CN110515041B - Vehicle distance measurement control method and system based on Kalman filtering technology - Google Patents
Vehicle distance measurement control method and system based on Kalman filtering technology Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/021—Auxiliary means for detecting or identifying radar signals or the like, e.g. radar jamming signals
- G01S7/022—Road traffic radar detectors
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/277—Analysis of motion involving stochastic approaches, e.g. using Kalman filters
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
Abstract
The application discloses a vehicle distance measurement control method and a system based on a Kalman filtering technology, comprising the following steps of a track starting module, a track detecting module, a target tracking module and a vehicle distance measuring module, wherein the track starting module detects stray data of a target to reject non-target data, and initializes a target track; the track updating module is used for outputting the latest track in real time in the latest frame of data, carrying out association matching on the latest track and a target track stored in the track center module, and updating the associated track by using a kalman filtering algorithm on the matched data; and deleting and carding the tracks which are not associated and matched by the removing module. The application has the beneficial effects that: the Kalman filtering technology is applied to millimeter wave radar data, so that jitter of measurement data caused by vehicle jitter can be avoided, the measurement data is close to a true value, and the accuracy of millimeter wave radar measurement is improved.
Description
Technical Field
The application relates to the technical field of intelligent perception of vehicles in the field of perception in automatic driving, in particular to a front vehicle distance measurement control method based on a Kalman filtering technology and a corresponding control system thereof.
Background
Unmanned techniques can be broken down into the materialization of understanding, learning, and memory of the "context awareness and localization-decision and planning-control and execution" process. The environment sensing and positioning are used as input of the unmanned system, and plays an important role in whether the whole system can accurately control and execute the vehicle.
The environment sensing and positioning system comprehensively applies sensing and reconstructing the position, the speed and other attributes of a positioning target relative to a host vehicle to a plurality of different types of sensors such as a camera, a millimeter wave radar, a Ridar, an ultrasonic radar and the like. Due to the performance limitations of the sensor itself and the jitter of the vehicle itself, the position measurement of the target by the sensing system cannot be directly used for the subsequent decision making system, and further filtering smoothing processing is required for the original measured data.
The vehicle running on the road can be regarded as a low-speed straight line model, and we are more concerned about the position information of the front vehicle object. Based on the millimeter wave radar scheme for sensing the position of the target vehicle, the position information of the target vehicle measured by the actual radar is inaccurate due to the influence of vehicle shake and the performance of the radar.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-described problems.
Therefore, one technical problem solved by the present application is: the method solves the problem of jitter of measured data caused by vehicle jitter in the driving process of the real vehicle, can remove false targets measured by the radar, and can track the existing targets.
In order to solve the technical problems, the application provides the following technical scheme: a vehicle distance measurement control method based on Kalman filtering technology comprises the following steps of a track starting module, a detection module, a target track initialization module and a control module, wherein the detection module detects stray data of a target to remove non-target data, and initializes the target track; the track updating module is used for outputting the latest track in real time in the latest frame of data, carrying out association matching on the latest track and a target track stored in the track center module, and updating the associated track by using a kalman filtering algorithm on the matched data; and deleting and carding the tracks which are not associated and matched by the removing module.
As a preferable scheme of the vehicle distance measurement control method based on the Kalman filtering technology, the application comprises the following steps: the track initiation further comprises the steps that after the detection module detects target stray data, if the detection target exists in five continuous frames, the detection target is considered to be a real obstacle, otherwise, the detection target is considered to be false target or non-target data, and the false target or non-target data is deleted.
As a preferable scheme of the vehicle distance measurement control method based on the Kalman filtering technology, the application comprises the following steps: the method comprises the steps of detecting a track address of a track center module, and carrying out data association and track maintenance, wherein the track address in the track center module is the data address of a detection module, in a new frame, the data address is matched with the track address, if the data address is matched with the track address, the data point is associated with a corresponding track, the associated track is updated by a kalman filtering algorithm, if the data point is not matched with the track address, the data point is input to the track initiation module to carry out track initiation, and circulation is executed.
As a preferable scheme of the vehicle distance measurement control method based on the Kalman filtering technology, the application comprises the following steps: and deleting the track, wherein if the existing track in the track center module is not associated in five continuous frames, the target corresponding to the track is considered to disappear in the field of view, and the track is deleted.
