering algorithm to obtain a classification result; acquiring a current kinematic short segment; determining a second characteristic parameter of the current kinematic short segment; determining the current working condition of the automobile according to the classification result and the second characteristic parameter; and adjusting the auxiliary driving system according to the current working condition of the automobile. The method of the invention carries out self-adaptive adjustment on the auxiliary driving system, solves the problems that the driver does not have enough energy or enough related knowledge to complete the operations of switching the driving mode, adjusting the driving parameters, opening and closing functions and the like, improves the driving safety of the automobile and reduces the operation load of the driver.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a driving assistance system adaptive adjustment method based on driving conditions according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an adaptive adjustment system of an assistant driving system based on a driving condition according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a driving assistance system self-adaptive adjustment method and system based on a driving condition, which can carry out self-adaptive adjustment on the driving assistance system according to the current driving condition so as to solve the problem that a driver does not have enough energy or enough related knowledge to complete operations such as driving mode switching, driving parameter adjustment, function switching and the like.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of an adaptive adjustment method for an assistant driving system based on a driving condition according to an embodiment of the present invention, and as shown in fig. 1, the adaptive adjustment method for an assistant driving system according to the present invention includes:
s1: acquiring a plurality of historical kinematic short segments when an automobile is in an acceleration state or a deceleration state in the running process in the past; the historical kinematic short segment is the speed of the automobile at each moment from the idle state to the next idle state.
Specifically, during the running process of the automobile, the running data is recorded by the computer, and includes, but is not limited to, derivative data such as speed, acceleration, mean value and extreme value thereof, i.e. a historical kinematic short segment is obtained, where the historical kinematic short segment refers to the speed of the automobile at each time between the start of the idle state of the automobile and the start of the next idle state of the automobile (the start point of the idle segment is selected as the start point of the historical kinematic short segment, and the start point of the next idle segment is selected as the end point of the kinematic short segment).
S2: determining a plurality of first characteristic parameters of a plurality of the historical kinematic short segments; the first characteristic parameters comprise segment time, average speed, maximum acceleration, minimum deceleration, average acceleration, average deceleration, idle time ratio, uniform speed time ratio, acceleration time ratio, deceleration time ratio, speed standard deviation and acceleration standard deviation.
Specifically, among the driving parameters of the automobile, the speed and the acceleration are the most obvious and easily obtained parameters, but the two parameters are obviously not comprehensive enough, so that much driving information is lost, and the first characteristic parameter is selected for reasonably evaluating the movement characteristics of the automobile and is shown in table 1.
TABLE 1
The above characteristic parameters are calculated as follows:
1. segment time T
T＝N (1.1)
Where N represents the number of data in the historical kinematic short segment, the sampling frequency is set to 1Hz, i.e. one data per second, so the number of data within a short segment represents the duration of the short segment.
2. Average velocity va
N, where i ═ 1,2,3.. N, N denotes the number of data in the historical kinematic short segment, v ═ v ·iIndicates the vehicle speed at the i-th time, vaThe average speed is indicated.
3. Maximum velocity vmax
vmax＝max{vi},i＝1,2,3,...,N (1.3)
4. Acceleration ai
Before calculating the maximum, average and standard deviation of the acceleration and deceleration, the acceleration of the vehicle needs to be calculated:
wherein, ai,i-1Is expressed as acceleration between the ith second and the ith-1 st second; v. ofiAnd vi-1And vehicle speeds representing the ith and (i-1) th seconds, respectively; t is tiAnd ti-1Respectively representing the time of the ith second and the ith-1 second.
5. Maximum acceleration amax
amax＝max{ai},i＝1,2,3,...,N (1.5)
6. Minimum deceleration amin
amin＝min{ai},i＝1,2,3,...,N (1.6)
7. Average acceleration aa
Wherein, ai1And Ti1Is 0.15m/s during the running of the vehicle2The above acceleration values and the duration of the acceleration.
8. Average deceleration ad
Wherein, ai2And Ti2Is set at-0.15 m/s during the running process of the vehicle2The above deceleration value and the duration of deceleration.
