CN108845509A - A kind of adaptive learning algorithms algorithm development system and method - Google Patents
A kind of adaptive learning algorithms algorithm development system and method Download PDFInfo
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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- G05D1/02—Control of position or course in two dimensions
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Abstract
It include real-time processing unit and I O board card module, energy supply control module, millimetre-wave radar simulator, radar sensor, Radar Signal Processing module, adaptive learning algorithms algoritic module, auto model the present invention provides a kind of adaptive learning algorithms algorithm development system；Real-time processing unit and I O board card module real time execution auto model and simulation objectives information is provided by I O board card；The power supply signal of energy supply control module offer radar sensor；Millimetre-wave radar simulator simulates the radar return under different radar modes；Radar sensor is used for detecting objects information；Radar Signal Processing module is used to handle the multiple-object information of radar output；Adaptive learning algorithms algorithm adjusts following distance according to different headways；Auto model includes kinetic model and traffic scene model.The present invention considers to participate in develop in link by more vehicle-mounted material objects to form closed-loop system, improves exploitation algorithm confidence level by developing algorithm in laboratory environments.
Description
Technical field
The invention belongs to intelligent driving auxiliary system technical fields, open more particularly, to a kind of adaptive learning algorithms algorithm
Send out system and method.
Background technique
Self-adaption cruise system is the extension of constant speed cruising system function, realizes that vehicle follows front truck in safe distance
Purpose, constant speed cruising system is based only on Engine ECU control and determines speed traveling, it cannot be guaranteed that the safety of driver, Zhi Nenghuan
Solution driver fatigue, and the safety of self-adaption cruise system combination millimetre-wave radar detection range this vehicle of information realization and target carriage
Spacing cruise control, so that driver safety be effectively ensured.
Summary of the invention
In view of this, the present invention is directed to propose a kind of adaptive learning algorithms algorithm development system, in laboratory environments,
Adaptive learning algorithms algorithm is developed by true radar sensor, is not necessarily to actual experimental place iterated revision algorithm, shortening is opened
The period is sent out, the confidence level of exploitation algorithm is improved.
In order to achieve the above objectives, the technical proposal of the invention is realized in this way：
A kind of adaptive learning algorithms algorithm development system, including real-time processing unit and I O board card module, millimeter wave thunder
Up to simulator, radar sensor, Radar Signal Processing module, adaptive learning algorithms algoritic module and auto model；
Real time execution auto model in the real-time processing unit and I O board card module provides simulation objectives by I O board card
The vehicle simulation result of every step iteration is transferred to millimetre-wave radar simulation by information by way of CAN bus/Ethernet
Device；
The millimetre-wave radar simulator connects radar sensor, simulates the radar return under different radar modes；
The radar sensor is for detecting objects ahead；
The Radar Signal Processing module operates in real-time processing unit and I O board card module, connects radar sensor,
According to multiple radar target informations, filtering one effective target object of tracking；
The adaptive learning algorithms algoritic module operates in real-time processing unit and I O board card module, realize safety with
With objects ahead vehicle；
The auto model includes kinetic model and traffic scene model.
It further, further include energy supply control module, the energy supply control module is for providing radar sensor power supply letter
Number.
Further, the auto model includes the suspension system of vehicle, steering system, braking system, wheel system, moves
Power transmission system, pilot model, engine SoftECU and vehicle body stabilizing control system SoftECU are received as controlled device
It realizes that vehicle follows purpose by self-adaption cruise system output request signal, abundant traffic environment ginseng is built in traffic scene model
With person, the running track of objects ahead vehicle fleet size, number, type, target carriage is set.
Further, the radar return includes target range, speed, angle and RCS information.
Compared with the existing technology, a kind of adaptive learning algorithms algorithm development system of the present invention has following excellent
Gesture：
1. the present invention provides be based on true millimetre-wave radar sensor in laboratory environments to develop adaptive cruise
Control algolithm method and system improve the confidence level of exploitation algorithm.
2. millimetre-wave radar data processing algorithm combination laterally offset amount provided by the invention, Kalman filtering, Life Cycle
The methods of phase, effective precisely identification danger ahead target.
3. adaptive learning algorithms algorithm provided by the invention considers different headway operating conditions, different type may be implemented
Driver (radical type/mild) demand for experience.
Another object of the present invention is to propose a kind of self-adapting cruise control method, adaptive cruise is realized.
