CN113859236A - Car following control system, car, method, device, equipment and storage medium - Google Patents

Car following control system, car, method, device, equipment and storage medium Download PDF

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CN113859236A
CN113859236A CN202111042596.8A CN202111042596A CN113859236A CN 113859236 A CN113859236 A CN 113859236A CN 202111042596 A CN202111042596 A CN 202111042596A CN 113859236 A CN113859236 A CN 113859236A
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acceleration
vehicle
front vehicle
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prediction
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蒙万佳
张�杰
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China Automotive Innovation 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/14—Adaptive cruise control
    • B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
    • B60W30/165—Automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
    • 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
    • B60W40/107—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
    • 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
    • B60W2520/105—Longitudinal acceleration

Abstract

The present application relates to the field of adaptive cruise control technologies, and in particular, to a vehicle following control system, a vehicle, a method, an apparatus, a device, and a storage medium. The system includes a forward vehicle acceleration prediction module that includes: the front vehicle acceleration collecting unit is used for continuously collecting the acceleration data of the front vehicle; the parameter identification unit is used for determining a front vehicle acceleration prediction model according to the acceleration data of the front vehicle; and the front vehicle acceleration prediction unit is used for determining a front vehicle acceleration time sequence in a prediction time domain according to the front vehicle acceleration prediction model. The acceleration time sequence of a limited step length in the future of the front vehicle is predicted by continuously collecting and analyzing the acceleration data of the front vehicle, so that the accurate prediction of the acceleration of the front vehicle is realized. And applying the predicted front vehicle acceleration sequence to model prediction control, thereby improving the control effect.

Description

Car following control system, car, method, device, equipment and storage medium
Technical Field
The present application relates to the field of adaptive cruise control technologies, and in particular, to a vehicle following control system, a vehicle, a method, an apparatus, a device, and a storage medium.
Background
Adaptive Cruise Control (ACC) is an intelligent automatic Control technology, which combines safe vehicle distance maintenance Control on the basis of the traditional constant-speed Cruise Control, carries out forward running environment monitoring through an environmental information sensing module, and automatically follows a front vehicle to run by a certain Control strategy when no vehicle is in the front or the front vehicle is far out of the safe vehicle distance at a constant speed with a preset vehicle speed, and when the vehicle is within the monitoring range and the vehicle speed of the front vehicle is less than the vehicle speed of the Cruise. When the vehicle runs following the front vehicle, the vehicle speed is adjusted according to the following vehicle distance mainly under the condition that the vehicle safety distance is certain, so that the running speed of the vehicle is controlled.
In the prior art, the mainstream control framework of the current car following control model is hierarchical control, wherein an upper controller takes a workshop state as an input and then outputs an expected acceleration. And the lower layer controller takes the expected acceleration as input and then converts the expected acceleration into the brake wheel cylinder pressure and the throttle opening of the controlled vehicle, thereby achieving the purpose of following the vehicle. However, in the algorithm for calculating the desired acceleration, the preceding vehicle acceleration term in the state equation is generally regarded as a measurable disturbance term, which brings about a drawback. First, in the real world, the acceleration of the leading vehicle shows a certain correlation in a period of time, and it is not reasonable to consider the acceleration of the leading vehicle as random noise. Second, the design of existing model predictive controllers mostly assumes that the measurable disturbance term variation in the prediction time domain is zero, which assumption reduces the effectiveness of the controller. This makes the expected acceleration calculation error large, which in turn causes the vehicle control to be out of compliance with the actual operating requirements, presenting a safety risk.
Disclosure of Invention
The invention aims to solve the technical problem that the calculation error is larger because the expected acceleration of the vehicle calculated by the conventional vehicle following model does not accord with the actual condition.
In order to solve the technical problem, in a first aspect, an embodiment of the present application discloses a following control system, where the system includes a preceding vehicle acceleration prediction module, where the preceding vehicle acceleration prediction module includes:
the front vehicle acceleration collecting unit is used for continuously collecting the acceleration data of the front vehicle;
the parameter identification unit is used for determining a front vehicle acceleration prediction model according to the acceleration data of the front vehicle;
and the front vehicle acceleration prediction unit is used for determining a front vehicle acceleration time sequence in a prediction time domain according to the front vehicle acceleration prediction model.
