CN113060146A - Longitudinal tracking control method, device, equipment and storage medium - Google Patents

Longitudinal tracking control method, device, equipment and storage medium Download PDF

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Publication number
CN113060146A
CN113060146A CN202110519112.8A CN202110519112A CN113060146A CN 113060146 A CN113060146 A CN 113060146A CN 202110519112 A CN202110519112 A CN 202110519112A CN 113060146 A CN113060146 A CN 113060146A
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vehicle
driving data
determining
style
acceleration
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CN113060146B (en
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李伟男
刘斌
吴杭哲
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FAW Group Corp
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FAW Group Corp
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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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • 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
    • B60W2530/00—Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
    • B60W2530/18—Distance travelled
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/802—Longitudinal distance

Abstract

The invention discloses a longitudinal tracking control method, a longitudinal tracking control device, longitudinal tracking control equipment and a storage medium. The method comprises the following steps: acquiring current driving data; determining a driving style according to the current driving data; the current state of the vehicle and the expected acceleration of the vehicle are determined according to the driving style and the current driving data, and through the technical scheme of the invention, the differentiated requirements of drivers with different habit characteristics can be met, the satisfaction degree and the comfort degree of the drivers are greatly improved, better driving experience is provided for the drivers, and the method has great significance for improving the system applicability, ensuring the vehicle safety and reducing traffic accidents.

Description

Longitudinal tracking control method, device, equipment and storage medium
Technical Field
The embodiment of the invention relates to the technical field of vehicles, in particular to a longitudinal tracking control method, a longitudinal tracking control device, longitudinal tracking control equipment and a storage medium.
Background
Automotive longitudinal motion control systems are typically designed in a layered structure: the upper layer controller outputs expected acceleration according to the relative vehicle distance and the vehicle speed, and the problems of driver characteristics, queue stability, traffic flow and the like are mainly considered during design; the lower-layer acceleration tracking controller enables the actual acceleration of the automobile to track an expected value through controlling an actuating mechanism, and the vehicle dynamics problem is mainly considered during design. The automobile longitudinal acceleration tracking control is one of the key technologies of the automobile longitudinal motion control.
In recent years, with the development of smart cars, research on the driving skills and the driving style of drivers has been advanced, and more researchers have been devoted to the recognition of the driving skills of drivers. Considering the factors of loss, energy waste, danger and the like of a real vehicle, a method for longitudinal tracking control based on a driving style needs to be researched and developed urgently.
Disclosure of Invention
Embodiments of the present invention provide a longitudinal tracking control method, apparatus, device, and storage medium, so as to meet differentiated requirements of drivers with different habitual characteristics, greatly improve satisfaction and comfort of the drivers, provide better driving experience for the drivers, and have great significance in improving system applicability, ensuring automobile safety, and reducing traffic accidents.
In a first aspect, an embodiment of the present invention provides a longitudinal tracking control method, including:
acquiring current driving data;
determining a driving style according to the current driving data;
and determining the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data.
In a second aspect, an embodiment of the present invention further provides a longitudinal tracking control apparatus, where the apparatus includes:
the acquisition module is used for acquiring current driving data;
the first determining module is used for determining the driving style according to the current driving data;
and the second determining module is used for determining the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the longitudinal tracking control method according to any one of the embodiments of the present invention.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the longitudinal tracking control method according to any one of the embodiments of the present invention.
The embodiment of the invention obtains the current driving data; determining a driving style according to the current driving data; the current state of the vehicle and the expected acceleration of the vehicle are determined according to the driving style and the current driving data, so that the differentiated requirements of drivers with different habit characteristics can be met, the satisfaction degree and the comfort degree of the drivers are greatly improved, better driving experience is provided for the drivers, and the method has great significance for improving the system applicability, guaranteeing the vehicle safety and reducing traffic accidents.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a longitudinal tracking control method according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a longitudinal tracking control apparatus according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device in a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example one
Fig. 1 is a flowchart of a longitudinal tracking control method according to an embodiment of the present invention, where the embodiment is applicable to a situation of longitudinal tracking control of a vehicle, and the method may be executed by a longitudinal tracking control apparatus according to an embodiment of the present invention, where the apparatus may be implemented in a software and/or hardware manner, as shown in fig. 1, the method specifically includes the following steps:
and S110, acquiring current driving data.
