CN113753060A - Vehicle control method and device, computing equipment and medium - Google Patents

Vehicle control method and device, computing equipment and medium Download PDF

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Publication number
CN113753060A
CN113753060A CN202011199616.8A CN202011199616A CN113753060A CN 113753060 A CN113753060 A CN 113753060A CN 202011199616 A CN202011199616 A CN 202011199616A CN 113753060 A CN113753060 A CN 113753060A
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prediction
period
rate
vehicle
determining
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CN113753060B (en
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边学鹏
窦凤谦
刘懿
高萌
张亮亮
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Beijing Jingdong Qianshi Technology Co Ltd
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Beijing Jingdong Qianshi Technology Co Ltd
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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
    • B60W40/10—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 vehicle motion
    • B60W40/105—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
    • 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
    • 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
    • B60W2050/0001—Details of the control system
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
    • 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

Abstract

The present disclosure provides a control method of a vehicle. The method includes acquiring a current speed of the vehicle; determining a target rate interval to which a current rate belongs from a plurality of rate intervals; determining a period prediction rule corresponding to the target rate interval; determining a prediction period according to the current rate and a period prediction rule; performing path prediction according to the prediction period to obtain a prediction result; and controlling the running of the vehicle according to the prediction result. The disclosure also provides a control device, a computing device and a medium of the vehicle.

Description

Vehicle control method and device, computing equipment and medium
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for controlling a vehicle, a computing device, and a medium.
Background
The model predictive control algorithm (hereinafter abbreviated as mpc) belongs to a multi-step predictive algorithm, and the mpc has several important parameters: the prediction time domain, the control time domain, the prediction period and the like, and how to design the parameters is an important problem.
The related art sets all the parameters to a fixed value, so that although the consistency of the prediction period is ensured, the tracking effect is poor for complex tracks (such as complex paths and complex speeds).
Disclosure of Invention
In view of the above, the present disclosure provides a control method, apparatus, computing device and medium for a vehicle.
One aspect of the present disclosure provides a control method of a vehicle, including: acquiring the current speed of the vehicle; determining a target rate interval to which the current rate belongs from a plurality of rate intervals; determining a period prediction rule corresponding to the target rate interval; determining a prediction period according to the current rate and the period prediction rule; performing path prediction according to the prediction period to obtain a prediction result; and controlling the running of the vehicle according to the prediction result.
According to an embodiment of the present disclosure, the method further comprises: obtaining a predicted time-domain parameter NpWherein, the N ispIs a positive integer; determining a functional relationship between a velocity and a predicted distance within each of the plurality of velocity intervals; and according to the functional relation between the speed and the prediction distance and the prediction time domain parameter NpAnd configuring a period prediction rule corresponding to each rate interval.
According to an embodiment of the present disclosure, the determining a functional relationship between a rate and a predicted distance in each of the plurality of rate intervals comprises: for each rate interval in the multiple rate intervals, acquiring multiple standard rates in the rate interval and standard prediction distances corresponding to the multiple standard rates; and fitting the plurality of standard rates and the corresponding standard prediction distances to obtain a functional relation between the rates and the prediction distances in the rate interval.
According to an embodiment of the present disclosure, the time domain parameter N is predicted according to a functional relationship between the rate and a prediction distancepIs arranged ofA period prediction rule corresponding to each of the rate intervals, comprising: configuring a period prediction rule corresponding to each rate interval according to the following formula:
Figure BDA0002753321880000021
wherein T is the period prediction rule, v isminThe α, A, B, k1 and k2 are constants for a preset velocity minimum, the A, B, k1 and k2 are determined as a function of the velocity and the predicted distance, C ═ k1 (v3-v2) + B.
According to an embodiment of the present disclosure, the performing path prediction according to the prediction cycle includes: from the predicted time-domain parameter NpAnd the prediction period, determining a planning model; and determining N using the planning modelpAnd the position coordinates, the course angle and the speed of each reference point are used as the prediction result.
