CN108550279A - Vehicle drive behavior prediction method based on machine learning - Google Patents
Vehicle drive behavior prediction method based on machine learning Download PDFInfo
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- CN108550279A CN108550279A CN201810287172.XA CN201810287172A CN108550279A CN 108550279 A CN108550279 A CN 108550279A CN 201810287172 A CN201810287172 A CN 201810287172A CN 108550279 A CN108550279 A CN 108550279A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Abstract
Vehicle drive behavior prediction method based on machine learning, it is related to car networking field, it is intended to that machine learning techniques, the relationship between digging vehicle attribute, road information and running environment information and vehicle drive behavior is utilized to improve the accuracy of vehicle drive behavior prediction.Specific steps include：Step 1, defined feature collection：Vehicle characteristics define, roadway characteristic definition, vehicle running environment definition；Step 2, vehicle movement prediction model：Feature extraction and data prediction：Vehicle is extracted with junction ahead distance feature, and crossing allows go to action feature extraction, tag extraction；Vehicle movement prediction model：Training sample set defines, the training of vehicle movement prediction model；Step 3, vehicle drive behavior prediction model：Gaussian component defines；Step 4, vehicle drive behavior prediction.
Description
Technical field
The present invention relates to car networking fields, and in particular to a kind of vehicle drive behavior prediction method based on machine learning.
Background technology
Vehicle drive behavior prediction serves primarily in the safety-related application of car networking, as crossroad vehicle collision avoidance is supervised
Survey etc..The existing research about vehicle drive behavior prediction, the main historical behavior track for considering vehicle and vehicle traveling
Traffic information.These researchs have ignored vehicle self attributes (such as vehicle to modeling idealization of vehicle and place traffic information
Height is wide), vehicle to crossing distance, traffic signals and track turn to allow mark etc. influence vehicle drive behavior it is important because
Element has relatively large deviation so as to cause in real urban road to the prediction result of driving behavior and actual conditions.
The present invention considers many factors of above-mentioned influence vehicle drive behavior, utilizes machine learning techniques, wheeled digging machine
Relationship between attribute, road information and running environment information and vehicle drive behavior improves vehicle drive behavior prediction
Accuracy.
Invention content
It goes to vehicle body attribute, road information and running environment and driving for existing vehicle drive behavior prediction model
The problem of relation excavation deficiency between, the present invention consider the important shadow such as vehicle attribute, road information and running environment
The factor of sound proposes a kind of vehicle drive behavior prediction method based on machine learning.
Technical solution of the present invention is：
A kind of vehicle drive behavior prediction method based on machine learning, which is characterized in that use full Connection Neural Network
Prediction vehicle movement clusters driving behavior using gauss hybrid models according to the displacement of prediction.Specific method includes such as
Lower step：
Step 1, defined feature collection, including vehicle characteristics definition, roadway characteristic definition, vehicle running environment characterizing definition.
Step 11, vehicle characteristics define
Vehicle characteristics include length of wagon L, body width W, and car speed, acceleration, current driving direction, crossing turns to
Action, wherein t moment car speed accelerate such as to be respectively labeled as v (t) and a (t), remaining feature is defined respectively as：
Define 1 vehicle heading vDir (t), indicate t moment direction of vehicle movement, between direct north clockwise
Angle indicates, meets：
0≤vDir (t) ＜ 360 ° (4)
Define 2 vehicle intersections and turn to vMov (t), indicate driving behavior of t moment vehicle when by crossroad, with to
The form of amount characterizes, since crossroad does not allow to reverse end for end, the case where being presently considered vehicle straight trip, turn left, turn right, such as
Formula (2).
3 current vehicle position P (t) are defined, indicate t moment vehicle in CA State Plane III in NAD83 coordinates
Two-dimensional coordinate vector in system, vector items unit is foot (ft).Location information is defined as follows：
P (t)=(x (t), y (t)) (3)
CA State Plane III in NAD83 coordinate systems are 1983 North America reference plane (NAD) coordinate systems.