9. Idle time ratio Ri
Wherein, TiThe speed is less than 1km/h and the absolute value of the acceleration or deceleration is within the range of0.15m/s2The time of day (c).
10. Uniform time ratio Rc
Rc＝1-Ri-Ra-Rd(1.10)
11. Acceleration time ratio Ra
Wherein, TaThe acceleration exceeds 0.15m/s in the running process of the vehicle2Acceleration running time of (1).
12. Deceleration time ratio Rd
Wherein, TdFor deceleration exceeding 0.15m/s during vehicle travel2The deceleration running time of (1).
13. Standard deviation of speed Sv
Determining the speed standard deviation by using a formula (1.13); wherein S isvRepresents the standard deviation of velocity, i-1, 2,3.. N, N represents the number of data in the historical kinematic short segment, v represents the number of data in the historical kinematic short segmentiIndicates the vehicle speed at the i-th time, vaThe average speed is indicated.
14. Standard deviation of acceleration Sa
Determining the acceleration standard deviation by adopting a formula (1.14); wherein S isaRepresents the standard deviation of acceleration, i is 1,2,3iRepresents the acceleration at the ith time, aaThe average acceleration is indicated.
S3: and processing the first characteristic parameter by adopting a principal component analysis method to obtain a processed characteristic parameter.
And (3) reducing the dimension of the data by using a principal component analysis method, wherein the principal component analysis method is a mathematical dimension reduction method, and in the data processing process, the variables with correlation are extracted, the original large variable quantity is replaced by a small variable quantity, and the information reflected by the original data (first characteristic parameter) is kept as much as possible.
The principal component analysis method is to recombine variables with certain correlation in the original data (first characteristic parameters) so as to obtain a group of variables which are unrelated in pairs to replace the original data (first characteristic parameters). Defining the new variable with the largest variance in all linear combinations as a first principal component M1, if the first principal component is not enough to completely represent information contained in original data (first characteristic parameters), selecting the new variable with the largest variance in the other linear combinations as a second principal component M2, and so on, and determining the number of the required principal components when the cumulative contribution rate of the selected principal components reaches more than 85%. The detailed steps of principal component analysis include:
1. and normalizing the data, namely normalizing the first characteristic parameters to obtain a normalized matrix.
Since the units of the first characteristic parameters are different, the principal component analysis method directly performed on the data can make the result have larger discreteness, obviously has an influence on the result, and in order to eliminate the influence, the first characteristic parameters are normalized and converted into a normalization matrix, so that the mean value of each column is 0, and the variance is l.
Let X be the first feature parameter matrix, where p is the number of historical kinematic short segments, and n is the number of feature parameters (n ═ 13):
carrying out standardization processing on the matrix X to obtain a standardized matrix Y, wherein the standardization process is as follows:
wherein r is 1,2,3.. p, j is 1,2,3.. n.
2. And calculating a correlation coefficient, and determining a correlation coefficient matrix of the standardized matrix according to the standardized matrix.
The correlation coefficient is calculated as follows:
the corresponding correlation coefficient matrix is:
3. determining an eigenvector and an eigenvalue of a correlation coefficient matrix according to the correlation coefficient matrix; sorting the eigenvalues according to the numerical values to obtain sorted eigenvalues; that is, the eigenvalue λ of the correlation coefficient matrix is obtained, and the eigenvalues are arranged as λ in the order from large to small1>λ2>λ3>……>λnAnd finding out ξ as characteristic vectors corresponding to the characteristic valuesr、ξrj，ξrjRepresenting feature vector ξrThe jth component of (a).
4. Calculating the contribution rate of the principal component, and determining the contribution rate and the accumulated contribution rate of each principal component in the first characteristic parameter according to the sorted characteristic values; and selecting the principal components to be analyzed according to the accumulated contribution rate.
The contribution ratio calculation formula of the jth principal component is as follows:
and calculating the contribution rate of each principal component in sequence, considering that the information of the original data, namely the first characteristic parameter is basically reflected when the cumulative contribution rate of the current m principal components exceeds 85%, and selecting the principal components, wherein each principal component refers to the first characteristic parameter.