As a preferable scheme of the vehicle distance measurement control method based on the Kalman filtering technology, the application comprises the following steps: the track updating module outputs the updated target data to the decision module, and the decision module correspondingly controls the vehicle according to the target data.
As a preferable scheme of the vehicle distance measurement control method based on the Kalman filtering technology, the application comprises the following steps: the method further comprises the following decision step that a user selects a control mode of the vehicle according to the user mode selection layer; the upper decision layer calculates expected speed, expected acceleration and data corresponding to the opening degree of an accelerator or the stroke of a brake pedal under a corresponding control mode according to the updated target data output by the track updating module; and the lower execution layer executes the data calculated by the upper decision layer to adjust the opening degree of the accelerator or the braking force and actually control the distance, the speed and the acceleration of the vehicle.
As a preferable scheme of the vehicle distance measurement control method based on the Kalman filtering technology, the application comprises the following steps: the user mode selection layer selects the control modes of the vehicle and further comprises modes of constant-speed cruising, low-speed following and traffic jam following.
As a preferable scheme of the vehicle distance measurement control method based on the Kalman filtering technology, the application comprises the following steps: the detection module comprises a laser sensor, a millimeter wave radar and an ultrasonic sensor, the measuring range of the millimeter wave radar is 0.5-200 m, the speed is-200 km/h to +200km/h, and the millimeter wave radar is arranged at the left and right positions right in front of the vehicle head.
The application solves the other technical problem that: the vehicle distance measurement control system based on the Kalman filtering technology is provided, and the control method can be applied to the system.
In order to solve the technical problems, the application provides the following technical scheme: a vehicle distance measurement control system based on Kalman filtering technology is characterized in that: the system comprises a track starting module, a track updating module, a track center module, a rejecting module and a decision module; the track starting module is used for detecting a target track and initializing the track; the track updating module is connected with the track starting module and is used for updating the track of the track center module and outputting the filtered data; the rejection module is connected with the track center module and is used for deleting the track of the track center module; and the decision module receives the data output by the track updating module and performs corresponding decision control on the vehicle.
As a preferable scheme of the vehicle distance measurement control system based on the Kalman filtering technology, the application comprises the following steps: the decision module comprises a user mode selection layer, an upper decision layer and a lower execution layer which are sequentially connected and arranged in the vehicle electronic control unit.
The application has the beneficial effects that: the Kalman filtering technology is applied to millimeter wave radar data, so that jitter of measurement data caused by vehicle jitter can be avoided, the measurement data is close to a true value, and the accuracy of millimeter wave radar measurement is improved; the Gaussian white noise of millimeter wave radar data can be reduced, the radar data is smoothed, and the fluctuation of the radar data caused by vehicle shake is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a schematic overall flow chart of a vehicle distance measurement control method based on a Kalman filtering technology according to a first embodiment of the application;
fig. 2 is a schematic diagram of raw measurement data of a millimeter wave radar according to a first embodiment of the present application;
fig. 3 is a schematic parameter diagram of a millimeter wave radar according to a first embodiment of the present application;
FIG. 4 is a schematic diagram of a brake actuator according to a first embodiment of the present application;
FIG. 5 is a schematic diagram of the electronic throttle controller according to the first embodiment of the present application;
FIG. 6 is a schematic diagram of the overall principle of a vehicle distance measurement control system based on Kalman filtering according to a second embodiment of the present application;
FIG. 7 is a schematic diagram of a target track to be detected according to the present application;
FIG. 8 is a schematic diagram of a range result with a true value standard of 3m according to the present application;
FIG. 9 is a diagram of a range result with a true value standard of 5m according to the present application;
FIG. 10 is a diagram of a range result with a practical truth criterion of 9m according to the present application;
FIG. 11 is a schematic diagram of a range result with a true value standard of 15m according to the present application;
fig. 12 is a schematic diagram showing comparison of the ranging experimental error results according to the present application.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present application can be understood in detail, a more particular description of the application, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present application have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present application, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, it is shown that an overall flow diagram of a vehicle distance measurement control method based on a Kalman filtering technology is provided in this embodiment, because of the unevenness of a road surface and the inherent vibration characteristics of a vehicle body, data measured by a millimeter wave radar or a laser sensor often have larger noise, short detection or false detection of a target often occurs, so that a great trouble is brought to smooth control of a vehicle, and referring to fig. 2, the original distance measurement data has great fluctuation, and the measured relative speed fluctuation is also great due to fluctuation of the road surface and vibration of the vehicle itself. So that if the signal cannot be reasonably processed, the vehicle cannot be smoothly controlled. The method proposed by the present embodiment has the steps of,
the track starting module 100, the detection module 101 detects the stray data of the target to remove non-target data, and initializes the target track; the track updating module 200, in the latest frame of data, the detection module 101 outputs the latest track in real time, the latest track is associated and matched with the target track stored in the track center module 300, and the associated track is updated by a kalman filtering algorithm on the matched data; the trace deletion module 400 performs deletion and carding on the trace which is not associated and matched. The track deletion also includes deleting the track if the existing track within the track center module 300 is not associated within five consecutive frames, and the target corresponding to the track is considered to be lost in the field of view. The detection module 101 comprises a laser sensor, a millimeter wave radar and an ultrasonic sensor, the measurement range of the millimeter wave radar is 0.5-200 m, the speed is-200 km/h to +200km/h, and the millimeter wave radar is arranged at the left and right positions right in front of the vehicle head.