According to an embodiment of the present disclosure, the controlling the traveling of the vehicle according to the prediction result includes: obtaining a control time domain parameter NcWherein, the N iscIs a positive integer; generating N according to the prediction resultcA control command; and according to said NcA control command, in the future NcAnd adjusting the running parameters of the vehicle at each moment.
Another aspect of the present disclosure provides a control apparatus of a vehicle, including: the speed acquisition module is used for acquiring the current speed of the vehicle; an interval determination module, configured to determine a target rate interval to which the current rate belongs from multiple rate intervals; a rule determining module, configured to determine a period prediction rule corresponding to the target rate interval; the period determining module is used for determining a prediction period according to the current rate and the period prediction rule; the prediction module is used for executing path prediction according to the prediction period so as to obtain a prediction result; and the control module is used for controlling the running of the vehicle according to the prediction result.
In accordance with an embodiment of the present disclosure,the device further comprises: a prediction time domain obtaining module for obtaining a prediction time domain parameter NpWherein, the N ispIs a positive integer; a functional relationship determination module for determining a functional relationship between a rate in each of the plurality of rate intervals and a predicted distance; and a rule configuration module for configuring the time domain parameter N according to the function relation between the speed and the prediction distancepAnd configuring a period prediction rule corresponding to each rate interval.
According to an embodiment of the present disclosure, the prediction module includes: a model building submodule for predicting a time domain parameter N based on the time domain parameterpAnd the prediction period, determining a planning model; and a model solution submodule for determining N using the planning modelpAnd the position coordinates, the course angle and the speed of each reference point are used as the prediction result.
Another aspect of the disclosure provides a computing device comprising: one or more processors; storage means for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
According to the embodiment of the present disclosure, the prediction period is continuously changed with the vehicle speed by relating the prediction period to the vehicle speed. When vehicle control is carried out, a corresponding period prediction rule is determined according to a target speed interval to which the current speed belongs, and a prediction period is determined according to the period prediction rule, so that the prediction period is adaptive to the vehicle speed. And when the path prediction is executed according to the prediction period and the vehicle is controlled to run according to the prediction result, the smoothness of vehicle control can be ensured, and the effect is better.
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The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
fig. 1 schematically shows an exemplary application scenario in which a control method of a vehicle may be applied according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a control method of a vehicle according to an embodiment of the disclosure;
FIG. 3 schematically illustrates a flow chart of a method of configuring a periodic prediction rule for each rate interval according to the present disclosure;
FIG. 4 is a schematic diagram showing the velocity in each velocity interval as a function of predicted distance;
fig. 5 schematically shows a block diagram of a control device of a vehicle according to an embodiment of the present disclosure; and
FIG. 6 schematically illustrates a block diagram of a computer system suitable for implementing the methods of embodiments of the present invention, in accordance with an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Embodiments of the present disclosure provide a control method of a vehicle and a control apparatus of a vehicle to which the method can be applied. The method includes acquiring a current speed of the vehicle; determining a target rate interval to which a current rate belongs from a plurality of rate intervals; determining a period prediction rule corresponding to the target rate interval; determining a prediction period according to the current rate and a period prediction rule; performing path prediction according to the prediction period to obtain a prediction result; and controlling the running of the vehicle according to the prediction result.
Fig. 1 schematically illustrates an exemplary application scenario 100 in which a control method of a vehicle may be applied according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of an application scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the application scenario 100 according to the embodiment may include a vehicle 10 and a vehicle 20, where the vehicle 10 and the vehicle 20 respectively travel along a lane in a direction indicated by an arrow, and during the travel of the two vehicles, travel paths of the two vehicles are predicted according to a model predictive control algorithm (mpc), and the travel of the two vehicles is controlled according to the prediction result. The current vehicle speed of the vehicle 10 is v1, and the predicted distance of the vehicle 10 is s 1. The current vehicle speed of the vehicle 20 is v2, and the predicted distance of the vehicle 20 is s 2.
According to the embodiment of the disclosure, when the vehicles travel at different speeds, the corresponding predicted distances are also different, and the faster the speed of the vehicle is, the larger the predicted distance is configured accordingly. In the application scenario 100, the vehicle speed v2 of the vehicle 20 is greater than the vehicle speed v1 of the vehicle 10, and thus the predicted distance s2 of the vehicle 20 is greater than the predicted distance s1 of the vehicle 10.