Wherein the current driving data includes: at least one of a current vehicle speed, a current acceleration, a current travel distance, a current vehicle state, a distance between the host vehicle and the preceding vehicle, a current deceleration, a host vehicle cumulative travel time, and a preceding vehicle speed.
The current driving data may be obtained through a CAN bus or a vehicle-mounted sensor, which is not limited in this embodiment of the present invention.
And S120, determining the driving style according to the current driving data.
Wherein the driving style can be any one of conservative type, general type and aggressive type. The driving style may also be set to other types according to user requirements, which is not limited in this embodiment of the present invention.
Specifically, the manner of determining the driving style according to the current driving data may be to obtain a driving data sample; establishing a decision tree module to be trained; training the decision tree model to be trained according to the driving data sample to obtain a target decision tree model; and inputting the current driving data into the target decision tree model to obtain the driving style corresponding to the current driving data.
And S130, determining the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data.
Specifically, the manner of determining the current state of the host vehicle and the desired acceleration of the host vehicle according to the driving style and the current driving data may be: determining a first distance according to the driving style; when the distance between the vehicle and the front vehicle is greater than the first distance, determining that the vehicle enters an acceleration follow-up state; determining a first desired acceleration of the host vehicle according to the following formula:
Figure BDA0003063229130000041
wherein, ahFor the first desired acceleration, epsilonstyleEpsilon when the driving style is conservative for personalized indexstyle0.8, when the driving style is generalstyle1, when the driving style is aggressivestyle＝1.2，vCIPVFor the speed of the preceding vehicle, v1Is the speed of the vehicle, vcaliTo calibrate the vehicle speed. The method for determining the current state of the vehicle and the desired acceleration of the vehicle according to the driving style and the current driving data may further comprise: determining a first distance according to the driving style; when the distance between the vehicle and the front vehicle is smaller than the first distance and larger than a distance threshold value, determining that the vehicle enters a following state; determining a second desired acceleration of the host vehicle according to the following formula:
Figure BDA0003063229130000051
Figure BDA0003063229130000052
wherein, bhFor the second desired acceleration, KCIPVIs a first calibration constant, DcaliFor calibrating the distance, DrealDistance between the vehicle and the preceding vehicle, DstyleIs a first distance; when the distance between the vehicle and the front vehicle is smaller than a distance threshold value, determining that the vehicle enters an emergency braking state; determining a third desired acceleration of the host vehicle according to the following formula:
Figure BDA0003063229130000053
wherein, chIs a third desired acceleration, dcaliFor the second calibrationAnd (4) counting.
Optionally, determining the driving style according to the current driving data includes:
obtaining a driving data sample;
establishing a decision tree module to be trained;
training the decision tree model to be trained according to the driving data sample to obtain a target decision tree model;
and inputting the current driving data into the target decision tree model to obtain the driving style corresponding to the current driving data.
Optionally, obtaining the driving data sample comprises:
acquiring historical driving data;
overlapping the historical driving data to obtain first driving data;
fitting the first driving data about sampling time to obtain a fitting function;
and supplementing the data between the first driving data sampling intervals according to the fitting function to obtain driving data samples.
Optionally, training the decision tree model to be trained according to the driving data sample to obtain a target decision tree model, including:
establishing a characteristic database according to the driving data sample, wherein the characteristic database comprises: at least one of an average speed, an average acceleration, an average deceleration, a speed standard deviation, an acceleration standard deviation, a travel distance, a maximum acceleration, a maximum deceleration, a maximum speed, an idle time ratio, an acceleration time ratio, a deceleration time ratio, a constant speed time ratio, a low speed time ratio, a medium speed time ratio, and a high speed time ratio;
and training at least one decision tree model to be trained on the basis of the characteristic database to obtain at least one target decision tree model.
Optionally, determining the current state of the vehicle and the desired acceleration of the vehicle according to the driving style and the current driving data includes:
determining a first distance according to the driving style;
when the distance between the vehicle and the front vehicle is greater than the first distance, determining that the vehicle enters an acceleration follow-up state;
determining a first desired acceleration of the host vehicle according to the following formula:
Figure BDA0003063229130000061
wherein, ahFor the first desired acceleration, epsilonstyleEpsilon when the driving style is conservative for personalized indexstyle0.8, when the driving style is generalstyle1, when the driving style is aggressivestyle＝1.2，vCIPVFor the speed of the preceding vehicle, v1Is the speed of the vehicle, vcaliTo calibrate the vehicle speed.