The track initiation further includes, after the detection module 101 detects the target spurious data, if the detected target exists in all five consecutive frames, considering the detected target as a real obstacle, otherwise, recognizing the detected target as false target or non-target data, and deleting the false target or non-target data.
The embodiment further includes data association and track maintenance, the track address in the track center module 300 is the data address of the detection module 101, in a new frame, the data address is matched with the track address, if the data address is matched with the track address, the data point is associated with the corresponding track, the associated track is updated by using a kalman filtering algorithm, if the data point is not matched with the track address, the data point is input to the track start module 100 to start the track, and the loop is executed.
The track updating module 200 outputs the updated target data to the decision module 500, and the decision module 500 performs corresponding control on the vehicle according to the target data. The method also comprises the following decision step that a user selects a control mode of the vehicle according to a user mode selection layer; the upper decision layer calculates the expected speed, the expected acceleration and the data corresponding to the accelerator opening or the brake pedal stroke under the corresponding control mode according to the updated target data output by the track updating module 200; the lower execution layer executes the data calculated by the upper decision layer to adjust the opening degree of the accelerator or the braking force and actually control the distance, the speed and the acceleration of the vehicle. The user mode selection layer selects the control modes of the vehicle and further comprises modes of constant-speed cruising, low-speed following and blocking and following.
The embodiment also needs to be explained that the millimeter wave radar sensor is adopted, the working frequency is 77GHz, the distance, the speed, the moving direction and the relative angle of the target can be measured, and the millimeter wave radar sensor has the advantages of small volume, high sensitivity, wide detection range, good weather adaptability and the like.
The schematic diagrams referring to fig. 3 to 5 are parameter schematic diagrams of the millimeter wave radar, for example, the millimeter wave radar is installed at a position of a logo right in front of a vehicle head, and an actuator is mainly composed of a brake actuator (a brake motor) and an electronic throttle controller. The brake motor adopts a mechanical component similar to power-assisted steering to pull a stay wire to drive a brake pedal, and imitates the braking operations of light stepping, slow braking, emergency braking and the like of a driver, the position of a braking stroke is fed back to a self-adaptive cruise control system of a vehicle in real time by a displacement sensor arranged at the brake pedal, a brake actuator receives a host machine instruction through a bus network, and the speed regulator is adjusted to drive the motor to forward and reverse, so that different stepping intensities of the brake pedal are realized, and the pedal is released. The electronic accelerator controller uses an RS485 interface to communicate with the main controller, and meanwhile, a GPI signal is arranged for quickly releasing the electronic accelerator, so that the response speed of the system is improved. The display adopts a bus communication mode to display information such as the distance of a front vehicle, the speed of the vehicle, the operation state and the like in real time, and is arranged in front of a driver.
The kalman filtering algorithm proposed in this embodiment includes the following steps,
firstly, an estimated process signal is established, and a state variable x epsilon R n of a discrete time process is calculated, so that the state variable x epsilon R n is described by the following discrete random differential equation: x is x k ＝Ax k-1 +Bu k-1 +w k Defining an observation variable z epsilon R m to obtain a measurement equation: z k ＝Hx k +v k ；
Random signal w k And v k The process excitation noise and the observation noise are respectively represented, and at the same time, random signals, namely the above stray data, are detected by the detection module 101, and are filtered according to a certain mode, namely a possible target is determined, a target track is initialized, and data obtained by excluding non-target data is removed as the input of a model. The Gaussian white noise of millimeter wave radar data can be reduced based on a kalman filtering technology, the radar data is smoothed, and fluctuation of the radar data caused by vehicle shake is reduced.
