CN113232652B

CN113232652B - Vehicle cruise control method and system based on kinematics model

Info

Publication number: CN113232652B
Application number: CN202110669399.2A
Authority: CN
Inventors: 程德心; 张伟; 汤戈; 蔡幼波
Original assignee: Wuhan Kotei Informatics Co Ltd
Current assignee: Wuhan Kotei Informatics Co Ltd
Priority date: 2021-06-16
Filing date: 2021-06-16
Publication date: 2022-10-25
Anticipated expiration: 2041-06-16
Also published as: CN113232652A

Abstract

The invention relates to a vehicle cruise control method based on a kinematic model, which comprises the steps of obtaining a state variable of a target vehicle motion and determining a first kinematic model of the target vehicle according to the state variable, wherein the state variable comprises speed, acceleration and turning rate; correcting the state variable of the target vehicle motion according to a Kalman filtering algorithm; predicting a second kinematic model of the target vehicle according to the corrected state variable of the motion of the target vehicle; and adjusting the automatic cruise control strategy of the self vehicle according to the second motion model. The invention utilizes a vehicle state estimation method, adopts a plurality of different front vehicle kinematic models for estimation, and combines Kalman filtering to solve the problems of controllability and fuel economy when the adaptive cruise tracks the front vehicle.

Description

Vehicle cruise control method and system based on kinematics model

Technical Field

The invention belongs to the technical field of automatic driving of automobiles, and particularly relates to a vehicle cruise control method and system based on a kinematic model.

Background

With the rapid extension of the highway network construction in China, the automatic cruise control also has wide development and application prospects. The development of technology makes the corresponding electronic technology more and more widely applied to automobiles, and the degree of automobile electronization is higher and higher. Particularly, after the microcontroller enters the field of automobile control, epoch-making changes are brought to automobile development, and the dynamic property, the operation stability, the safety, the fuel economy and the environmental friendliness of the automobile are greatly improved. In inland countries, there are many opportunities for driving automobiles for long distance, and the frequency and range for changing the speed of the automobile are small when the automobile runs on an expressway, so that the automobile can run at a stable speed.

After the automobile cruise control system is adopted, when the automobile runs on the expressway for a long time, a driver does not need to control the accelerator pedal any more. Therefore, the burden of a driver is relieved, traffic accidents are reduced or avoided, but the traditional cruise system is poor in control performance, and low in energy consumption and efficiency of fuel oil.

Disclosure of Invention

In order to improve the operability and energy consumption efficiency of a vehicle cruise system, the invention provides a vehicle cruise control method based on a kinematic model in a first aspect, which comprises the following steps: acquiring state variables of the motion of a target vehicle and determining a first kinematic model of the target vehicle according to the state variables, wherein the state variables comprise speed, acceleration and turning rate; correcting the state variable of the target vehicle motion according to a Kalman filtering algorithm; predicting a second kinematic model of the target vehicle according to the corrected state variable of the motion of the target vehicle; and adjusting the automatic cruise control strategy of the self vehicle according to the second motion model.

In some embodiments of the invention, the kinematic models include a constant velocity model, a constant acceleration model, a constant turn and constant velocity model, and a constant turn and constant acceleration model.

In some embodiments of the present invention, said correcting the state variable of the target vehicle motion according to the kalman filter algorithm comprises the steps of: obtaining prior distribution of state variables according to a first kinematic model of a target vehicle; sampling from the prior distribution to obtain a sigma point set consisting of a plurality of sigma points; the multiple sigma points belong to the same Gaussian distribution, and the mean value and covariance of the Gaussian distribution are the same as the prior distribution of the state variables; and predicting the mean and the variance of the sigma point set, and updating or correcting the state variable of the motion of the target vehicle according to the mean and the variance.

Further, the predicting the mean and variance of the sigma point set and updating or correcting the state variable of the motion of the target vehicle according to the mean and variance comprises the following steps:

predicting a mean value and a variance corresponding to next state distribution of the target vehicle according to the mean value of the sigma point set and a preset state transfer function variance to obtain a predicted measured value of the next state of the target vehicle;

and fusing the prior value of the state variable, the actual measurement value and the predicted measurement value of the next state of the target vehicle to obtain the state variable of the next state of the target vehicle.

In some embodiments of the invention, the cruise control strategy comprises controlling the speed and/or acceleration of the vehicle in a curve and/or on a ramp to reduce the transient fuel consumption of the vehicle.

Further, the control of the speed and/or acceleration of the vehicle under a curve and/or a slope road condition comprises the following steps:

obtaining curve or ramp information of a road ahead by using a high-precision map;

and automatically and dynamically adjusting the speed and/or acceleration of the vehicle to keep the speed and/or acceleration change stable and below a threshold value according to the state variable determined by the second motion model and the curve or ramp information.