During the running of the vehicles 10 and 20, the vehicles 10 and 20 may be controlled by control commands. In this embodiment, in order to ensure the smoothness of the control command, the prediction distance is effectively associated with the vehicle speed, and the prediction period is also associated with the vehicle speed, so as to obtain the following formula:
s＝Np*T*v
where s is the predicted distance, NpIn order to predict the time domain parameters, T is the prediction period, and v is the current speed of the vehicle.
Further, a calculation formula of the prediction period can be derived according to the formula:
Figure BDA0002753321880000061
according to the embodiment of the present disclosure, the predicted distance s at different speeds may be estimated from the driving experience and the real vehicle commissioning experience, and the value of s may be set according to the estimation result.
According to the embodiment of the disclosure, different N can be comparedpAnd NcUnder the value, the amplitude characteristic, the phase characteristic, the controller frequency and the 99-quantile of the controller at the vehicle end of the controller of the vehicleSolving time and the like, and setting NpAnd NcSize. In addition, in setting the parameter NpAnd NcIn the process, the influence of factors such as a planned track, the wheel base of the vehicle, the motor performance of a vehicle chassis, a control period, the calculation performance of an industrial personal computer and the like can be considered.
After T is obtained through calculation, T is taken as a prediction period, and N is taken aspAnd NcAnd respectively carrying out the mpc rolling optimization solution on the basis of a vehicle kinematics or dynamics model for a prediction time domain and a control time domain. Final output NpA predicted point and NcA control instruction. Then generating the N to the vehiclecAnd a control command for controlling the running of the vehicle.
Fig. 2 schematically shows a flow chart of a control method of a vehicle according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes the following operations S210 to S260.
In operation S210, a current velocity of the vehicle is acquired.
Then, a target rate interval to which the current rate belongs is determined from among the plurality of rate intervals in operation S220.
In operation S230, a period prediction rule corresponding to the target rate interval is determined.
In operation S240, a prediction period is determined according to the current rate and the period prediction rule.
In operation S250, path prediction is performed according to the prediction period to obtain a prediction result.
In operation S260, the travel of the vehicle is controlled according to the prediction result.
According to the embodiments of the present disclosure, the current velocity of the vehicle may be obtained from data detected by a speedometer sensor inside the vehicle, or may also be measured by a velocity measurement device outside the vehicle. It should be noted that, in practical applications, any other means may also be used to obtain the current speed of the vehicle, and the present disclosure does not specifically limit the manner of obtaining the current speed of the vehicle.
According to the embodiment of the present disclosure, when the vehicle travels at different speeds, the corresponding predicted distances are different, and the faster the speed of the vehicle, the larger the predicted distance. Therefore, in this embodiment, the velocity may be divided into a plurality of velocity intervals in advance, and each velocity interval corresponds to one of the cycle prediction rules, so that the prediction cycle determined according to the cycle prediction rule may vary with the variation of the vehicle speed, and continuity is maintained at all times.
According to the embodiment of the disclosure, the time domain parameter N can be predicted according topAnd predicting the period, determining a planning model, and then determining N using the planning modelpThe position coordinates, the course angle and the speed of each reference point are used as prediction results.
For example, in this embodiment, after T is obtained, N may be obtained by performing backward interpolation on the track to be tracked with T as a period and the track point closest to the vehicle as a starting pointpA track point as a reference point, wherein N ispThe track points are all on the track.
In this embodiment, the reference point elements may include { x, y, θ, v }, for example, where x and y are position coordinates, θ is a heading angle, and v is a velocity. Thereby NpThe reference points may form a reference point of size Np4, i.e. as prediction result.
According to embodiments of the present disclosure, the planning model may include, for example, a vehicle kinematics or dynamics model. Illustratively, the planning model is represented by the following formula,
x(k+1)＝f1(x(k)，T)；
y(k+1)＝f2(y(k)，T)；
θ(k+1)＝f3(θ(k)，T)；
v(k+1)＝f4(v(k)，T)；
wherein k is [0, N ]p]And f1, f2, f3 and f4 indicate that x, y, theta, v at the current time are calculated from x, y, theta, v and T at the previous time, respectively.