Further, the system further comprises a vehicle acceleration determining module, and the vehicle acceleration determining module is used for determining the expected acceleration of the vehicle according to the time sequence of the acceleration of the vehicle ahead and the vehicle following state information.
Further, the system further comprises an operation control determination module for determining a brake cylinder pressure and a throttle opening of the own vehicle according to the desired acceleration of the own vehicle.
In a second aspect, the embodiment of the present application discloses a vehicle, which includes the following control system as described above.
In a third aspect, an embodiment of the present application discloses a car following control method, including:
acquiring historical acceleration data of a front vehicle in a first preset time period; the first preset time period is a preset time domain from the current moment to a preset historical moment;
determining a preceding vehicle acceleration prediction model according to the historical acceleration data;
predicting a preceding vehicle acceleration time sequence in a second preset time period according to the preceding vehicle acceleration prediction model; the second preset time period is a preset time domain from the current moment to a preset future moment;
and determining the expected acceleration of the self vehicle according to the acceleration time sequence of the front vehicle.
Further, the determining the expected acceleration of the vehicle according to the time series of the acceleration of the vehicle ahead comprises:
acquiring car following state information;
and determining the expected acceleration of the self vehicle according to the acceleration time sequence of the front vehicle and the following state information.
Further, after determining the preceding vehicle acceleration prediction model according to the historical acceleration data, the method further includes:
acquiring the acceleration data of the previous vehicle at the current moment;
and correcting the preceding vehicle acceleration prediction model according to the preceding vehicle acceleration data at the current moment.
In a fourth aspect, an embodiment of the present application discloses a following control device, the device includes:
the acquisition module is used for acquiring historical acceleration data of a front vehicle in a first preset time period; the first preset time period is a preset time domain from the current time to the historical time;
the front vehicle acceleration prediction model determining module is used for determining a front vehicle acceleration prediction model according to the historical acceleration data;
the prediction module is used for predicting the time sequence of the acceleration of the front vehicle in a second preset time period according to the prediction model of the acceleration of the front vehicle; the second preset time period is a preset time domain from the current time to the future time;
and the expected acceleration determining module is used for determining the expected acceleration of the vehicle according to the time sequence of the acceleration of the vehicle ahead.
In an optional embodiment, the vehicle desired acceleration determination module includes:
the following state information acquisition unit is used for acquiring following state information;
and the expected acceleration determining unit is used for determining the expected acceleration of the vehicle according to the time sequence of the acceleration of the vehicle ahead and the following state information.
In an optional embodiment, the apparatus further comprises:
the front vehicle acceleration data acquisition unit is used for acquiring the front vehicle acceleration data at the current moment;
and the preceding vehicle acceleration prediction model correction unit is used for correcting the preceding vehicle acceleration prediction model according to the preceding vehicle acceleration data at the current moment.
In a fifth aspect, an embodiment of the present application discloses an electronic device, where the device includes a processor and a memory, where the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded by the processor and executes the following control method.
In a sixth aspect, an embodiment of the present application discloses a computer-readable storage medium, where at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the car following control method as described above.
The following control system, the following vehicle, the following method, the following device, the following equipment and the following storage medium have the following technical effects:
the front vehicle acceleration prediction module comprises a front vehicle acceleration collection unit, a parameter identification unit and a front vehicle acceleration prediction unit, and is used for continuously collecting front vehicle acceleration data for analysis so as to predict an acceleration time sequence with a limited step length in the future of the front vehicle and realize accurate prediction of the front vehicle acceleration. And applying the predicted front vehicle acceleration sequence to model prediction control, thereby improving the control effect.
