CN107103185A - Intelligent vehicle active collision avoidance method based on grey forecasting model - Google Patents
Intelligent vehicle active collision avoidance method based on grey forecasting model Download PDFInfo
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- CN107103185A CN107103185A CN201710206122.XA CN201710206122A CN107103185A CN 107103185 A CN107103185 A CN 107103185A CN 201710206122 A CN201710206122 A CN 201710206122A CN 107103185 A CN107103185 A CN 107103185A
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Abstract
The invention discloses the intelligent vehicle active collision avoidance method based on grey forecasting model, belong to vehicle active safety technologies field.The present invention is using grey forecasting model by carrying out data processing to vehicle GPS original time series, draw the predicted value of cumulative sequence, and then regressive reduction draws the predicted value of original series, the position of main vehicle and surrounding vehicles future time instance is predicted with this, calculate the speed and acceleration of the main vehicle of future time instance and surrounding vehicles, risk of collision vehicle is judged whether, and reminds main vehicle drivers away from dangerous matter sources.Data needed for of the invention are few, and short-term forecast precision is higher, and real-time is good；The complexity of sensor-based collision prevention control system is avoided simultaneously, improves accuracy and robustness.Due to reducing number of sensors, cost reduction improves economy.
Description
Technical field
The invention belongs to vehicle active safety technologies field, and in particular to the intelligent vehicle based on grey forecasting model is actively
Collision avoidance method.
Background technology
In recent years, frequent accidents occur, and traffic safety problem becomes a big public hazards of modern society, and China is
One of most country of toll on traffic in the world.According to statistics, in all traffic accidents, automobile collision accident is
Principal mode, accounts for the 60%~70% of traffic accident.In order to reduce traffic accident, collision avoidance system is arisen at the historic moment, and they lead
If by collecting the information of adjacent vehicle on road, determine whether the vehicle of dangerous traveling, and remind driver avoid with
It is collided.Typical collision avoidance system is measurement road information of the vehicle by actives such as radar, sound or vision sensors,
These information mainly include the relative position and relative velocity between main vehicle and measurement vehicle.These relative motions are believed
Breath is combined with the information that main vehicle is obtained from movable sensor, so as to estimate collision time.Automotive active anti-collision system
Driver can be reminded to note and intervening measure can be taken before accident occurs, can effectively reduce the generation of automobile collision accident.
, can not be effectively pre- but sensor-based collision prevention control system can only monitor surrounding vehicles transport condition in real time
Surrounding vehicles transport condition is surveyed, its economy, accuracy, actual effect and robustness is not ensured and controlled well.
The content of the invention
In order to overcome the above-mentioned deficiencies of the prior art, the invention provides the intelligent vehicle based on grey forecasting model actively
Collision avoidance method.
Technical scheme uses following steps：
Step one, the single-chip microcomputer in collision avoidance controller receives vehicle ViGps time sequence be used as original series, represent
For
Step 2, calculates the level of time series than λ (k), and judges level can held in covering than whether λ (k) falls, if falling
It can hold in covering, then sequenceGray prediction can be carried out as the data of model GM (1,1)；Otherwise, it is necessary to sequence
Conversion process is done, falling into it can hold in covering；
Original series are done the cumulative sequence of 1 Accumulating generation by step 3
Step 4, orderFor the sequence that adds upClose to equal value sequence, be expressed as
Step 5 is rightSet up the differential equation and corresponding albinism differential equation is respectively： OrderFor parameter vector to be estimated, the differential equation is write as matrix form, by Least Square Method
U, willBring albinism differential equation into and draw k+1 moment vehicles viThe predicted value of position, wherein k=2, n, a for development
Coefficient, b is grey actuating quantity；
Step 6, according to k+1 moment vehicles V in step 5iThe predicted value of position, surrounding vehicles carry out instantaneous velocity and wink
Brief acceleration estimates that the gps time sequence of the speed estimated and collection is issued main vehicle by surrounding vehicles, and main vehicle can be pre-
Survey position and the speed of surrounding vehicles and itself subsequent time, thus calculate main vehicle and surrounding vehicles relative distance L and
Relative velocity v, whether be hazardous vehicles, remind main vehicle drivers away from dangerous matter sources if judging surrounding vehicles.