Optionally, determining the current state of the vehicle and the desired acceleration of the vehicle according to the driving style and the current driving data includes:
determining a first distance according to the driving style;
when the distance between the vehicle and the front vehicle is smaller than the first distance and larger than a distance threshold value, determining that the vehicle enters a following state;
determining a second desired acceleration of the host vehicle according to the following formula:
Figure BDA0003063229130000071
wherein, bhFor the second desired acceleration, KCIPVIs a first calibration constant, DcaliFor calibrating the distance, DrealDistance between the vehicle and the preceding vehicle, DstyleIs a first distance;
when the distance between the vehicle and the front vehicle is smaller than a distance threshold value, determining that the vehicle enters an emergency braking state;
determining a third desired acceleration of the host vehicle according to the following formula:
Figure BDA0003063229130000072
wherein, chIs a third desired acceleration, dcaliIs a second calibration constant.
Optionally, the driving data includes: at least one of speed, acceleration, travel distance, idle state, acceleration state, deceleration state, distance between the host vehicle and the preceding vehicle, deceleration, host vehicle cumulative travel time, and preceding vehicle speed.
In a specific example, the invention provides a personalized vehicle longitudinal control method, which comprises the following steps:
step 1, data preprocessing: historical driving data x and subsequent main data acquired by test acquisition are shown in table 1:
TABLE 1
Number k 1 2 … m
Time t t1 t2 … tm
Driving data x x1 x2 … xm
Superimposed data X X1 X2 … Xm
Historical driving data X is the data of gathering through the real-vehicle test, but receive the limitation of data acquisition equipment, the data of gathering often are the discrete form, data interval cycle is sampling step length promptly, in order to obtain more comprehensive, more continuous driving data, supplement historical driving data X, at first stack historical driving data X, obtain first driving data, in order to increase the linear relation of data, conveniently carry out subsequent fitting operation, specifically, first driving data X is obtained through the stack by historical driving data X:
X1＝x1
X2＝x1+x2
X3＝x1+x2+x3
……
Xm＝x1+x2+x3+……+xm
fitting the first driving data X with better linear relation with respect to the sampling time t, and constructing an independent variable vector in the software matlab: m sampling times t ═ t1、t2…tm]And constructing a dependent variable vector: m pieces of superimposed data X ═ X1、X2…Xm]。
The order number n of the fitting equation was determined by the MATLAB program:
for i＝1:6
xx1＝polyfit(t,X,i)；
XX＝polyval(xxy1,t)；
if sum(XX-X)2<0.05
c＝i
break；
end
end
and then the fitting equation order n when the sum of the squared error values is less than 0.05 can be obtained.
The function is next entered in the MATLAB window:
yy2＝polyfit(t,X,n)
the polynomial fitting function coefficient can be obtained by pressing the enter key:
a0、a1……、an；
aiis corresponding to xn-iThe coefficients of (a) can thus be written to obtain a fitting function:
Figure BDA0003063229130000091
wherein n is the order of a fitting equation, based on the fitting relationship between the sampling time t and the first driving data X, by fitting a function
Figure BDA0003063229130000092
Supplementing data between sampling intervals of the first driving data X, every two adjacent superimposed data Xe、XgIs supplemented with an overlay data Xf：
Figure BDA0003063229130000093
Wherein, Xe、XgThe sampling sequence numbers of (a) are t-e, t-g, g-e +1,
Figure BDA0003063229130000094
the supplemented superimposed data X is shown in table 2:
TABLE 2
Time t t1 t1.5 t2 … te tf tg … tm
Superimposed data X X1 X1.5 X2 … Xe Xf Xg … Xm
Further, the relationship between the first driving data X and the historical driving data X is used for solving the expanded driving data sample in a reverse mode:
x1＝X1
x1.5＝X1.5-X1
x2＝X2-X1.5
……
xm＝Xm-Xm-1
the driving data samples are shown in table 3:
TABLE 3
Figure BDA0003063229130000101
Defining driving data samples:
Y1＝x1
Y2＝x1.5
Y3＝x2
……
Y2m-1＝xm
the driving data samples are shown in table 4:
TABLE 4
Figure BDA0003063229130000102
In the subsequent steps, driving style analysis is performed by using the driving data samples.