Drawings
In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic structural diagram of a car following control system according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a following dynamics logic of a following control system according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a following control method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a car following control device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The adaptive cruise control system is an intelligent automatic control system. In the running process of the vehicle, a vehicle distance sensor installed at the front part of the vehicle continuously scans the road in front of the vehicle, and meanwhile, a wheel speed sensor collects a vehicle speed signal. When the distance to the vehicle ahead is too small, the ACC control unit may appropriately brake the wheels and reduce the output of the engine by coordinating with an Electronic Stability Program (ESP) and an engine control system, so that the vehicle is always kept at a safe distance from the vehicle ahead.
The main flow control framework of the current following vehicle control model is hierarchical control, wherein an upper controller takes a workshop state as input and then outputs expected acceleration. And the lower layer controller takes the expected acceleration as input and then converts the expected acceleration into the brake wheel cylinder pressure and the throttle opening of the controlled vehicle, thereby achieving the purpose of following the vehicle.
There are many ways to implement the upper layer controller, and one of them is to use a model predictive control method. The method firstly models following behaviors of a self vehicle and a front vehicle, and the model is called as a following system dynamic model. Defining the inter-vehicle distance difference and the relative speed as follows:
Figure BDA0003249927180000061
d (t) is the relative distance between the preceding vehicle and the following vehicle, ddes(t) is the desired relative distance, vp(t) is the front vehicle speed, vf(t) is the speed of the own vehicle. If the constant time interval strategy is adopted, ddes(t) is:
ddes(t)＝τhvf(t)+d0
the derivation of the two sides of the system of equations is:
Figure BDA0003249927180000062
af(t) is the acceleration of the vehicle, ap(t) is the front vehicle acceleration. Because of the hysteresis of the structures such as the driving system, the braking system and the like, the expected acceleration and the actual acceleration can be expressed as a first-order inertia link, and therefore, the following can be obtained:
Figure BDA0003249927180000063
the system state equation can be finally obtained as follows:
Figure BDA0003249927180000064
wherein:
Figure BDA0003249927180000065
B＝[0 0 K/τ]T
Γ＝[0 1 0]T
x＝[Δd Δv af]T
u＝afdes
υ＝ap
and then selecting a control time domain and a prediction time domain, designing a cost function and a constraint according to requirements, and finally, settling an equation into a quadratic programming problem and solving by using methods such as an interior point method and the like. In the solving process, the acceleration a of the front vehiclepIs a significant influence factor which can not be ignored, and whether the acceleration of the front vehicle is accurate or not can directly influence the solved expected acceleration a of the self vehiclefdesAccuracy and further influence the car following control effect.
In order to ensure the calculation accuracy of the expected acceleration of the vehicle, improve the vehicle following control effect, and ensure the vehicle following safety, an embodiment of the present application provides a vehicle following control system, please refer to fig. 1, fig. 1 is a schematic structural diagram of the vehicle following control system provided by the embodiment of the present application, the system includes a preceding vehicle acceleration prediction module, and the preceding vehicle acceleration prediction module includes:
and the front vehicle acceleration collecting unit is used for continuously collecting the acceleration data of the front vehicle.
And the parameter identification unit is used for determining a front vehicle acceleration prediction model according to the acceleration data of the front vehicle.
And the front vehicle acceleration prediction unit is used for determining a front vehicle acceleration time sequence in a prediction time domain according to the front vehicle acceleration prediction model.
In the following control system in the embodiment of the application, a preceding vehicle acceleration prediction module is arranged to predict the acceleration of the preceding vehicle. The front vehicle acceleration prediction module comprises a front vehicle acceleration collection unit, a parameter identification unit and a front vehicle acceleration prediction unit.
The front vehicle acceleration collecting unit continuously collects current acceleration data of a front vehicle and stores the collected front vehicle acceleration data. When the stored data exceeds a certain amount, the latest previous groups of data are reserved. In an alternative embodiment, the collected vehicle acceleration before may be set to a collection time domain, for example, within 5 minutes before the current time, that is, from the historical vehicle acceleration before 5 minutes to the current time, the vehicle acceleration before collecting unit collects and stores the historical vehicle acceleration data within the 5 minutes, and continuously updates the collected vehicle acceleration data within the collection time domain in real time as the time progresses. The front vehicle acceleration collecting unit collects front vehicle acceleration data through a vehicle-mounted data collecting sensor. Optionally, the vehicle-mounted data acquisition sensor may be an ultrasonic radar, a millimeter wave radar, a laser radar, or the like.