The process excitation noise covariance matrix and the observed noise covariance matrix may vary with each iterative calculation, when the control function u k-1 Or process excitation noise w k-1 When zero, the n x n order gain matrix A in the differential equation linearly maps the state of the previous time k-1 to the state of the current time k. In practice a may vary over time but is assumed here to be constant. The nxl order matrix B represents the gain of the optional control input u e R l.
Calculation prototype of filter: definition of the definitionTo at the same timeKnowing a priori state estimates of the kth step in the case of the state prior to the kth step;
definition of the definitionAs a known measured variable Z k And (5) estimating the posterior state of the kth step. Thereby defining a priori estimation errors
Difference and posterior estimation error:
the covariance of the a priori estimation error is:
the covariance of the posterior estimation error is:
constructing an expression of a Kalman filter: prior estimationAnd the weighted measured variable Zk and its prediction +.>The linear combination of the differences constitutes a posterior state estimate +.>Finally->
K is calculated by the steps of:
the larger the residual gain K, the larger the observed noise covariance R. In particular, when R tends to zero, there are:
is dependent on the measured variable Z before it is known k In case X k Is>The filter state update equation is:
after the time update equation and the measurement update equation are calculated, the whole process is repeated again, the posterior estimation obtained by the previous calculation is used as the prior estimation of the next calculation and is continuously recursively carried out, so that the estimation and tracking of discrete data can be realized, and the acquired track data is more accurate.
Scene one:
in order to verify the accuracy of the filtering algorithm, the embodiment performs error analysis on the estimated position and the real position of the filtered target, verifies the effect of the method on improving the accuracy by comparing the position data with the position data obtained by a single sensor, and takes target points with the distances of 3m, 5m, 9m and 15m from the origin of a vehicle coordinate system as actual truth standards by referring to the schematic diagram of fig. 7, and the method is based on a distance measurement result diagram obtained after the kalman filtering, the traditional millimeter wave radar and the laser radar (single sensor) respectively detect 4 target points for 5 times. In this example, 4 sets of experimental data were tested as a comparison, 5 times per set, to obtain 4 sets of experimental results as shown in fig. 8-11.
As can be seen from the figures, the data measured by the individual sensors has fluctuations due to the influence of internal and external environmental factors. And according to experimental data, adopting the kalman filtering technology based on the method to finish radar data fusion.
Fig. 12 is a graph of error values for five individual radar sensor measurements. As can be seen from the graph, the measurement error proposed herein is stabilized within 0.01m, and compared with the millimeter wave radar, the measurement error reduction percentage interval of 5 times of data is about 50% -400%, and the laser radar is about 20% -150%, which shows that the accuracy of the method is higher and is closer to a true value.
Example 2
Referring to the illustration of fig. 6, the present embodiment is schematically shown as a vehicle distance measurement control system based on a Kalman filtering technology, and the vehicle distance measurement control method of the above embodiment can be applied to the system of the present embodiment to implement, which is a hardware part of the above embodiment.
The system comprises a track starting module 100, a track updating module 200, a track center module 300, a rejecting module 400 and a decision module 500; the track initiation module 100 is used for detecting a target track and initializing the track; the track updating module 200 is connected with the track starting module 100 and is used for updating the track of the track center module 300 and outputting the filtered data; the rejection module 400 is connected with the track center module 300 and is used for deleting the track of the track center module 300; the decision module 500 receives the data output by the track update module 200 to perform corresponding decision control on the vehicle. The decision module 500 includes a user mode selection layer, an upper decision layer, and a lower execution layer, which are sequentially connected and disposed in the vehicle electronic control unit.
The emergency collision avoidance function is highest, and when the system determines that the vehicle is about to collide, braking is performed with the maximum braking force: secondly, entering corresponding braking and decelerating functions according to a control mode selected by a user, and if the system determines that the danger can close the accelerator output at the moment, executing no acceleration action even if a driver mistakenly steps on the accelerator: on the premise that the system judges safety, a driver can accelerate by increasing the accelerator to finish actions such as overtaking, and then enter a driver control mode; and finally, entering a constant-speed cruising, low-speed following or traffic jam following function according to the real-time road condition. The driver may choose to exit the auto-cruise system at any time.