Step 2, driving style analysis
A characteristic parameter library for judging the driving style is constructed based on the driving data sample, and the characteristic parameter library comprises the following 16 characteristic parameters:
(1) average speed: the arithmetic mean value of the vehicle speed within T seconds does not contain the idle state of the vehicle;
(2) average acceleration: the arithmetic mean value of the acceleration of the vehicle in the acceleration state in each unit time (second) within T seconds;
(3) average deceleration: an arithmetic average of the deceleration of the vehicle in the decelerating state for each unit time (second) within T seconds;
(4) standard deviation of speed: standard deviation of vehicle speed within T seconds, namely including idle state;
(5) acceleration standard deviation: a standard deviation of the acceleration of the vehicle in an acceleration state for T seconds;
(6) driving mileage: the driving distance of the vehicle within T seconds;
(7) maximum acceleration: maximum value of acceleration of the vehicle in an acceleration state within T seconds;
(8) maximum deceleration: the maximum value of deceleration of the vehicle in the deceleration state within T seconds;
(9) maximum speed: maximum value of vehicle speed within T seconds;
(10) idle time ratio: the cumulative time span of the idle state accounts for the percentage of the total time span within T seconds;
(11) acceleration time ratio: the cumulative time length in the acceleration state accounts for the percentage of the total time length within T seconds;
(12) the deceleration time ratio is: the cumulative time length in the deceleration state accounts for the percentage of the total time length within T seconds;
(13) uniform speed time ratio: the cumulative time length in the constant speed (the continuous process that the absolute value of the vehicle acceleration is less than 0.1m/s2 and is not idle) state accounts for the percentage of the total time length within T seconds;
(14) low speed time ratio: the percentage of the accumulated time length of the vehicle running speed less than 40km/h in the total time length within T seconds;
(15) medium speed time ratio: the cumulative time length of the vehicle running speed between 40-70km/h accounts for the percentage of the total time length of the time period within T seconds;
(16) high speed time ratio: the accumulated time length of the vehicle running speed of more than 70km/h accounts for the percentage of the total time length of the time period within T seconds;
wherein T represents a time period, and the calculation formula of T is as follows:
Figure BDA0003063229130000121
wherein, KTIs a first speed coefficient, CTCC is the first speed base and CC is the first speed exponent.
When the real-time vehicle speed is more than 100km/h, KT＝1.3，CT＝20，CC＝1.2；
When the real-time vehicle speed is more than 60km/h and less than 100km/h, KT＝1.8，CT＝15，CC＝1.5；
When the real-time vehicle speed is less than 60km/h, KT＝2.2，CT＝8，CC＝1.8；
Training N decision tree classifiers based on a characteristic parameter library constructed by driving data Y for training, wherein K parameters are selected from 16 characteristic parameters at random as input parameters of the decision tree classifiers during each training, and a calculation formula of K is as follows:
K＝4×KN×CN DD；
wherein, KNIs a second velocity coefficient, CNIs the second speed base and is a speed base,
when the real-time vehicle speed is more than 100km/h, KN＝1.3，CN＝20；
When the real-time vehicle speed is more than 60km/h and less than 100km/h, KN＝1.8，CN＝15；
When the real-time vehicle speed is less than 60km/h, KN＝2.2，CN＝8；
Wherein the numerical value of DD is randomly generated under MATLAB:
clear all
clc
warning off
b＝randperm(8)；
DD ═ b (1); % randomly generated DD.
Then, training and generating of the driving style model is realized by adopting a decision tree, specifically under MATLAB, and a specific language program is as follows:
mat% load data
a＝randperm(50)；
Train＝data(a(1:25),:)；
Test＝data(a(6:end),:)；
P_train＝Train(:,K+1:end)；
T_train＝Train(:,K)；
P_test＝Test(:,K+1:end)；
T_test＝Test(:,K)；
model＝classRF_train(P_train,T_train)；
The above procedure is repeatedly run for 50 times, 50 driving style identification decision trees are generated in total, after driving data is input, each decision tree in the 50 driving style identification decision trees can output a driving style (conservative type, general type and aggressive type) identification result, the results output by the 50 decision trees are voted, and the driving style (conservative type, general type and aggressive type) with the largest number of votes is determined as a final driving style judgment result.