The parameter identification unit is internally provided with a front vehicle acceleration identification model, and the parameter identification unit identifies historical front vehicle acceleration data collected by the front vehicle acceleration collection unit and determines a front vehicle acceleration prediction model according to the front vehicle acceleration identification model. The preceding vehicle acceleration identification model may be a regression prediction model, such as an ARIMA model, or may be other statistical models, which are not limited herein, and may be selected by those skilled in the art according to actual situations. After the front vehicle acceleration identification model is selected, the structure of the model is determined accordingly, and the front vehicle acceleration data can be identified in real time by inputting the front vehicle acceleration data into the model, so that the parameter identification unit can identify the parameters in real time to adapt to the continuously changed vehicle following condition.
As an example, the parametric front vehicle acceleration identification model is:
Figure BDA0003249927180000081
in the recognition model, ak|k+1、ak、ak|k-1For the historical preceding vehicle acceleration data collected by the preceding vehicle acceleration collecting unit,
Figure BDA0003249927180000082
to identifyAnd identifying a parameter, wherein the parameter is not determined but determined according to the acceleration data of the pre-history vehicle. When in use
Figure BDA0003249927180000083
And obtaining a front vehicle acceleration prediction model after the determination. It is clear that,
Figure BDA0003249927180000084
not fixed, but changes over time. With the continuous update of the historical preceding vehicle acceleration data collected by the preceding vehicle acceleration collecting unit, the parameters identified based on the historical preceding vehicle acceleration data
Figure BDA0003249927180000085
As well as over time. Therefore, the preceding vehicle acceleration prediction model is also continuously updated and changed, so that the preceding vehicle acceleration data closest to the real situation can be predicted according to the latest preceding vehicle acceleration prediction model. In this embodiment, the method for parameter identification may be a least square method or other statistical principles, and is not limited herein.
The preceding vehicle acceleration prediction unit predicts a preceding vehicle acceleration time series of the preceding vehicle in a preset prediction time domain by using the preceding vehicle acceleration prediction model determined by the parameter identification unit. The prediction time domain is a time period from the current time to a future time, for example, a prediction time domain is from the current time to 5 minutes in the future. Specifically, if the preceding vehicle acceleration prediction model determined by the parameter identification unit is an ARIMA (2,1) model, the preceding vehicle acceleration prediction equation is as follows:
Figure BDA0003249927180000086
Figure BDA0003249927180000087
Figure BDA0003249927180000088
…
wherein, ak|k+1、ak|k+2、ak|k+2The predicted front vehicle acceleration at the time k +1, k +2, and k +3 is the predicted front vehicle acceleration at the time k.
If the prediction time domain of the front vehicle acceleration prediction equation is P, the front vehicle acceleration pre-sequencing is as follows: a isk|k+1、ak|k+2、ak|k+2，…，ak|k+pAnd P predicted values are obtained.
Fig. 2 is a schematic view of following dynamics logic of a following control system according to an embodiment of the present application, in which afIndicating acceleration of the vehicle, apIndicating the acceleration of the front vehicle, phipRepresenting the result of parameter identification, Ap representing the predicted sequence of acceleration of the leading vehicle, adesIndicating a desired acceleration, Pcon indicating a wheel cylinder pressure, αthRepresents the throttle opening degree, Δ d represents the inter-vehicle distance, and Δ v represents the inter-vehicle relative speed. As shown in fig. 2, the preceding vehicle acceleration collecting unit is configured to collect preceding vehicle acceleration data in real time, the parameter identifying unit is configured to perform regression parameter identification on historical preceding vehicle acceleration data to obtain a preceding vehicle acceleration regression prediction model, and the preceding vehicle acceleration predicting unit is configured to predict the preceding vehicle acceleration according to the preceding vehicle acceleration regression prediction model.