In order to achieve the above object, the technical proposal of the invention is realized in this way：
A kind of self-adapting cruise control method, specifically comprises the following steps：
Step 1：The Radar Signal Processing module passes through setting lateral distance information screen fellow road-users target
|y|≤y0；
Wherein y is the lateral displacement of target of radar real-time detection, the laterally offset magnitude of y0 setting；
Step 2：Screen this lane most risk target：To the multiple-object information of this lane identification, according to minimum range
Method obtains risk object information
Xmin=min { x1,x2,…,xn},n≤4；
Wherein, millimetre-wave radar simulator could support up 4 target simulations；
Step 3：Tracking prediction is carried out to the risk object that step 2 identifies based on kalman filter method；
Step 4：In conjunction with Life Cycle Method, the consistency checking of target information and primary election target information is realized, to guarantee
The effective target of the most risk of real-time tracking one；
Step 5：Adaptive learning algorithms algoritic module is according to the leading vehicle distance, speed signal and vehicle of the target carriage of detection
Away from when away from information calculate two workshops target range, by target range and actual range comparison judge, to Engine ECU send
Torque increase request or to vehicle body stabilizing control system send braking deceleration request control vehicle speed of increasing and decrease, realize this vehicle with
The adaptive cruise model- following control of target carriage.
A kind of self-adapting cruise control method and a kind of above-mentioned adaptive learning algorithms algorithm development system of the invention
Beneficial effect is identical, is not repeating herein.
Detailed description of the invention
The attached drawing for constituting a part of the invention is used to provide further understanding of the present invention, schematic reality of the invention
It applies example and its explanation is used to explain the present invention, do not constitute improper limitations of the present invention.In the accompanying drawings：
Fig. 1 is a kind of adaptive learning algorithms algorithm development systematic schematic diagram described in the embodiment of the present invention；
Fig. 2 is adaptive learning algorithms algorithm development method flow diagram described in the embodiment of the present invention.
Specific embodiment
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the present invention can phase
Mutually combination.
In the description of the present invention, it is to be understood that, term " center ", " longitudinal direction ", " transverse direction ", "upper", "lower",
The orientation or positional relationship of the instructions such as "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outside" is
It is based on the orientation or positional relationship shown in the drawings, is merely for convenience of description of the present invention and simplification of the description, rather than instruction or dark
Show that signified device or element must have a particular orientation, be constructed and operated in a specific orientation, therefore should not be understood as pair
Limitation of the invention.In addition, term " first ", " second " etc. are used for description purposes only, it is not understood to indicate or imply phase
To importance or implicitly indicate the quantity of indicated technical characteristic.The feature for defining " first ", " second " etc. as a result, can
To explicitly or implicitly include one or more of the features.In the description of the present invention, unless otherwise indicated, " multiple "
It is meant that two or more.
In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, term " installation ", " phase
Even ", " connection " shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or be integrally connected；It can
To be mechanical connection, it is also possible to be electrically connected；It can be directly connected, can also can be indirectly connected through an intermediary
Connection inside two elements.For the ordinary skill in the art, above-mentioned term can be understood by concrete condition
Concrete meaning in the present invention.
The present invention will be described in detail below with reference to the accompanying drawings and embodiments.
As shown in Figure 1, adaptive learning algorithms algorithm development system includes real-time processing unit and I O board card module, power supply
Control module, millimetre-wave radar simulator, radar sensor, Radar Signal Processing module, adaptive learning algorithms algoritic module,
Auto model；Real-time processing unit and I O board card module real time execution auto model and simulation objectives letter is provided by I O board card
Breath；The power supply signal of energy supply control module offer radar sensor；Millimetre-wave radar simulator is simulated under different radar modes
Radar return；Radar sensor is used for detecting objects information；Radar Signal Processing module is used to handle more mesh of radar output
Mark information；Adaptive learning algorithms algorithm adjusts following distance according to different headways；Auto model includes kinetic model
With traffic scene model.
The present invention runs vehicle dynamic model model, model of place, thunder by real-time processing unit and I O board card module
Up to Processing Algorithm, adaptive learning algorithms strategy, meet the processing speed ability of 1ms.
Voltage signal needed for millimetre-wave radar is set by programmable power supply, it is contemplated that the vehicle mounted electric of passenger car and commercial vehicle
There are inconsistent situations for source size, this is based on the millimetre-wave radar demand voltage value that 12V is arranged subject to passenger car.
Traffic scene is developed, the running track of setting front simulation objectives realizes the acceleration and deceleration of target vehicle, cuts, cuts
Out, the scenes operating condition such as opposite car.