And determining the processed characteristic parameters according to the principal component to be analyzed and the characteristic vector.
5. Calculating a principal component load matrix Z, wherein the calculation formula is as follows:
wherein j is 1,2,3.. n, and q is 1,2,3.. m.
6. Multiplying the normalized characteristic parameter matrix with the principal component load matrix to obtain a principal component score matrix, namely the processed characteristic parameters;
s4: classifying the processed characteristic parameters by adopting a K-Means clustering algorithm to obtain a classification result; the classification result comprises a plurality of working conditions and a plurality of first characteristic parameters under each working condition.
The method comprises the steps of obtaining the number of types, usually 2-4 types, to be separated from historical kinematic short segments by adopting a K-Means clustering algorithm, defining the quickest, unobstructed and slowest running as smooth working conditions, defining the congested working conditions as congested working conditions, and defining a proper intermediate transition working condition between the smooth and congested working conditions according to the difference of the classified numbers (if the clustering result shows that the classification into two types is most reasonable, the intermediate working condition does not need to be defined).
The clustering algorithm is as follows:
1. setting a clustering number K, generally 2 types, setting 3 types for vehicles with motion modes, and randomly setting K clustering centers;
2. calculating the Euclidean distance between each kinematic short segment and the centroids, and dividing the short segments into the closest class:
wherein x isikThe k variable representing the ith fragment, each fragment having p variables (determined by principal component analysis), yikRepresents the selected cluster center (cluster center is a randomly selected set of data in the short fragment set, also containing p variables).
3. Determining the center position of each type through calculation, and determining the position as a new clustering center;
4. and repeating the operation of 2-3 steps according to the new clustering center until the clustering center is not changed any more, which indicates that the clustering result is stable.
S5: and acquiring the current short kinematic segment in the current automobile driving process.
S6: determining a second characteristic parameter of the current kinematic short segment; the second characteristic parameters comprise current segment time, current average speed, current maximum acceleration, current minimum deceleration, current average acceleration, current average deceleration, current idle-slow time ratio, current uniform speed-time ratio, current acceleration-time ratio, current deceleration-time ratio, current speed standard deviation and current acceleration standard deviation.
S7: and determining the current working condition of the automobile according to the classification result and the second characteristic parameter.
S7 specifically includes:
and calculating the mean value of the first characteristic parameters under each working condition in the classification result to obtain mean value data corresponding to each working condition.
Specifically, the mean value of the segment time in the historical short kinematic segments under each type of working condition is calculated, that is, the sum of the segment time of all the short kinematic segments is divided by the number of all the short kinematic segments, and the mean value is solved by other parameters in the first characteristic parameter.
Judging whether the second characteristic parameter is smaller than a preset characteristic parameter threshold value or not, and obtaining a first judgment result;
and if the first judgment result shows that the second characteristic parameter is smaller than a preset characteristic parameter threshold value, selecting a working condition corresponding to the mean value data closest to the second characteristic parameter as the current working condition of the automobile.
Specifically, judging that the current segment time in the second characteristic parameter is closest to the segment time average value under which working condition, judging that the current average speed in the second characteristic parameter is closest to the average speed average value under which working condition, finishing judging 13 characteristics, and selecting the working condition corresponding to the average value data closest to the second characteristic parameter as the current working condition of the automobile.
And if the judgment result is that the second characteristic parameter is greater than or equal to a preset characteristic parameter threshold value, returning to the step of acquiring the plurality of historical kinematic short segments in the acceleration state or the deceleration state in the previous automobile driving process. And then the current kinematic short segment generated by the current driving is put into a database, and the calculation is carried out according to the steps to directly obtain the working condition type of the current kinematic short segment.
S8: and adjusting the auxiliary driving system according to the current working condition of the automobile.
Setting of standard mode: and each parameter maintains the default setting of the vehicle leaving the factory, and the driving requirements under most working conditions are met.