In the embodiment of the application, the system further comprises a self-vehicle acceleration determining module and an operation control determining module, wherein the self-vehicle acceleration determining module is used for determining the expected acceleration of the self-vehicle according to the acceleration time sequence of the front vehicle and the following vehicle state information. The operation control determination module is used for determining the brake wheel cylinder pressure and the throttle opening of the bicycle according to the expected acceleration of the bicycle.
In the embodiment of the application, after the preceding vehicle acceleration prediction module predicts the preceding vehicle acceleration in the prediction time domain, the prediction result of the preceding vehicle acceleration and following vehicle state information such as a vehicle-to-vehicle distance, a vehicle-to-vehicle relative speed, a vehicle-to-vehicle relative distance and the like are input into the own vehicle acceleration determination module as initial conditions, and the model prediction controller in the own vehicle acceleration determination module obtains the expected acceleration of the own vehicle according to the information. And then the operation control module, namely a lower controller, converts the expected acceleration of the vehicle into the brake wheel cylinder pressure and the throttle opening of the vehicle, thereby achieving the purpose of following the vehicle.
In the following control system in the embodiment of the application, a time sequence Ap of the acceleration of the leading vehicle in the prediction time domain is deduced by using a plurality of latest acceleration data of the leading vehicle collectedk|k+1,ak|k+2,ak|k+2，…，ak|k+p}. And then determining the expected acceleration of the self vehicle according to the predicted time sequence of the acceleration of the front vehicle and the following vehicle state information. Because the acceleration of the front vehicle has certain correlation in a period of time, the predicted acceleration data of the front vehicle predicted according to the latest acceleration data of the front vehicle has higher accuracy and better accords with the actual situation. Compared with the traditional model prediction control, when the expected acceleration of the vehicle is determined, because the input information also comprises a preceding vehicle acceleration prediction sequence, the more ideal expected acceleration of the vehicle can be obtained, and further the ideal brake wheel cylinder pressure and the ideal throttle opening degree can be obtained, and the higher vehicle control effect can be realized. The method and the device for mining the historical acceleration information of the front vehicle from the angle of statistics improve the effect of vehicle following control.
The embodiment of the application also provides a vehicle, and the vehicle comprises the vehicle following control system.
The vehicle of this application embodiment include as above with car control system to can realize better with car control effect, improve with the security of car.
The embodiment of the application also provides a car following control method, fig. 3 is a flow chart of the car following control method provided by the embodiment of the application, the description provides the method operation steps as the embodiment or the flow chart, but more or less operation steps can be included based on conventional or non-creative labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In practice, the system or server product may be implemented in a sequential or parallel manner according to the embodiments or methods shown in the figures, for example, in a parallel processor or multi-threaded environment. Specifically, as shown in fig. 3, the method may include:
s301: acquiring historical acceleration data of a front vehicle in a first preset time period.
In the embodiment of the application, the following control system collects the acceleration data of the front vehicle in real time through the acceleration collecting unit of the front vehicle. The front vehicle refers to a vehicle directly in front of the controlled vehicle and adjacent to the controlled vehicle. The first preset time period is a preset time domain from the current moment to a preset historical moment. For example, within 5 minutes from the current time onward. The historical acceleration data in the first preset time period collected by the front vehicle acceleration collecting unit are multiple and correspond to time.
S303: and determining a preceding vehicle acceleration prediction model according to the historical acceleration data.
In the embodiment of the application, a parameter identification unit is arranged in the following vehicle control system, and the parameter identification unit identifies historical front vehicle acceleration data collected by a front vehicle acceleration collection unit and determines a front vehicle acceleration prediction model according to a front vehicle acceleration identification model.
S305: and predicting the time sequence of the acceleration of the front vehicle in a second preset time period according to the prediction model of the acceleration of the front vehicle.
In the embodiment of the application, a preceding vehicle acceleration prediction unit is arranged in the following vehicle control system, and the preceding vehicle acceleration prediction unit predicts a preceding vehicle acceleration time sequence of a preceding vehicle in a second preset time period by using a preceding vehicle acceleration prediction model determined by the parameter identification unit. The second preset time period is a preset time period from the current time to a preset future time, for example, a predicted time period from the current time to the future 5 minutes.