Because target vehicle is realized by modeling, distance, angle, speed, size of target vehicle etc. are got in real time
These information are passed to millimetre-wave radar simulator, millimetre-wave radar simulator by information by way of EtherNet bus
Electromagnetic wave signal can be simulated according to these signals, and the millimetre-wave radar simulator supports 4 angles and 4 targets
Simulation capacity, it is possible to while the electromagnetic wave signal of 4 targets is simulated, the electromagnetic wave signal of simulation is issued by transmission antenna
It goes.
The receiving antenna of millimetre-wave radar sensor receives the electromagnetic wave signal of simulator transmission antenna emulation, senses at this time
The detection of objects ahead can be realized in device, and sensor can detect multiple object information existing for front.
Due to the multiple vehicle target information in front, need to filter out the target of a most risk, Radar Signal Processing
Module realizes object filtering and tracking.
The target information that adaptive learning algorithms strategy is exported according to Radar Signal Processing module, interpretation current state are
It is no that acceleration and deceleration is needed still to maintain current vehicle speed, so that vehicle is kept safe distance traveling.
Engine SoftECU and vehicle body stabilizing control system SoftECU are built in auto model, it is assumed that work as objects ahead
When acceleration, according to the torque increase request signal that adaptive learning algorithms strategy exports, engine SoftECU response torque torque increase is asked
It asks, it can be achieved that the acceleration of this vehicle follows front truck, when objects ahead vehicle slows down, according to the deceleration of adaptive learning algorithms strategy output
Request signal is spent, vehicle body stabilizing control system SoftECU responds deceleration request, realizes that the deceleration of this vehicle follows front truck.
As shown in Figure 1, whole system realizes closed-loop control, adaptive learning algorithms algorithm development, millimeter wave thunder are realized
The purpose developed up to target tracking algorism.
As shown in Fig. 2, adaptive learning algorithms algorithm development method of the present invention, the front detected according to millimetre-wave radar is more
The parameter informations such as distance, position, speed, the article size of a target develop the radar data Processing Algorithm based on target level, real
The validity that objects ahead follows is showed, the robustness of algorithm is high, while realizing different type driver to adaptive cruise
The demand for experience of control, method include the following steps：
Step 1：The Radar Signal Processing module passes through setting lateral distance information screen fellow road-users target
|y|≤y0；
Wherein y is the lateral displacement of target of radar real-time detection, the laterally offset magnitude of y0 setting；
Step 2：This lane most risk target is screened, to the multiple-object information of this lane identification, according to minimum range
Method obtains risk object information
Xmin=min { x1,x2,…,xn},n≤4
This millimetre-wave radar simulator could support up 4 target simulations；
Step 3：Based on kalman filter method to step 2 identification target following prediction
Using the target fore-and-aft distance of millimetre-wave radar detection, longitudinal velocity, sideway angle as observational variable, system is constructed
State equation,
State variable predictive equation：
X (k | k-1)=AX (k-1 | k-1)+BU (k)
Covariance matrix predictive equation：
P (k | k-1)=AP (k-1 | k-1) AT+Q
Kalman gain matrix equation：
Kg (k)=P (k | k-1) HT/[HP(k|k-1)HT+R]
State variable optimizes equation：
X (k | k)=X (k | k-1)+Kg (k) [Z (K)-H X (k | k-1)]
The optimal renewal equation of covariance：
P (k | k)=[I-Kg (k) H] P (k | k-1)
Wherein, X (k | k-1) laststate prediction result, A are state-transition matrix, and X (k-1 | k-1) laststate is optimal
As a result, the state-transition matrix of B input, the control amount of U (k) present status, P (k | k-1) is the corresponding covariance of X (k | k-1),
P (k-1 | k-1) is the corresponding covariance of X (k-1 | k-1), Q, R system covariance, the kalman gain of Kg (k) current state, H
Observing matrix, Z (K) current state observation, X (k | k) current state maximum likelihood estimate.
Step 4：In conjunction with Life Cycle Method, the consistency checking of target information and primary election target information is realized, to guarantee
The effective target of the most risk of real-time tracking one
Biggish life threshold value will lead to effective target handoff delay, and lesser life threshold value cannot exclude of short duration number
According to interference, therefore this guarantees reasonable effective target handover mechanism using number provided with life threshold period 1.5s.