In a second aspect of the invention, a vehicle cruise control system based on a kinematic model is provided, which comprises an acquisition module, a correction module, a prediction module and an adjustment module, wherein the acquisition module is used for acquiring state variables of motion of a target vehicle and determining a first kinematic model of the target vehicle according to the state variables, wherein the state variables comprise speed, acceleration and turning rate; the correction module is used for correcting the state variable of the target vehicle motion according to a Kalman filtering algorithm; the prediction module is used for predicting a second kinematic model of the target vehicle according to the corrected state variable of the motion of the target vehicle; and the adjusting module is used for adjusting the automatic cruise control strategy of the vehicle according to the second motion model.

Further, the correction module comprises an acquisition unit, a sampling unit and an updating unit, wherein the acquisition unit is used for acquiring prior distribution of the state variables according to the first kinematic model of the target vehicle; the sampling unit is used for sampling from the prior distribution to obtain a sigma point set consisting of a plurality of sigma points; and the updating unit is used for predicting the mean value and the variance of the sigma point set and updating or correcting the state variable of the motion of the target vehicle according to the mean value and the variance.

In a third aspect of the present invention, there is provided an electronic device comprising: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the kinematics model-based vehicle cruise control method provided in the first aspect of the invention.

In a fourth aspect of the present invention, a computer-readable medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the kinematic model-based vehicle cruise control method provided in the first aspect of the present invention.

The invention has the beneficial effects that:

1. the method utilizes a vehicle state estimation method, adopts a plurality of different front vehicle kinematic models for estimation, and combines Kalman filtering to solve the problems of controllability and fuel economy when the adaptive cruise tracks the front vehicle;

2. the burden of the driver is reduced, so that the occurrence of traffic accidents is reduced or avoided;

3. unnecessary vehicle speed change is reduced, fuel can be saved to the maximum extent, and exhaust pollution is reduced; 4. by switching different motion models, the comfort of the driver is improved.

Drawings

FIG. 1 is a basic flow diagram of a kinematic model-based vehicle cruise control method in some implementations of the invention;

FIG. 2 is a schematic diagram of a motion model and a transformation relationship in the present invention;

FIG. 3 is a schematic illustration of an auto cruise control strategy for an own vehicle for a kinematic model based vehicle cruise control method in some implementations of the present invention;

FIG. 4 is a schematic diagram of a basic configuration of a kinematic model-based vehicle cruise control system in some implementations of the invention;

FIG. 5 is a schematic diagram of a kinematic model-based vehicle cruise control system in accordance with some embodiments of the present invention;

FIG. 6 is a schematic diagram of an electronic device in some implementations of the invention.

Detailed Description

The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.

Referring to fig. 1, in a first aspect of the invention, there is provided a kinematic model-based vehicle cruise control method, comprising: s100, acquiring a state variable of the motion of a target vehicle and determining a first kinematic model of the target vehicle according to the state variable, wherein the state variable comprises speed, acceleration and turning rate; s200, correcting a state variable of the motion of the target vehicle according to a Kalman filtering algorithm; s300, predicting a second kinematic model of the target vehicle according to the corrected state variable of the target vehicle; and S400, adjusting the automatic cruise control strategy of the vehicle according to the second motion model.

It is understood that the target vehicles mentioned above generally refer to one or more target vehicles located in front of the own vehicle, and preferably, the speed and/or acceleration of the target vehicles relative to the own vehicle is constant and within the sensor measurement range of the own vehicle.

Referring to fig. 2, in some embodiments of the present invention, to simplify the solution and improve the real-time performance of the state variable measurement, different motion states of the vehicle in the road are divided: the kinematic models include a constant velocity model, a constant acceleration model, constant turn and constant velocity models, and constant turn and constant acceleration models. Specifically, a primary motion model and a secondary motion model are included, wherein: the primary motion model (linear motion model) includes a Constant Velocity (CV) model and a Constant Acceleration (CA) model;

the secondary motion model includes a Constant Turn Rate and Velocity (CTRV) model, and a Constant Turn Rate and Acceleration (CTRA) model. For example, the constant turning rate and speed model CVTR is a general form of CV, and when the angular velocity ω is equal to 0, it is CV. The CTRV model assumes that a moving object is traveling in a straight line while still being able to move at a fixed turn rate and constant speed.