Further, fall into a trap formula of the level than λ (k) of evaluation time sequence of the step 2 is
Further, holding in the step 2 is covered as
Further, the matrix form of the differential equation is in the step 5
Further, it is by Least Square Method U formula in the step 5：Its
In
Further, k+1 moment vehicles v in the step 5iThe predicted value of position is：
Beneficial effects of the present invention are：The present invention predicts main vehicle and surrounding vehicles future time instance using grey forecasting model
Position, the speed and acceleration of the main vehicle of future time instance and surrounding vehicles are calculated with this, risk of collision is judged whether
Vehicle, and main vehicle drivers are reminded away from dangerous matter sources.Data needed for of the invention are few, and short-term forecast precision is higher, and real-time is good；Together
When avoid the complexity of sensor-based collision prevention control system, improve accuracy and robustness.Due to reducing sensing
Device quantity, cost reduction, improves economy.
Brief description of the drawings
Fig. 1 is the structured flowchart of the intelligent vehicle active collision avoidance method of the invention based on grey forecasting model；
Fig. 2 carries out the flow chart of gray prediction for the present invention；
Fig. 3 vehicles operating range schematic diagram within each sampling period；
Fig. 4 is different collision avoidance modes and friction speed interval and linear relationship chart of the safety time away from TTC values.
Embodiment
It is below in conjunction with accompanying drawing and specifically real to make the purpose of the present invention, technical scheme and effect clearer, clear and definite
Applying example, the invention will be further described.
Gray prediction is to carry out estimation prediction to the development and change rule of system action feature, while can also be special to behavior
Estimation calculating is carried out at the time of the abnormal conditions levied occur, and feelings are distributed to the future event of event occurs in specific time zone
The method that condition makes research.Gray model is to be changed into randomness by production using Discrete Stochastic number to be significantly weakened and relatively have
The generation number of rule, it is established that the model of differential equation form its change procedure is described.The model of gray system theory has
Many kinds, but single order single argument GM (1,1) model in gray system theory is mainly used in gray prediction.
As shown in Figure 1, 2, the intelligent vehicle active collision avoidance method based on grey forecasting model, including step：
1) single-chip microcomputer in collision avoidance controller receives vehicle ViGps time sequence as original series, be expressed as：
Wherein：I=1,2,3n；
2) level of time series is calculated than λ (k)：
If the level of the time series calculated all falls that covering can be being held than λ (k)It is interior, then original series
Gray prediction can be carried out as the data of model GM (1,1)；Otherwise, it is necessary to original seriesConversion process is done, makes it
Falling into can hold in covering；Appropriate constant c is taken to do translation transformation so that level falls than λ (k) can hold coveringIt is interior：
Wherein：K=1,2n；
Original seriesThe new sequence of generation：
3) to original seriesDo 1 cumulative sequence of cumulative (AGO) generation：
Wherein：
4) makeFor the sequence that adds upClose to average (MEAN) sequence：
Wherein：
5) it is rightSet up the differential equation：
Albinism differential equation is accordingly：
Wherein:A is development coefficient, and b is grey actuating quantity；
OrderFor parameter vector to be estimated, then the matrix form of equation (9) is：
OrderThen by Least Square Method U：
WillBring equation (10) into, obtain：
Therefore, k+1 moment vehicles ViThe predicted value of position is：
6) according to step 5) in k+1 moment vehicles ViThe predicted value of position, estimation whether the event of dangerous traveling will
Occur；
Assuming that vehicle ViCollecting GPS sequences from the beginning isSampling period
For T, then vehicle V in each cycleiOperating range is the difference DELTA L of two gps time sequence of pointsm, as shown in Figure 3；Assuming that vehicle
ViAcceleration and initial velocity within the ith sample cycle are respectively ai、vi, then have：
According to the formula (21) derived and (22), main vehicle and surrounding vehicles progress instantaneous velocity and instantaneous acceleration are estimated
The gps time sequence of the speed estimated and collection is issued main vehicle by meter, surrounding vehicles, and main vehicle can predict surrounding vehicles
And position and the speed of itself subsequent time；It therefore, it can calculate main vehicle and the phase of surrounding vehicles by formula (21), (22)
Adjust the distance L and relative velocity v, when there is car speed to be more than safety time away from TTC more than road speeds limitation, or L/v, TTC
Refer to two cars travelled in the same direction on same path and kept for time of the present speed required for collision occurs, as shown in figure 4, judging
It is hazardous vehicles, and reminds driver away from dangerous matter sources.