Setting of low speed/economy mode: compared with the standard mode, on the gear shifting rule, the gear-up time is advanced, the gear-down time is delayed, and the stability of the vehicle is improved; on the braking system, the sensitivity of a brake pedal is increased by adjusting the clearance of the braking system, so that the reliability of the braking system is ensured; on a steering system, the steering sensitivity is increased by adjusting the angular transmission ratio of the steering system, so that the steering system is convenient to deal with complex working conditions; in the setting of the intelligent auxiliary system, various safety functions are reduced, including but not limited to safety distance thresholds in functions of an automatic emergency braking system (AEB), a front collision early warning system (FCW), a lane departure early warning system (FDW), a pedestrian collision early warning system (PCW) and the like, so that the safety of the vehicle is ensured.
Setting of high speed/sport mode (partly with the vehicle in need): compared with the standard mode, on the gear shifting rule, the gear-up time is delayed, the gear-down time is advanced, and the dynamic property of the vehicle is improved; on the power system, the sensitivity of an accelerator pedal is increased by adjusting a throttle control strategy, so that the response of power is more obvious; on a steering system, the steering sensitivity is properly reduced by adjusting the angular transmission ratio of the steering system, and safety accidents such as large steering, sideslip and the like of a vehicle in high-speed running are avoided; on the setting of the intelligent auxiliary system, the safety distance threshold of each function can be properly increased, and excessive useless prompts are avoided on the basis of ensuring the safety of the vehicle.
Fig. 2 is a schematic structural diagram of an adaptive adjustment system of an assistant driving system based on a driving condition according to an embodiment of the present invention, and as shown in fig. 2, the present invention further provides an adaptive adjustment system of an assistant driving system based on a driving condition, where the adaptive adjustment system of an assistant driving system includes:
a historical kinematic short segment obtaining module 201, configured to obtain a plurality of historical kinematic short segments when an automobile is in an acceleration state or a deceleration state in a past driving process; the historical kinematic short segment is the speed of the automobile at each moment from the idle state to the next idle state;
a first feature parameter determination module 202, configured to determine a plurality of first feature parameters of a plurality of the historical kinematic short segments; the first characteristic parameters comprise segment time, average speed, maximum acceleration, minimum deceleration, average acceleration, average deceleration, idle time ratio, uniform speed time ratio, acceleration time ratio, deceleration time ratio, speed standard deviation and acceleration standard deviation;
a processed feature parameter obtaining module 203, configured to process the first feature parameter by using a principal component analysis method to obtain a processed feature parameter;
a classification result determining module 204, configured to classify the processed feature parameters by using a K-Means clustering algorithm to obtain a classification result; the classification result comprises a plurality of working conditions and a plurality of first characteristic parameters under each working condition;
a current kinematics short segment obtaining module 205, configured to obtain a current kinematics short segment in a current vehicle driving process;
a second feature parameter determination module 206, configured to determine a second feature parameter of the current kinematic short segment; the second characteristic parameters comprise current segment time, current average speed, current maximum acceleration, current minimum deceleration, current average acceleration, current average deceleration, current idle-slow time ratio, current uniform speed time ratio, current acceleration time ratio, current deceleration time ratio, current speed standard deviation and current acceleration standard deviation;
the current working condition determining module 207 is used for determining the current working condition of the automobile according to the classification result and the second characteristic parameter;
and the driving assistance system adjusting module 208 is used for adjusting the driving assistance system according to the current working condition of the automobile.
Preferably, the first characteristic parameter determining module 202 specifically includes:
a speed standard deviation determining unit for employing a formula
Determining the speed standard deviation; wherein S isvRepresents the standard deviation of velocity, i-1, 2,3.. N, N represents the number of data in the historical kinematic short segment, v represents the number of data in the historical kinematic short segmentiIndicates the vehicle speed at the i-th time, vaThe average speed is indicated.
Preferably, the first characteristic parameter determining module 202 further includes:
an acceleration standard deviation determination unit for adoptingFormula (II)
Determining the acceleration standard deviation; wherein S isaRepresents the standard deviation of acceleration, i is 1,2,3iRepresents the acceleration at the ith time, aaThe average acceleration is indicated.