According to the embodiment of the disclosure, a control time domain parameter N may be obtainedcWherein N iscIs a positive integer, and N is generated according to the prediction resultcA control command, then according to NcA control command, in the future NcEvery moment is transferredAnd the running parameters of the whole vehicle are obtained, so that the control of the vehicle is realized.
According to the embodiment of the present disclosure, the prediction period is continuously changed with the vehicle speed by relating the prediction period to the vehicle speed. When vehicle control is carried out, a corresponding period prediction rule is determined according to a target speed interval to which the current speed belongs, and a prediction period is determined according to the period prediction rule, so that the prediction period is adaptive to the vehicle speed. And when the path prediction is executed according to the prediction period and the vehicle is controlled to run according to the prediction result, the smoothness of vehicle control can be ensured, and the effect is better.
The following further describes a method for configuring the period prediction rule corresponding to each rate interval.
Fig. 3 schematically illustrates a flow chart of a method of configuring a periodic prediction rule corresponding to each rate interval according to the present disclosure.
As shown in fig. 3, the method includes the following operations S310 to S330. Operations S310 to S330 may be performed, for example, before operation S210.
In operation S310, a prediction time domain parameter N is acquiredpWherein N ispIs a positive integer.
In operation S320, a functional relationship between a velocity and a predicted distance in each of a plurality of velocity intervals is determined.
In operation S330, a time domain parameter N is predicted according to a functional relationship between a rate and a prediction distancepAnd configuring a period prediction rule corresponding to each rate interval.
According to the embodiment of the disclosure, for each of a plurality of rate intervals, a plurality of standard rates within the rate interval and a standard prediction distance corresponding to the plurality of standard rates may be acquired. The standard speed and the standard prediction distance can be preset manually according to driving experience and real vehicle debugging experience and serve as calculated standard values. And then, fitting the plurality of standard rates and the corresponding standard prediction distances to obtain a functional relation between the rate and the prediction distances in the rate interval.
Illustratively, in this embodiment, the rate interval may include a first interval, a second interval, a third interval, and a fourth interval. The first interval is [0, v1), the second interval is [ v1, v2), the third interval is [ v2, v3), the fourth interval is [ v3, v 4).
According to an embodiment of the present disclosure, if the speed is in the first interval, the period prediction rule includes calculating the prediction period according to a first formula, where the first formula is
Figure BDA0002753321880000091
Where T is the prediction period, NpTo predict the temporal parameters, v is velocity, vminα is a constant set to prevent a calculation error when v is 0, and a is a constant, which is a preset minimum speed value. The value of a may be fitted using a plurality of standard rates and corresponding standard predicted distances within the first interval.
If the speed is in the second interval, the period prediction rule comprises calculating the prediction period according to a second formula, wherein the second formula is
T＝B。
And B is a constant, and the value of B can be obtained by fitting a plurality of standard rates in the second interval and corresponding standard prediction distances.
If the speed is in a third interval, the period prediction rule comprises calculating the prediction period according to a third formula, wherein the third formula is
k1*(v-v2)+B。
Where k1 is a constant, the value of k1 can be obtained by fitting a plurality of standard rates in the third interval and the corresponding standard prediction distances.
If the speed is in the fourth interval, the period prediction rule comprises calculating the prediction period according to a fourth formula, wherein the fourth formula is
k2*(v-v3)+C。
Where k2 is a constant, the value of k2 can be obtained by fitting a plurality of standard rates in the third interval and corresponding standard prediction distances, and C is k1 (v3-v2) + B.
If the speed does not belong to any of the first, second, third, and fourth intervals, an error is reported and the subsequent operation is not executed.
For example, the first interval is 0-1m/s, the second interval is 1.5-5m/s, the third interval is 5-11m/s, and the fourth interval is 11-22 m/s. The standard rates and corresponding standard predicted distances within each interval are shown in table 1. Fig. 4 schematically shows a functional relationship between the velocity and the predicted distance in each velocity interval, and a fitting image such as that shown in fig. 4 can be obtained by fitting the data in table 1.