TTC=Xr/Vr (23)
Wherein：XrFor the relative distance in two workshops, VrFor the relative velocity in two workshops.
It is described above that the present invention is briefly described, not by above-mentioned working range limit value, as long as taking the present invention
Thinking and method of work carry out simple modification and apply to other equipment, or make and changing in the case where not changing central scope principle of the present invention
Enter and retouch etc. behavior, within protection scope of the present invention.
Claims (6)
1. the intelligent vehicle active collision avoidance method based on grey forecasting model, it is characterised in that comprise the following steps：
Step one, the single-chip microcomputer in collision avoidance controller receives vehicle ViGps time sequence as original series, be expressed as
Step 2, calculates the level of time series than λ (k), and judges level can held in covering than whether λ (k) falls, if falling to hold
Covering is interior, then sequenceGray prediction can be carried out as the data of model GM (1,1)；Otherwise, it is necessary to sequenceDo and become
Processing is changed, falling into it can hold in covering；
Original series are done the cumulative sequence of 1 Accumulating generation by step 3
Step 4, orderFor the sequence that adds upClose to equal value sequence, be expressed as
Step 5 is rightSet up the differential equation and corresponding albinism differential equation is respectively： OrderFor parameter vector to be estimated, the differential equation is write as matrix form, by Least Square Method
U, willBring albinism differential equation into and draw k+1 moment vehicles viThe predicted value of position, wherein k=2 ..., n, a for development be
Number, b is grey actuating quantity；
Step 6, according to k+1 moment vehicles V in step 5iThe predicted value of position, surrounding vehicles carry out instantaneous velocity and instantaneously added
The gps time sequence of the speed estimated and collection is issued main vehicle by velocity estimation, surrounding vehicles, and main vehicle can predict week
Vehicle and position and the speed of itself subsequent time are enclosed, so as to calculate the relative distance L of main vehicle and surrounding vehicles and relative
Speed v, whether be hazardous vehicles, remind main vehicle drivers away from dangerous matter sources if judging surrounding vehicles.
2. the intelligent vehicle active collision avoidance method according to claim 1 based on grey forecasting model, it is characterised in that institute
Stating fall into a trap formula of the level than λ (k) of evaluation time sequence of step 2 is
3. the intelligent vehicle active collision avoidance method according to claim 1 based on grey forecasting model, it is characterised in that institute
State and be covered as holding in step 2
4. the intelligent vehicle active collision avoidance method according to claim 1 based on grey forecasting model, it is characterised in that institute
The matrix form for stating the differential equation in step 5 is
5. the intelligent vehicle active collision avoidance method according to claim 4 based on grey forecasting model, it is characterised in that institute
State in step 5 by Least Square Method U formula and be：Wherein
6. the intelligent vehicle active collision avoidance method according to claim 1 based on grey forecasting model, it is characterised in that institute
State k+1 moment vehicles v in step 5iThe predicted value of position is：
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Cited By (5)
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| CN109164799A (en) * | 2018-07-24 | 2019-01-08 | 江苏大学 | A kind of driving Active collision avoidance system and method |
| WO2019213980A1 (en) * | 2018-05-08 | 2019-11-14 | 清华大学 | Intelligent vehicle safety decision-making method employing driving safety field |
| CN111369826A (en) * | 2020-02-28 | 2020-07-03 | 上海擎感智能科技有限公司 | Dangerous vehicle reminding method, reminding device, readable storage medium and electronic equipment |
| CN113096411A (en) * | 2021-03-17 | 2021-07-09 | 武汉大学 | Vehicle collision early warning method at intersection based on Internet of vehicles environmental system |
| CN114257806A (en) * | 2022-03-01 | 2022-03-29 | 深圳思谋信息科技有限公司 | Industrial camera fault prediction method and device, computer equipment and storage medium |
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