Preferably, the processed feature parameter obtaining module 203 specifically includes:
the standardized matrix determining unit is used for carrying out standardized processing on the first characteristic parameter to obtain a standardized matrix;
a correlation coefficient matrix determining unit configured to determine a correlation coefficient matrix of the normalized matrix according to the normalized matrix;
the characteristic vector and characteristic value determining unit is used for determining the characteristic vector and the characteristic value of the correlation coefficient matrix according to the correlation coefficient matrix;
the sorted eigenvalue determining unit is used for sorting the eigenvalues according to the numerical value to obtain sorted eigenvalues;
the contribution rate determining unit is used for determining the contribution rate and the accumulated contribution rate of each principal component in the first characteristic parameter according to the sorted characteristic values;
the principal component determining unit is used for selecting principal components needing to be analyzed according to the accumulated contribution rate;
and the processed characteristic parameter determining unit is used for determining the processed characteristic parameters according to the principal component needing to be analyzed and the characteristic vector.
Preferably, the module 207 for determining the current operating condition of the vehicle specifically includes:
the mean value data determining unit is used for calculating the mean value of the first characteristic parameters under each working condition in the classification result to obtain mean value data corresponding to each working condition;
the first judgment result acquisition unit is used for judging whether the second characteristic parameter is smaller than a preset characteristic parameter threshold value or not to acquire a first judgment result;
the current working condition determining unit of the automobile is used for selecting the working condition corresponding to the mean value data closest to the second characteristic parameter as the current working condition of the automobile if the first judgment result shows that the second characteristic parameter is smaller than a preset characteristic parameter threshold value;
and the returning unit is used for returning to the step of acquiring the plurality of historical kinematic short segments in the acceleration state or the deceleration state in the past automobile driving process if the judgment result is that the second characteristic parameter is greater than or equal to the preset characteristic parameter threshold value.
In order to improve the limitation of an intelligent assistant driving function on mode and parameter setting, the invention provides an assistant driving system self-adaptive adjustment method and system based on driving conditions. Namely, the running parameters such as the opening and closing of each function of the intelligent auxiliary driving and the selection of the vehicle running mode are adjusted.
When the automobile is started for the first time every day, the data processing is automatically carried out, and after the automobile runs for more than a certain time (such as 45 days or 360 hours), expired data before a set time limit can be automatically deleted before the data processing is carried out, so that the timeliness of the judgment etalon is ensured; in the running process of the automobile, a running computer converts current running data into kinematic short segments in real time, calculates parameters such as speed, acceleration and the like required by classification, finds out segments most similar to the current kinematic short segments from a historical kinematic short segment library, and judges the current running condition of the automobile according to the category of the current kinematic short segments; the driver can also calculate again when idling, and the kinematic short segment generated by the current running is added into the database, and clustering calculation is carried out again so as to obtain the most accurate class judgment of the current running condition.
According to the current working condition of the automobile, the invention adjusts the mode and parameters of the automobile movement: the automobile under the congestion working condition can be set to be in a low-speed or economic mode, the auxiliary driving function is completely started, the threshold values of all parameters of the auxiliary driving function are properly reduced, collision is avoided, and safety is ensured; the automobile in a smoother working condition can be set to be in a standard mode; if necessary, the automobile in the fluent working condition can be set to a high-speed/motion mode, the dynamic property of the automobile is enhanced, and the threshold values of all parameters of the auxiliary driving function are properly increased.