Step 3, personalized longitudinal car following control:
when the distance D between the vehicle and the front vehiclerealGreater than the first distance DstyleHour (when the driving style is conservative D)styleWhen the driving style is general type D28 style20, when the driving style is aggressive Dstyle16), the vehicle enters an acceleration follow-up state, and the first expected acceleration of the vehicle is:
Figure BDA0003063229130000131
wherein, ahFor the first desired acceleration, epsilonstyleEpsilon when the driving style is conservative for personalized indexstyle0.8, when the driving style is generalstyle1, when the driving style is aggressivestyle＝1.2，vCIPVAt the target vehicle speed, v1Is a bookVehicle speed, vcaliFor calibrating the vehicle speed, the value of the calibrated vehicle speed can be 15, and can also be set according to the requirements of users.
When the distance D between the vehicle and the front vehiclerealIs less than DstyleAnd when the distance threshold is greater than the distance threshold, wherein the distance threshold may be 5m, the vehicle enters a following state, and the expected acceleration of the vehicle is:
when the distance between the vehicle and the front vehicle is smaller than the first distance and larger than a distance threshold value, determining that the vehicle enters a following state;
determining a second desired acceleration of the host vehicle according to the following formula:
Figure BDA0003063229130000141
wherein, bhFor the second desired acceleration, KCIPVIs a first calibration constant, DcaliFor calibrating the distance, DrealDistance between the vehicle and the preceding vehicle, DstyleIs a first distance; kCIPVValues may be 36.6, DcaliThe value may be 8.
When the distance between the vehicle and the front vehicle is smaller than a distance threshold value, determining that the vehicle enters an emergency braking state;
determining a third desired acceleration of the host vehicle according to the following formula:
Figure BDA0003063229130000142
wherein, chIs a third desired acceleration, dcaliThe value of the second calibration constant may be 2.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The embodiment of the invention explores the characteristic rule of the driving habit of a driver through data mining and machine learning theories, establishes a driving habit identification and characterization scheme, further considers the driving habit design of the driver, realizes the personalized vehicle longitudinal control method, makes the design of an automobile return to human-oriented, meets the differentiation requirements of the drivers with different habit characteristics, greatly improves the satisfaction degree and comfort degree of the drivers, provides better driving experience for the drivers, and has great significance for improving the system applicability, ensuring the automobile safety and reducing traffic accidents.
According to the technical scheme of the embodiment, the current driving data is acquired; determining a driving style according to the current driving data; the current state of the vehicle and the expected acceleration of the vehicle are determined according to the driving style and the current driving data, so that the differentiated requirements of drivers with different habit characteristics can be met, the satisfaction degree and the comfort degree of the drivers are greatly improved, better driving experience is provided for the drivers, and the method has great significance for improving the system applicability, guaranteeing the vehicle safety and reducing traffic accidents.
Example two
Fig. 2 is a schematic structural diagram of a longitudinal tracking control device according to a second embodiment of the present invention. The embodiment may be applicable to the case of longitudinal tracking control, and the apparatus may be implemented in a software and/or hardware manner, and may be integrated in any device that provides a longitudinal tracking control function, as shown in fig. 2, where the longitudinal tracking control apparatus specifically includes: an acquisition module 210, a first determination module 220, and a second determination module 230.
The obtaining module 210 is configured to obtain current driving data;
a first determining module 220, configured to determine a driving style according to the current driving data;
a second determining module 230, configured to determine a current state of the host vehicle and a desired acceleration of the host vehicle according to the driving style and the current driving data.
The product can execute the method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
According to the technical scheme of the embodiment, the current driving data is acquired; determining a driving style according to the current driving data; the current state of the vehicle and the expected acceleration of the vehicle are determined according to the driving style and the current driving data, so that the differentiated requirements of drivers with different habit characteristics can be met, the satisfaction degree and the comfort degree of the drivers are greatly improved, better driving experience is provided for the drivers, and the method has great significance for improving the system applicability, guaranteeing the vehicle safety and reducing traffic accidents.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a computer device in a third embodiment of the present invention. FIG. 3 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in FIG. 3 is only an example and should not impose any limitation on the scope of use or functionality of embodiments of the present invention.
As shown in FIG. 3, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.