In this embodiment, the track initiation module 100, the track update module 200, the track center module 300, the rejection module 400, and the decision module 500 are data processing chips integrated on a circuit board, and are connected with a vehicle-mounted electronic control unit, that is, a vehicle-mounted ECU unit. The track updating module 200 adopts the algorithm program and is implanted into a chip and integrated into a circuit board hardware module of the vehicle-mounted control chip.
An algorithm may be understood as a complete solution step consisting of basic operations and a defined order of operations. Or as a finite, exact sequence of calculations, designed as required, and such steps and sequences can solve a class of problems, an algorithm being a finite sequence of instructions, a program being an ordered set of computer instructions, a representation of the algorithm in a programming language, a specific implementation of the algorithm on a computer, the algorithm typically being described in a semi-formal language, and the program being
The program described in a formalized computer language is an ordered set of computer instructions and the algorithm is the step of solving the problem; the programs are code implementations of algorithms, and one algorithm may program out different programs in different programming languages. The embedded chip is realized by transplanting the program into the chip hardware, so in this embodiment, the algorithm implemented as described above is programmed and transplanted onto the chip, and the chip with the embedded algorithm and the on-board control circuit board are integrated to form the hardware of the circuit board, which is called embedded development. Similarly, the control module is a microprocessor with data processing, and the application of the embodiment in vehicle-mounted is for example an ECU unit, an electronic control unit, also called a "car running computer", "vehicle mounted computer" and the like. The microcomputer controller for automobile is composed of Microprocessor (MCU), memory (ROM, RAM), input/output interface (I/O), A/D converter (A/D) and large-scale integrated circuits for shaping and driving. The term "ECU" is simply the brain of the vehicle ". The CPU is a core part in the ECU, and has the functions of operation and control, when the engine is running, the CPU collects signals of all sensors, performs operation, converts the operation result into a control signal and controls the work of a controlled object. It also exercises control over memory (ROM/FLASH/EEPROM, RAM), input/output interfaces (I/O) and other external circuitry; the program stored in the memory ROM is written based on data obtained through accurate calculation and a large number of experiments, and the inherent program is continuously compared and calculated with the collected signals of each sensor when the engine works. And the ECU is modified, namely the purpose of changing the operation of the engine is achieved by changing the method for processing the problem (the originally set ECU program). The ECU program is a set of algorithm stored in the memory, and processes the signals converted from the input device via the controller to generate corresponding command signals, and transmits the command signals from the output device, so as to realize control over more running states of the vehicle.
Therefore, in the embodiment, the accurate measurement and tracking of the target distance around the vehicle are realized by integrating and setting the inter-module circuit boards in the vehicle through the electric connection, the kalman filtering technology is applied to millimeter wave radar data, and the data is calculated and processed by being implanted into a chip, so that the jitter of the measured data caused by vehicle jitter can be avoided, the measured data is close to a true value, the accuracy of millimeter wave radar measurement is improved, more accurate data is provided for a driving decision system, and the safety of an automatic driving system is improved.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered in the scope of the claims of the present application.
Claims (6)
1. A vehicle distance measurement control method based on Kalman filtering technology is characterized in that: comprises the steps of,
the track starting module (100) is used for removing non-target data from stray data of a detection target of the detection module (101) and initializing a target track;
the track initiation further comprises the steps that after the detection module (101) detects target stray data, if the detection target exists in five continuous frames, the detection target is considered to be a real obstacle, otherwise, the detection target is considered to be false target or non-target data, and the false target or non-target data is deleted;
the track updating module (200) is used for outputting the latest track in real time by the detection module (101) in the latest frame of data, the latest track is associated and matched with a target track stored in the track center module (300), and the associated track is updated by a kalman filtering algorithm for the matched data;
the kalman filtering algorithm comprises the following steps of establishing an estimated process signal, wherein a state variable x epsilon R n of a discrete time process is expressed as a discrete random differential equation: x is x k ＝Ax k-1 +Bu k-1 +w k Defining an observation variable z epsilon R m to obtain a measurement equation: z k ＝Hx k +v k The method comprises the steps of carrying out a first treatment on the surface of the In which the random signal w k And v k Respectively representing process excitation noise and observation noise, and simultaneously filtering target stray data detected by the detection module (101) according to a certain mode, namely determining a possible target, initializing a target track and removing data obtained by non-target data as input of a model; when controlling the function u k-1 Or process excitation noise w k-1 When zero, the n×n order gain in the differential equationThe matrix A linearly maps the state of the previous moment k-1 to the state of the current moment k; wherein A is set as a constant, and the n×l order matrix B represents the gain of the optional control input u εR≡l;
performing data association and track maintenance, wherein the track address in the track center module (300) is the data address of the detection module (101), in a new frame, the data address is matched with the track address, if the data address is matched with the track address, the data point is associated with a corresponding track, the associated track is updated by a kalman filtering algorithm, if the data point is not matched with the track address, the data point is input to the track starting module (100) to start the track, and circulation is performed;
deleting the tracks, and deleting and carding the tracks which are not matched in a correlated way by a rejecting module (400);
the track deletion further comprises the step of considering that the target corresponding to the track disappears in the field of view and deleting the track if the existing track in the track center module (300) is not associated in five continuous frames;
the detection module (101) comprises a laser sensor, a millimeter wave radar and an ultrasonic sensor, the measurement range of the millimeter wave radar is 0.5-200 m, the speed is-200 km/h to +200km/h, and the millimeter wave radar is arranged at the left and right positions right in front of the vehicle head.