S307: and determining the expected acceleration of the self vehicle according to the acceleration time sequence of the front vehicle.
In the embodiment of the application, a vehicle following control system is provided with a vehicle acceleration determining module, and the vehicle acceleration determining module is used for determining the expected acceleration of the vehicle. Specifically, the vehicle following acceleration determining module determines the expected acceleration of the vehicle according to the time sequence of the acceleration of the vehicle ahead and the vehicle following state information by acquiring the vehicle following state information. In an optional embodiment, the vehicle acceleration determining module obtains a prediction result of a preceding vehicle acceleration and following state information such as a vehicle-to-vehicle distance, a vehicle-to-vehicle relative speed, a following relative distance and the like, and the model prediction controller in the vehicle acceleration determining module obtains a desired vehicle acceleration according to the information.
In the embodiment of the application, because the acceleration data of the front vehicle is in a constantly changing state, in order to ensure the accuracy of the acceleration prediction of the front vehicle, the parameter identification unit needs to constantly modify the acceleration prediction model of the front vehicle so as to adapt to a constantly changing actual following scene. In an optional embodiment, after determining the preceding vehicle acceleration prediction model according to the historical acceleration data, the method further includes: and acquiring the acceleration data of the previous vehicle at the current moment, and correcting the acceleration prediction model of the previous vehicle by the parameter identification unit according to the acceleration data of the previous vehicle at the current moment.
In the embodiment of the application, the following control system is further provided with an operation control module, and after the expected acceleration of the vehicle is determined, the operation control module obtains the expected acceleration of the vehicle and converts the expected acceleration of the vehicle into the brake wheel cylinder pressure and the throttle opening of the vehicle, so that the purpose of following the vehicle is achieved by controlling the vehicle.
The following control method in the embodiment of the invention can predict the acceleration data of the preceding vehicle within a certain time period in the future by collecting the acceleration data of the preceding vehicle in real time and determining the acceleration prediction model of the preceding vehicle according to the acceleration data of the preceding vehicle, so that the ideal expected acceleration of the vehicle can be determined according to the acceleration of the preceding vehicle, and the vehicle is controlled according to the brake cylinder pressure and the throttle opening of the vehicle converted from the expected acceleration of the vehicle, thereby realizing better following control effect.
The embodiment of the present application further provides a following control device, and fig. 4 is a schematic structural diagram of the following control device provided in the embodiment of the present application, and as shown in fig. 4, the following control device includes:
the acquisition module is used for acquiring historical acceleration data of a front vehicle in a first preset time period; the first preset time period is a preset time domain from the current time to the historical time.
And the front vehicle acceleration prediction model determining module is used for determining a front vehicle acceleration prediction model according to the historical acceleration data.
The prediction module is used for predicting the time sequence of the acceleration of the front vehicle in a second preset time period according to the prediction model of the acceleration of the front vehicle; the second preset time period is a preset time domain from the current time to the future time.
And the expected acceleration determining module is used for determining the expected acceleration of the vehicle according to the acceleration time sequence of the vehicle ahead.
In an alternative embodiment, the desired acceleration determination module includes:
and the car following state information acquisition unit is used for acquiring the car following state information.
And the expected acceleration determining unit is used for determining the expected acceleration of the vehicle according to the acceleration time sequence of the vehicle ahead and the following state information.
In an alternative embodiment, the apparatus further comprises:
and the front vehicle acceleration data acquisition unit is used for acquiring the front vehicle acceleration data at the current moment.
And the front vehicle acceleration prediction model correction unit is used for correcting the front vehicle acceleration prediction model according to the front vehicle acceleration data at the current moment.
The device and method embodiments in the embodiments of the present application are based on the same application concept.
The embodiment of the application discloses electronic equipment, and the equipment comprises a processor and a memory, wherein at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded by the processor and executes the vehicle meeting method.