Optionally, the CTRV state variable is represented by a vector or a matrix:

where x represents a state variable, px, py, v, ψ, and

respectively representing the abscissa, the ordinate, the speed magnitude, the yaw angle and the angular speed of the target vehicle in a measuring space, namely the change rate of the speed and the angular speed is 0 under a constant turning rate; accordingly, the state variable of the next state of the target vehicle is represented as:

in step S200 of some embodiments of the present invention, the correcting the state variable of the target vehicle motion according to the kalman filter algorithm includes the steps of: obtaining prior distribution of state variables according to a first kinematic model of a target vehicle; sampling from the prior distribution to obtain a sigma point set consisting of a plurality of sigma points; the multiple sigma points belong to the same Gaussian distribution, and the mean value and covariance of the Gaussian distribution are the same as the prior distribution of the state variables; and predicting the mean value and the variance of the sigma point set, and updating or correcting the state variable of the motion of the target vehicle according to the mean value and the variance.

Further, the predicting the mean and variance of the sigma point set and updating or correcting the state variable of the target vehicle motion according to the mean and variance comprises the following steps: predicting a mean value and a variance corresponding to next state distribution of the target vehicle according to the mean value of the sigma point set and a preset state transfer function variance to obtain a predicted measured value of the next state of the target vehicle; and fusing the prior value of the state variable, the actual measurement value and the predicted measurement value of the next state of the target vehicle to obtain the state variable of the next state of the target vehicle.

It will be appreciated that the sigma point set state transfer function is determined by a first kinematic model of the target vehicle, i.e. the transfer function describes the state variables at the next state in relation to the current state variables.

Referring to fig. 3, in step S400 of some embodiments of the present invention, the cruise control strategy includes controlling the speed and/or acceleration of the own vehicle under a curve and/or a slope to reduce the transient fuel consumption of the own vehicle.

Further, the control of the speed and/or acceleration of the vehicle under a curve and/or a slope road condition comprises the following steps: acquiring curve or ramp information of a road ahead by using a high-precision map; and automatically and dynamically adjusting the speed and/or acceleration of the vehicle to keep the speed and/or acceleration change stable and below a threshold value according to the state variable determined by the second motion model and the curve or ramp information.

Specifically, a curve economy strategy: (1) bending: a speed reduction strategy in advance; (2) bending: an early acceleration strategy; the ramp economic strategy is as follows: (1) ascending a slope: an early acceleration strategy; (2) bending: and (4) an early deceleration strategy. That is, the speed is kept constant on a straight road, the acceleration is kept constant in the deceleration or acceleration stage, the length of the uphill slope and the length of the downhill slope are represented by d and a, respectively, and the corresponding speed change is Δ V _d And Δ V _a And (4) showing.

Because transient fuel consumption is generally 6% -30% higher than steady-state fuel consumption, curves and ramps are typical transient scenes. By combining a high-precision map, the curvature, the length and the up-down gradient of the curve in front can be obtained in advance, so that the fuel energy consumption efficiency and the economy of the vehicle in the cruise control process are improved.

Example 2

Referring to fig. 4, in a second aspect of the present invention, there is provided a vehicle cruise control system 1 based on a kinematic model, comprising an obtaining module 11, a correcting module 12, a predicting module 13, and an adjusting module 14, wherein the obtaining module 11 is used for obtaining state variables of motion of a target vehicle, and determining a first kinematic model of the target vehicle according to the state variables, wherein the state variables comprise speed, acceleration and turning rate; the correction module 12 is configured to correct a state variable of the target vehicle motion according to a kalman filtering algorithm; the prediction module 13 is used for predicting a second kinematic model of the target vehicle according to the corrected state variable of the motion of the target vehicle; and the adjusting module 14 is used for adjusting the automatic cruise control strategy of the self-vehicle according to the second motion model.

Optionally, the acquisition module 11 comprises one or more of a ranging sensor, a vision sensor or a positioning module. Specifically, the method comprises the following steps: the monocular camera is arranged below a windshield of a vehicle, the laser radar is arranged on the roof of the vehicle, and the millimeter wave radar is arranged on a front bumper; according to a monocular camera located in front of the own vehicle, a motion state of the preceding vehicle is detected to determine a motion model of the preceding vehicle.

In order to improve the measurement or prediction accuracy and ensure the stability of the self-vehicle in the automatic cruise control process, in some embodiments of the invention: the correction module 12 comprises an acquisition unit, a sampling unit and an updating unit, wherein the acquisition unit is used for acquiring prior distribution of state variables according to a first kinematic model of a target vehicle; the sampling unit is used for sampling from the prior distribution to obtain a sigma point set consisting of a plurality of sigma points; and the updating unit is used for predicting the mean value and the variance of the sigma point set and updating or correcting the state variable of the motion of the target vehicle according to the mean value and the variance.