TABLE 1
Speed value or speed interval (m/s) Predicted distance (m)
0.0-1.0 1.5
1.5 2.25
5.0 7.5
11.0(40km/h) 30
22.0(80km/h) 80
Exemplarily, in NpFor example, 15, the data in table 1 above is interpolated to find the vehicle speedThe functional relationship between the rate and the predicted distance, in combination with the above formula, yields the relationship between T and v as follows:
Figure BDA0002753321880000101
when the vehicle is in a low-speed stage, such as below 1.0m/s, the lock prediction distance is 1.5m, and the lock prediction period is at most 0.5 s. When the vehicle speed is in the range of 1.0m/s to 5.0m/s, the prediction period is kept constant for 0.1 s. When the vehicle speed is in the range of 5.Om/s to 11.0m/s, the prediction period T is 0.013333 (v-5.0) + 0.1. When the vehicle speed is in the range of 11.0m/s to 22.0m/s, the prediction period T is 0.010909 (v-11.0) + 0.18.
Although the speed range is segmented by the strategy, the smoothness of the control command is fundamentally ensured by ensuring the continuity of the predicted distance of the vehicle, the phenomena of sudden change and the like of the control command are effectively prevented, and the tracking accuracy of the control system at different vehicle speeds is also ensured.
Fig. 5 schematically shows a block diagram of a control apparatus of a vehicle according to an embodiment of the present disclosure.
As shown in fig. 5, the apparatus 500 includes a rate acquisition module 510, an interval determination module 520, a rule determination module 530, a period determination module 540, a prediction module 550, and a control module 560.
A velocity acquisition module 510 for acquiring a current velocity of the vehicle.
An interval determining module 520, configured to determine a target rate interval to which the current rate belongs from the multiple rate intervals.
A rule determining module 530, configured to determine a periodic prediction rule corresponding to the target rate interval.
And a period determining module 540, configured to determine a prediction period according to the current rate and the period prediction rule.
And a prediction module 550, configured to perform path prediction according to the prediction cycle to obtain a prediction result.
And a control module 560 for controlling the vehicle to run according to the prediction result.
According to the embodiment of the present disclosure, the prediction period is continuously changed with the vehicle speed by relating the prediction period to the vehicle speed. When vehicle control is carried out, a corresponding period prediction rule is determined according to a target speed interval to which the current speed belongs, and a prediction period is determined according to the period prediction rule, so that the prediction period is adaptive to the vehicle speed. And when the path prediction is executed according to the prediction period and the vehicle is controlled to run according to the prediction result, the smoothness of vehicle control can be ensured, and the effect is better.
According to another embodiment of the present disclosure, the apparatus may further include a prediction time domain obtaining module, a functional relationship determining module, and a rule configuring module. Wherein, the prediction time domain obtaining module is used for obtaining the prediction time domain parameter NpWherein N ispIs a positive integer. And the functional relation determining module is used for determining the functional relation between the speed in each speed interval in the plurality of speed intervals and the prediction distance. A rule configuration module for predicting time domain parameter N according to the functional relation between the speed and the prediction distancepAnd configuring a period prediction rule corresponding to each rate interval.
According to another embodiment of the present disclosure, the prediction module may include a model building sub-module and a model solving sub-module. Wherein the model establishing submodule is used for predicting the time domain parameter NpAnd predicting the period to determine the planning model. A model solution submodule for determining N using the planning modelpThe position coordinates, the course angle and the speed of each reference point are used as prediction results.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any of the rate acquisition module 510, the interval determination module 520, the rule determination module 530, the period determination module 540, the prediction module 550, and the control module 560 may be combined in one module to be implemented, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the rate obtaining module 510, the interval determining module 520, the rule determining module 530, the period determining module 540, the predicting module 550 and the controlling module 560 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware and firmware, or by a suitable combination of any of them. Alternatively, at least one of the rate acquisition module 510, the interval determination module 520, the rule determination module 530, the period determination module 540, the prediction module 550, and the control module 560 may be at least partially implemented as a computer program module that, when executed, may perform a corresponding function.