The embodiments in the present description 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. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A driving assistance system self-adaptive adjustment method based on driving conditions is characterized by comprising the following steps:
acquiring a plurality of historical kinematic short segments when an automobile is in an acceleration state or a deceleration state in the running process in the past; the historical kinematic short segment is the speed of the automobile at each moment from the idle state to the next idle state;
determining a plurality of first characteristic parameters of a plurality of the historical kinematic short segments; the first characteristic parameters comprise segment time, average speed, maximum acceleration, minimum deceleration, average acceleration, average deceleration, idle time ratio, uniform speed time ratio, acceleration time ratio, deceleration time ratio, speed standard deviation and acceleration standard deviation;
processing the first characteristic parameter by adopting a principal component analysis method to obtain a processed characteristic parameter;
classifying the processed characteristic parameters by adopting a K-Means clustering algorithm to obtain a classification result; the classification result comprises a plurality of working conditions and a plurality of first characteristic parameters under each working condition;
acquiring a current kinematic short segment in the current automobile driving process;
determining a second characteristic parameter of the current kinematic short segment; the second characteristic parameters comprise current segment time, current average speed, current maximum acceleration, current minimum deceleration, current average acceleration, current average deceleration, current idle-slow time ratio, current uniform speed time ratio, current acceleration time ratio, current deceleration time ratio, current speed standard deviation and current acceleration standard deviation;
determining the current working condition of the automobile according to the classification result and the second characteristic parameter;
and adjusting the auxiliary driving system according to the current working condition of the automobile.
2. The adaptive adjustment method for the driving assistance system based on the driving condition as claimed in claim 1, wherein the determining a plurality of first characteristic parameters of a plurality of the historical kinematic short segments specifically comprises:
using a formula
Determining the speed standard deviation; wherein S isvRepresents the standard deviation of velocity, i-1, 2,3.. N, N represents the number of data in the historical kinematic short segment, v represents the number of data in the historical kinematic short segmentiIndicates the vehicle speed at the i-th time, vaThe average speed is indicated.
3. The adaptive adjustment method for a driving assistance system according to claim 1, wherein the determining a plurality of first characteristic parameters of a plurality of the historical kinematic short segments further comprises:
using a formula
Determining the acceleration standard deviation; wherein S isaRepresents the standard deviation of acceleration, i is 1,2,3iRepresents the acceleration at the ith time, aaThe average acceleration is indicated.
4. The driving assistance system adaptive adjustment method based on the driving condition as claimed in claim 1, wherein the processing the first characteristic parameter by using a principal component analysis method to obtain a processed characteristic parameter specifically comprises:
carrying out standardization processing on the first characteristic parameters to obtain a standardized matrix;
determining a correlation coefficient matrix of the standardized matrix according to the standardized matrix;
determining an eigenvector and an eigenvalue of a correlation coefficient matrix according to the correlation coefficient matrix;
sorting the eigenvalues according to the numerical values to obtain sorted eigenvalues;
determining the contribution rate and the accumulated contribution rate of each principal component in the first characteristic parameter according to the sorted characteristic values;
selecting principal components to be analyzed according to the accumulated contribution rate;
and determining the processed characteristic parameters according to the principal component to be analyzed and the characteristic vector.
5. The driving assistance system adaptive adjustment method based on the driving condition as claimed in claim 1, wherein the determining the current condition of the vehicle according to the classification result and the second characteristic parameter specifically comprises:
calculating the mean value of the first characteristic parameters under each working condition in the classification result to obtain mean value data corresponding to each working condition;
judging whether the second characteristic parameter is smaller than a preset characteristic parameter threshold value or not, and obtaining a first judgment result;
if the first judgment result is that the second characteristic parameter is smaller than a preset characteristic parameter threshold value, selecting a working condition corresponding to the mean value data closest to the second characteristic parameter as the current working condition of the automobile;
and if the judgment result is that the second characteristic parameter is greater than or equal to a preset characteristic parameter threshold value, returning to the step of acquiring the plurality of historical kinematic short segments in the acceleration state or the deceleration state in the previous automobile driving process.