In the embodiment of the present application, the memory may be used to store software programs and modules, and the processor executes various functional applications and data processing by operating the software programs and modules stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs needed by functions and the like; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory may also include a memory controller to provide the processor access to the memory. As an example, the device is an in-vehicle computer, such as an ECU.
The embodiment of the application discloses a computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to realize the vehicle meeting method.
In an embodiment of the present application, the storage medium may be located in at least one network client of a plurality of network clients of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a U disk, a Read-only Memory ROM, a Read-only Memory, a random access Memory RAM, a random access Memory, a removable hard disk, a magnetic disk or an optical disk.
The following control system, the following vehicle, the following method, the following device, the following equipment and the following storage medium have the advantages that:
and (3) continuously collecting the acceleration data of the front vehicle to perform regression analysis so as to predict the acceleration time sequence of the front vehicle with a finite step length in the future. Meanwhile, in order to deal with the continuously changed driving environment, real-time parameter identification is carried out on the regression model, so that the previous vehicle acceleration regression prediction model is updated in real time. The predicted front vehicle acceleration sequence is applied to model prediction control, and the following control effect can be improved.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (10)

1. A vehicle following control system, said system comprising a preceding vehicle acceleration prediction module, said preceding vehicle acceleration prediction module comprising:
the front vehicle acceleration collecting unit is used for continuously collecting the acceleration data of the front vehicle;
the parameter identification unit is used for determining a front vehicle acceleration prediction model according to the acceleration data of the front vehicle;
and the front vehicle acceleration prediction unit is used for determining a front vehicle acceleration time sequence in a prediction time domain according to the front vehicle acceleration prediction model.
2. The vehicle following control system according to claim 1, further comprising a vehicle acceleration determination module configured to determine a desired acceleration of the vehicle from the time series of acceleration of the vehicle ahead and the vehicle following state information.
3. The follow-up control system according to claim 2, further comprising an operation control determination module for determining a brake cylinder pressure and a throttle opening degree of the own vehicle according to the desired acceleration of the own vehicle.
4. A vehicle characterized by comprising the following control system according to any one of claims 1 to 3.
5. A car following control method, characterized by comprising:
acquiring historical acceleration data of a front vehicle in a first preset time period; the first preset time period is a preset time domain from the current moment to a preset historical moment;
determining a preceding vehicle acceleration prediction model according to the historical acceleration data;
predicting a preceding vehicle acceleration time sequence in a second preset time period according to the preceding vehicle acceleration prediction model; the second preset time period is a preset time domain from the current moment to a preset future moment;
and determining the expected acceleration of the self vehicle according to the acceleration time sequence of the front vehicle.
6. The following control method according to claim 5, wherein the determining of the desired acceleration of the own vehicle from the time series of the acceleration of the preceding vehicle includes:
acquiring car following state information;
and determining the expected acceleration of the self vehicle according to the acceleration time sequence of the front vehicle and the following state information.
7. The vehicle following control method according to claim 6, wherein after determining a preceding vehicle acceleration prediction model from the historical acceleration data, further comprising:
acquiring the acceleration data of the previous vehicle at the current moment;
and correcting the preceding vehicle acceleration prediction model according to the preceding vehicle acceleration data at the current moment.
8. A car following control apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring historical acceleration data of a front vehicle in a first preset time period; the first preset time period is a preset time domain from the current time to the historical time;
the front vehicle acceleration prediction model determining module is used for determining a front vehicle acceleration prediction model according to the historical acceleration data;
the prediction module is used for predicting the time sequence of the acceleration of the front vehicle in a second preset time period according to the prediction model of the acceleration of the front vehicle; the second preset time period is a preset time domain from the current time to the future time;
and the expected acceleration determining module is used for determining the expected acceleration of the vehicle according to the time sequence of the acceleration of the vehicle ahead.
9. An electronic device, characterized in that the device comprises a processor and a memory, wherein at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded by the processor and executes the following control method according to any one of claims 5 to 7.
10. A computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the following control method according to any one of claims 5 to 7.
CN202111042596.8A 2021-09-07 2021-09-07 Car following control system, car, method, device, equipment and storage medium Pending CN113859236A (en)

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