Adaptive learning algorithms algoritic module calculates two workshops away from information according to when leading vehicle distance, speed signal and spacing
Target range
T=Δ X/v
Wherein, Δ X is two following distances of millimetre-wave radar detection, this vehicle speed of v, t is headway, different by setting
Headway value control the safe distance of this vehicle and target carriage；
Judged by target range and actual range comparison, send torque increase request to Engine ECU or stablized to vehicle body
Control system sends the speed of increasing and decrease of braking deceleration request control vehicle, realizes that the adaptive cruise of this vehicle and target carriage follows
Control.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Within mind and principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (5)
1. a kind of adaptive learning algorithms algorithm development system, it is characterised in that：Including real-time processing unit and I O board card module,
Millimetre-wave radar simulator, radar sensor, Radar Signal Processing module, adaptive learning algorithms algoritic module and vehicle
Model；
Real time execution auto model in the real-time processing unit and I O board card module provides simulation objectives letter by I O board card
Breath, is transferred to millimetre-wave radar simulator for the vehicle simulation result of every step iteration by way of CAN bus/Ethernet；
The millimetre-wave radar simulator connects radar sensor, simulates the radar return under different radar modes；
The radar sensor is for detecting objects ahead；
The Radar Signal Processing module operates in real-time processing unit and I O board card module, connects radar sensor, according to
Multiple radar target informations, filtering one effective target object of tracking；
The adaptive learning algorithms algoritic module operates in real-time processing unit and I O board card module, before realizing that safety follows
Square target vehicle；
The auto model includes kinetic model and traffic scene model.
2. a kind of adaptive learning algorithms algorithm development system according to claim 1, it is characterised in that：It further include power supply
Control module, the energy supply control module is for providing radar sensor power supply signal.
3. a kind of adaptive learning algorithms algorithm development system according to claim 1, it is characterised in that：The vehicle mould
Type includes the suspension system of vehicle, steering system, braking system, wheel system, power drive system, pilot model, starts
Machine SoftECU and vehicle body stabilizing control system SoftECU is received as controlled device and is exported request letter by self-adaption cruise system
Number realization vehicle follows purpose, builds abundant traffic environment participant in traffic scene model, setting objects ahead vehicle fleet size,
The running track of number, type, target carriage.
4. a kind of adaptive learning algorithms algorithm development system according to claim 1, it is characterised in that：The radar returns
Wave includes target range, speed, angle and RCS information.
5. a kind of self-adapting cruise control method, it is characterised in that：Specifically comprise the following steps：
Step 1：The Radar Signal Processing module passes through setting lateral distance information screen fellow road-users target
|y|≤y0；
Wherein y is the lateral displacement of target of radar real-time detection, the laterally offset magnitude of y0 setting；
Step 2：Screen this lane most risk target：To the multiple-object information of this lane identification, obtained according to minimum distance method
To risk object information, specific formula is
Xmin=min { x1,x2,…,xn},n≤4；
Wherein, millimetre-wave radar simulator could support up 4 target simulations；
Step 3：Tracking prediction is carried out to the risk object that step 2 identifies based on kalman filter method；
Step 4：In conjunction with Life Cycle Method, the consistency checking of target information and primary election target information is realized, to guarantee real-time
The effective target of the most risk of tracking one；
Step 5：When adaptive learning algorithms algoritic module is according to the leading vehicle distance, speed signal and spacing of the target carriage of detection
The target range that two workshops are calculated away from information, is judged by target range and actual range comparison, sends torque increase to Engine ECU
Request or the speed of increasing and decrease that braking deceleration request control vehicle is sent to vehicle body stabilizing control system, realize this vehicle and target
The adaptive cruise model- following control of vehicle.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110203204A (en) * | 2019-05-17 | 2019-09-06 | 深圳森云智能科技有限公司 | A kind of vehicle-surroundings environment perception method |
| CN110275168A (en) * | 2019-07-09 | 2019-09-24 | 厦门金龙联合汽车工业有限公司 | A kind of multi-targets recognition and anti-collision early warning method and system |
| CN111736486A (en) * | 2020-05-01 | 2020-10-02 | 东风汽车集团有限公司 | Sensor simulation modeling method and device for L2 intelligent driving controller |
| CN112800613A (en) * | 2021-02-04 | 2021-05-14 | 西南交通大学 | Railway vehicle on-line monitoring algorithm development system |
| CN115166659A (en) * | 2022-07-27 | 2022-10-11 | 中国船舶重工集团公司第七二四研究所 | Three-coordinate radar array element-level flexible track target simulator |
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