6. The self-adaptive adjusting system of the assistant driving system based on the driving condition is characterized by comprising the following components:
the historical kinematic short segment acquisition module is used for acquiring a plurality of historical kinematic short segments when the automobile is in an acceleration state or a deceleration state in the running process in the past; the historical kinematic short segment is the speed of the automobile at each moment from the idle state to the next idle state;
a first feature parameter determination module for determining a plurality of first feature parameters of a plurality of the historical kinematic short segments; the first characteristic parameters comprise segment time, average speed, maximum acceleration, minimum deceleration, average acceleration, average deceleration, idle time ratio, uniform speed time ratio, acceleration time ratio, deceleration time ratio, speed standard deviation and acceleration standard deviation;
the processed characteristic parameter acquisition module is used for processing the first characteristic parameter by adopting a principal component analysis method to obtain a processed characteristic parameter;
the classification result determining module is used for classifying the processed characteristic parameters by adopting a K-Means clustering algorithm to obtain a classification result; the classification result comprises a plurality of working conditions and a plurality of first characteristic parameters under each working condition;
the current kinematics short segment acquisition module is used for acquiring a current kinematics short segment in the current automobile driving process;
a second feature parameter determination module, configured to determine a second feature parameter of the current kinematic short segment; the second characteristic parameters comprise current segment time, current average speed, current maximum acceleration, current minimum deceleration, current average acceleration, current average deceleration, current idle-slow time ratio, current uniform speed time ratio, current acceleration time ratio, current deceleration time ratio, current speed standard deviation and current acceleration standard deviation;
the current working condition determining module of the automobile is used for determining the current working condition of the automobile according to the classification result and the second characteristic parameter;
and the auxiliary driving system adjusting module is used for adjusting the auxiliary driving system according to the current working condition of the automobile.
7. The driving assistance system adaptive adjustment system based on the driving condition as claimed in claim 6, wherein the first characteristic parameter determination module specifically comprises:
a speed standard deviation determining unit for employing a formula
Determining the speed standard deviation; wherein S isvRepresents the standard deviation of velocity, i-1, 2,3.. N, N represents the number of data in the historical kinematic short segment, v represents the number of data in the historical kinematic short segmentiIndicates the vehicle speed at the i-th time, vaThe average speed is indicated.
8. The adaptive adjustment system for the driving assistance system according to claim 6, wherein the first characteristic parameter determination module further comprises:
an acceleration standard deviation determining unit for employing a formula
Determining the acceleration criterionA difference; wherein S isaRepresents the standard deviation of acceleration, i is 1,2,3iRepresents the acceleration at the ith time, aaThe average acceleration is indicated.
9. The driving assistance system adaptive adjustment system based on the driving condition of claim 6, wherein the processed characteristic parameter obtaining module specifically comprises:
the standardized matrix determining unit is used for carrying out standardized processing on the first characteristic parameter to obtain a standardized matrix;
a correlation coefficient matrix determining unit configured to determine a correlation coefficient matrix of the normalized matrix according to the normalized matrix;
the characteristic vector and characteristic value determining unit is used for determining the characteristic vector and the characteristic value of the correlation coefficient matrix according to the correlation coefficient matrix;
the sorted eigenvalue determining unit is used for sorting the eigenvalues according to the numerical value to obtain sorted eigenvalues;
the contribution rate determining unit is used for determining the contribution rate and the accumulated contribution rate of each principal component in the first characteristic parameter according to the sorted characteristic values;
the principal component determining unit is used for selecting principal components needing to be analyzed according to the accumulated contribution rate;
and the processed characteristic parameter determining unit is used for determining the processed characteristic parameters according to the principal component needing to be analyzed and the characteristic vector.
10. The driving assistance system adaptive adjustment system based on the driving condition as claimed in claim 6, wherein the current condition determining module of the vehicle specifically comprises:
the mean value data determining unit is used for calculating the mean value of the first characteristic parameters under each working condition in the classification result to obtain mean value data corresponding to each working condition;
the first judgment result acquisition unit is used for judging whether the second characteristic parameter is smaller than a preset characteristic parameter threshold value or not to acquire a first judgment result;
the current working condition determining unit of the automobile is used for selecting the working condition corresponding to the mean value data closest to the second characteristic parameter as the current working condition of the automobile if the first judgment result shows that the second characteristic parameter is smaller than a preset characteristic parameter threshold value;
and the returning unit is used for returning to the step of acquiring the plurality of historical kinematic short segments in the acceleration state or the deceleration state in the past automobile driving process if the judgment result is that the second characteristic parameter is greater than or equal to the preset characteristic parameter threshold value.
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