FIG. 6 schematically shows a block diagram of a computer system suitable for implementing the above described method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 6 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 6, a computer system 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. Processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 601 may also include onboard memory for caching purposes. Processor 601 may include a single processing unit or multiple processing units for performing different actions of a method flow according to embodiments of the disclosure.
In the RAM 603, various programs and data necessary for the operation of the system 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or RAM 603. It is to be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, system 600 may also include an input/output (I/O) interface 605, input/output (I/O) interface 605 also connected to bus 604. The system 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: 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), 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 present disclosure, 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. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 602 and/or RAM 603 described above and/or one or more memories other than the ROM 602 and RAM 603.
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 or flowchart illustration, and combinations of blocks in the block diagrams 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.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (10)

1. A control method of a vehicle, comprising:
acquiring the current speed of the vehicle;
determining a target rate interval to which the current rate belongs from a plurality of rate intervals;
determining a period prediction rule corresponding to the target rate interval;
determining a prediction period according to the current rate and the period prediction rule;
performing path prediction according to the prediction period to obtain a prediction result; and
and controlling the running of the vehicle according to the prediction result.
2. The method of claim 1, further comprising:
obtaining a predicted time-domain parameter NpWherein, the N ispIs a positive integer;
determining a functional relationship between a velocity and a predicted distance within each of the plurality of velocity intervals; and
according to the functional relation between the speed and the prediction distance and the prediction time domain parameter NpAnd configuring a period prediction rule corresponding to each rate interval.
3. The method of claim 2, wherein said determining a functional relationship between velocity and predicted distance within each of said plurality of velocity intervals comprises:
for each rate interval in the multiple rate intervals, acquiring multiple standard rates in the rate interval and standard prediction distances corresponding to the multiple standard rates; and
and fitting the plurality of standard rates and the corresponding standard prediction distances to obtain a functional relation between the rates and the prediction distances in the rate interval.
4. The method of claim 3Method, wherein said predicting time domain parameter N is based on a functional relationship between said rate and a prediction distancepConfiguring a cycle prediction rule corresponding to each of the rate intervals, including:
configuring a period prediction rule corresponding to each rate interval according to the following formula:
Figure FDA0002753321870000011
wherein T is the period prediction rule, v isminThe α, A, B, k1 and k2 are constants for a preset velocity minimum, the A, B, k1 and k2 are determined as a function of the velocity and the predicted distance, C ═ k1 (v3-v2) + B.
5. The method of claim 2, wherein the performing path prediction according to the prediction period comprises:
from the predicted time-domain parameter NpAnd the prediction period, determining a planning model; and
determining N using the planning modelpAnd the position coordinates, the course angle and the speed of each reference point are used as the prediction result.
6. The method of claim 3, wherein said controlling the travel of the vehicle based on the prediction comprises:
obtaining a control time domain parameter NcWherein, the N iscIs a positive integer;
generating N according to the prediction resultcA control command; and
according to said NcA control command, in the future NcAnd adjusting the running parameters of the vehicle at each moment.
7. A control device of a vehicle, comprising:
the speed acquisition module is used for acquiring the current speed of the vehicle;
an interval determination module, configured to determine a target rate interval to which the current rate belongs from multiple rate intervals;
a rule determining module, configured to determine a period prediction rule corresponding to the target rate interval;
the period determining module is used for determining a prediction period according to the current rate and the period prediction rule;
the prediction module is used for executing path prediction according to the prediction period so as to obtain a prediction result; and
and the control module is used for controlling the running of the vehicle according to the prediction result.
8. The apparatus of claim 7, further comprising:
a prediction time domain obtaining module for obtaining a prediction time domain parameter NpWherein, the N ispIs a positive integer;
a functional relationship determination module for determining a functional relationship between a rate in each of the plurality of rate intervals and a predicted distance; and
a rule configuration module for configuring the time domain parameter N according to the function relationship between the rate and the prediction distancepAnd configuring a period prediction rule corresponding to each rate interval.
9. A computing device, comprising:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1 to 6.
10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 6.
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