Referring to fig. 5, in the above-mentioned embodiment, the adjusting module 14 (controller) of the vehicle cruise control system 1 controls the transmission ratio, the throttle opening, the automatic pressure or the pedal stroke of the vehicle by controlling one or more of the automatic transmission controller, the engine throttle actuator and the brake actuator, so as to ensure that the speed or the acceleration of the vehicle is kept constant to the maximum extent during the driving process of the vehicle on a curve or a slope, thereby reducing the instantaneous fuel consumption. The adjusting module also comprises a human-computer interface, and the cruising speed specified by a user, the ideal distance between the target vehicle and the like can be set through the human-computer interface.

Example 3

In a third aspect of the present invention, there is provided an electronic apparatus comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method provided by the first aspect of the invention.

Referring to fig. 6, an electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

The following devices may be connected to the I/O interface 505 in general: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; a storage device 508 including, for example, a hard disk; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 6 may represent one device or may represent multiple devices as desired.

In particular, the processes described above with reference to the flow diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the 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. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer-readable medium carries one or more computer programs which, when executed by the electronic device, cause the electronic device to:

computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, python, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims

1. A vehicle cruise control method based on a kinematic model is characterized by comprising the following steps:

acquiring state variables of the motion of a target vehicle and determining a first kinematic model of the target vehicle according to the state variables, wherein the state variables comprise speed, acceleration and turning rate;

correcting the state variable of the movement of the target vehicle according to a Kalman filtering algorithm;

predicting a second kinematic model of the target vehicle according to the corrected state variable of the motion of the target vehicle;

adjusting an automatic cruise control strategy of the vehicle according to the second kinematic model, wherein the automatic cruise control strategy comprises controlling the speed and/or the acceleration of the vehicle under the condition of a curve and/or a ramp so as to reduce the transient oil consumption of the vehicle; and automatically and dynamically adjusting the speed and/or the acceleration of the self according to the state variable determined by the second kinematic model and the curve or ramp information to keep the speed and/or the acceleration change to be stable and lower than a threshold value.

2. A kinematic model-based vehicle cruise control method according to claim 1, characterized in that the first and second kinematic models comprise constant velocity models, constant acceleration models, constant turn and constant velocity models, and constant turn and constant acceleration models.

3. The kinematic model-based vehicle cruise control method according to claim 1, characterized in that said correction of the state variables of the target vehicle motion according to the kalman filter algorithm comprises the steps of:

acquiring prior distribution of state variables according to a first kinematic model of a target vehicle;

sampling from the prior distribution to obtain a sigma point set consisting of a plurality of sigma points; the multiple sigma points belong to the same Gaussian distribution, and the mean value and covariance of the Gaussian distribution are the same as the prior distribution of the state variables;

and predicting the mean value and the variance of the sigma point set, and updating or correcting the state variable of the motion of the target vehicle according to the mean value and the variance.

4. A vehicle cruise control method according to claim 3, characterised in that said predicting the mean and variance of the sigma point set and updating or correcting the state variables of the target vehicle motion in accordance therewith comprises the steps of:

predicting a mean value and a variance corresponding to next state distribution of the target vehicle according to the mean value of the sigma point set and a preset state transfer function variance to obtain a predicted measurement value of the next state of the target vehicle;

5. A vehicle cruise control system based on a kinematic model is characterized by comprising an acquisition module, a correction module, a prediction module and an adjustment module,

the acquisition module is used for acquiring state variables of the motion of the target vehicle and determining a first kinematic model of the target vehicle according to the state variables, wherein the state variables comprise speed, acceleration and turning rate;

the correction module is used for correcting the state variable of the target vehicle motion according to a Kalman filtering algorithm;

the prediction module is used for predicting a second kinematic model of the target vehicle according to the corrected state variable of the target vehicle;

the adjusting module is used for adjusting an automatic cruise control strategy of the vehicle according to the second kinematic model, wherein the automatic cruise control strategy comprises the step of controlling the speed and/or the acceleration of the vehicle under the condition of a curve and/or a ramp so as to reduce the transient oil consumption of the vehicle; and automatically and dynamically adjusting the speed and/or the acceleration of the self according to the state variable determined by the second kinematic model and the curve or ramp information to keep the speed and/or the acceleration change to be stable and lower than a threshold value.

6. The kinematic model-based vehicle cruise control system according to claim 5, said correction module comprising an acquisition unit, a sampling unit, an update unit,

the acquisition unit is used for acquiring prior distribution of the state variables according to a first kinematic model of the target vehicle;

the sampling unit is used for sampling from the prior distribution to obtain a sigma point set consisting of a plurality of sigma points;

and the updating unit is used for predicting the mean value and the variance of the sigma point set and updating or correcting the state variable of the motion of the target vehicle according to the mean value and the variance.

7. An electronic device, comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the kinematic model-based vehicle cruise control method of any of claims 1-4.

8. A computer-readable medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, carries out a kinematics model-based vehicle cruise control method according to any of the claims 1-4.