Computer device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system Memory 28 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 30 and/or cache Memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (a Compact disk-Read Only Memory (CD-ROM)), Digital Video disk (DVD-ROM), or other optical media may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. In the computer device 12 of the present embodiment, the display 24 is not provided as a separate body but is embedded in the mirror surface, and when the display surface of the display 24 is not displayed, the display surface of the display 24 and the mirror surface are visually integrated. Moreover, computer device 12 may also communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN)) and/or a public Network (e.g., the Internet) via Network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, Redundant processing units, external disk drive Arrays, disk array (RAID) systems, tape drives, and data backup storage systems, to name a few.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, implementing the longitudinal tracking control method provided by the embodiment of the present invention:
acquiring current driving data;
determining a driving style according to the current driving data;
and determining the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data.
Example four
A fourth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the longitudinal tracking control method provided in all the embodiments of the present invention of the present application:
acquiring current driving data;
determining a driving style according to the current driving data;
and determining the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (Hyper Text Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A longitudinal tracking control method, comprising:
acquiring current driving data;
determining a driving style according to the current driving data;
and determining the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data.
2. The method of claim 1, wherein determining a driving style from the current driving data comprises:
obtaining a driving data sample;
establishing a decision tree module to be trained;
training the decision tree model to be trained according to the driving data sample to obtain a target decision tree model;
and inputting the current driving data into the target decision tree model to obtain the driving style corresponding to the current driving data.
3. The method of claim 2, wherein obtaining driving data samples comprises:
acquiring historical driving data;
overlapping the historical driving data to obtain first driving data;
fitting the first driving data about sampling time to obtain a fitting function;
and supplementing the data between the first driving data sampling intervals according to the fitting function to obtain driving data samples.
4. The method of claim 2, wherein training the decision tree model to be trained according to the driving data samples to obtain a target decision tree model comprises:
establishing a characteristic database according to the driving data sample, wherein the characteristic database comprises: at least one of an average speed, an average acceleration, an average deceleration, a speed standard deviation, an acceleration standard deviation, a travel distance, a maximum acceleration, a maximum deceleration, a maximum speed, an idle time ratio, an acceleration time ratio, a deceleration time ratio, a constant speed time ratio, a low speed time ratio, a medium speed time ratio, and a high speed time ratio;
and training at least one decision tree model to be trained on the basis of the characteristic database to obtain at least one target decision tree model.
5. The method of claim 1, wherein determining a current state of the host vehicle and a desired acceleration of the host vehicle based on the driving style and the current driving data comprises:
determining a first distance according to the driving style;
when the distance between the vehicle and the front vehicle is greater than the first distance, determining that the vehicle enters an acceleration follow-up state;
determining a first desired acceleration of the host vehicle according to the following formula:
Figure FDA0003063229120000021
wherein, ahFor the first desired acceleration, epsilonstyleEpsilon when the driving style is conservative for personalized indexstyle0.8, when the driving style is generalstyle1, when the driving style is aggressivestyle＝1.2，vCIPVFor the speed of the preceding vehicle, v1Is the speed of the vehicle, vcaliTo calibrate the vehicle speed.
6. The method of claim 5, wherein determining a current state of the host vehicle and a desired acceleration of the host vehicle based on the driving style and the current driving data comprises:
determining a first distance according to the driving style;
when the distance between the vehicle and the front vehicle is smaller than the first distance and larger than a distance threshold value, determining that the vehicle enters a following state;
determining a second desired acceleration of the host vehicle according to the following formula:
Figure FDA0003063229120000022
wherein, bhFor the second desired acceleration, KCIPVIs a first calibration constant, DcaliFor calibrating the distance, DrealDistance between the vehicle and the preceding vehicle, DstyleIs a first distance;
when the distance between the vehicle and the front vehicle is smaller than a distance threshold value, determining that the vehicle enters an emergency braking state;
determining a third desired acceleration of the host vehicle according to the following formula:
Figure FDA0003063229120000031
wherein, chIs a third desired acceleration, dcaliIs a second calibration constant.
7. The method of claim 1, wherein the driving data comprises: at least one of a vehicle speed, an acceleration, a travel distance, an idle state, an acceleration state, a deceleration state, a distance between the host vehicle and the preceding vehicle, a deceleration, a host vehicle cumulative travel time, and a preceding vehicle speed.
8. A longitudinal tracking control apparatus, comprising:
the acquisition module is used for acquiring current driving data;
the first determining module is used for determining the driving style according to the current driving data;
and the second determining module is used for determining the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the longitudinal tracking control method according to any of claims 1-7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a longitudinal tracking control method according to any one of claims 1 to 7.
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