2. The vehicle distance measurement control method based on the Kalman filtering technique as claimed in claim 1, wherein: the track updating module (200) outputs updated target data to the decision module (500), and the decision module (500) correspondingly controls the vehicle according to the target data.
3. The vehicle distance measurement control method based on the Kalman filtering technique as claimed in claim 2, wherein: also included is a decision step of the following,
the user selects a control mode of the vehicle according to the user mode selection layer;
the upper decision layer calculates expected speed, expected acceleration and data corresponding to the opening degree of an accelerator or the stroke of a brake pedal under a corresponding control mode according to the updated target data output by the track updating module (200);
and the lower execution layer executes the data calculated by the upper decision layer to adjust the opening degree of the accelerator or the braking force and actually control the distance, the speed and the acceleration of the vehicle.
4. The vehicle distance measurement control method based on Kalman filtering technique as claimed in claim 3, wherein: the user mode selection layer selects the control modes of the vehicle and further comprises modes of constant-speed cruising, low-speed following and traffic jam following.
5. A system for implementing the Kalman filter technique based vehicle distance measurement control method according to any one of claims 1 to 4, characterized in that: the system comprises a track starting module (100), a track updating module (200), a track center module (300), a rejecting module (400) and a decision module (500);
the track starting module (100) is used for detecting a target track and initializing the track;
the track updating module (200) is connected with the track starting module (100) and is used for updating the track of the track center module (300) and outputting the filtered data;
the rejection module (400) is connected with the track center module (300) and is used for deleting the track of the track center module (300);
the decision module (500) receives the data output by the track updating module (200) to perform corresponding decision control on the vehicle.
6. The Kalman filter technology based vehicle distance measurement control system according to claim 5, wherein: the decision module (500) comprises a user mode selection layer, an upper decision layer and a lower execution layer which are sequentially connected and arranged in the vehicle electronic control unit.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108344992A (en) * | 2017-12-20 | 2018-07-31 | 北京华航无线电测量研究所 | A kind of multi-object tracking method for vehicle-mounted millimeter wave radar |
| CN108645412A (en) * | 2018-05-31 | 2018-10-12 | 惠州华阳通用电子有限公司 | A kind of adaptive track initiation method of multisensor |
| CN109508000A (en) * | 2018-12-16 | 2019-03-22 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Isomery multi-sensor multi-target tracking method |
-
2019
- 2019-08-30 CN CN201910814499.2A patent/CN110515041B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108344992A (en) * | 2017-12-20 | 2018-07-31 | 北京华航无线电测量研究所 | A kind of multi-object tracking method for vehicle-mounted millimeter wave radar |
| CN108645412A (en) * | 2018-05-31 | 2018-10-12 | 惠州华阳通用电子有限公司 | A kind of adaptive track initiation method of multisensor |
| CN109508000A (en) * | 2018-12-16 | 2019-03-22 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Isomery multi-sensor multi-target tracking method |
Non-Patent Citations (2)
| Title |
|---|
| "An Adaptive Cruise Control System for Autonomous Vehicles";Man Hyung Lee;《INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING》;20130331;正文第373-380页 * |
| "具有堵车跟踪功能的车辆自适应巡航控制";张国新;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20190115;正文第7-13、17-23页 * |
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