To sum up, the feature set feature of t moment vehiclev(t) it is defined as follows：
featurev(t)={ L, W, v (t), a (t), vDir (t), vMov (t), P (t) } (4)
Step 12, roadway characteristic defines
Crossroad or T-shaped road junction are abstracted as quadrangle, with its four apex coordinates since the angle of direction northwest by
According to identifying successively clockwise, label is as follows：
I=(x1, y1, x2, y2, x3, y3, x4, y4) (5)
It defines 4 crossroad set ISet to indicate in survey region, the set of all crossroad compositions.
ISet=(I1, I2..., Im...) and (6)
It defines 5 road segment segments and refers to a section of the road between two adjacent crossroads, by road segment segment both sides four crossway
Mouthful identify the road segment segment.The road segment segment set for defining road i is as follows：
RSegSet (i)=(I1I2, I2I3..., IkIm...) and (7)
Wherein Ik(1≤k≤n, n are maximum crossroad number) indicates the crossroad that number is k.
Every road segment segment includes several tracks, defines crossroad Ik, ImBetween road segment segment track set it is as follows：
IkIm=(lid1, lid2..., lidn) (8)
Wherein lidk(1≤k≤n) indicates lane number.
It defines 6 track direction lDir (x, y) and indicates that the direction of travel that track allows, wherein x, y indicate the position in track
Coordinate.Angle defines same vDir.Vehicle is identical as track direction in the travel direction vDir of current lane straight way, even current vehicle
It is in the tracks i, then has
VDir (t)=lDiri(x, y), wherein (x, y)=P (t) (9)
Define on the left of 7 tracks can lane change quantity LAL (left available lanes) be under certain track current location,
The number of lanes that vehicle can change to the left.By the current location (x, y) of current lane i Lane Searches in the same direction to the left, until meeting
Indicate that vehicle can not lane change to the left or until searching leftmost side track in the same direction to solid line.The number of lanes searched is vehicle
It can lane change quantity LAL on the left of roadi(x, y).
Define on the right side of 8 tracks can lane change amount R AL (right available lanes) be in certain track current location
Under, number of lanes that vehicle can change to the right.By current location (x, the y) Lane Searches in the same direction to the right of current lane i, until
It encounters solid line and indicates that vehicle can not lane change to the right or until searching rightmost side track in the same direction.The number of lanes searched is
It can lane change amount R AL on the right side of tracki(x, y).
Defining 9 straight trip area tracks allows driving behavior to include straight trip, lane change to the left and to the right lane change.Definition vector sld tables
Show that the basic driving behavior that track allows is as follows：
The driving behavior SLD that straight trip area track allows has
The driving behavior for defining the permission of 10 crossing area in preparation tracks includes straight trip, is turned left, right-hand bend and u-turn.Define to
It is as follows to measure the basic driving behavior that pld indicates that track allows：
The driving behavior PLD that crossing area in preparation track allows can be expressed as：
PLD (x, y)=β1(x, y) pldst+β2pldtl+β3pldtr+β4pldta+β5pldsp (13)
Wherein { β1, β2, β3, β4, β5|βi=0 ∨ βi=1,1≤i≤5, i ∈ N }
βiFor the probability coefficent of certain driving behavior, it is 1 to choose, and is otherwise 0.
To sum up, the roadway characteristic collection feature at position (x, y)r(x, y) can be defined as follows：
Step 13, vehicle running environment defines
Define 11 crossing distances, vehicle i and front crossroad ImDistanceIndicate with the fronts vehicle i along with work as
The distance between crossroad stop line in front of preceding track.
Define 12t moment, traffic lights TLiSignal allows action to use vector sigi(t) it indicates.
Defining the crossing of 13 vehicle t moments allows go to action to be expressed as IAM (t).This feature is limited by track permission
Driving behavior PLD and traffic light signals allow to act sigi(t).The Hadamard products of matrix are expressed as, such as formula (16).
IAM (t)=PLD (P (t)) * sigi(t) (16)
To sum up, t moment vehicle running environment feature set featuree(t) it is defined as follows：
featuree(t)={ VID (t), IAM (t) } (17)
Combining step 11, step 12 and step 13, t moment influence feature set feature (t) definition of vehicle drive behavior
For
Feature (t)=featurev(t)∪featurer(P(t))∪featuree(t) (18)
Step 2, vehicle movement prediction model
Step 21, feature extraction and data prediction
The vehicle characteristics directly acquired include Vehicle length L, vehicle width W, car speed v, vehicle acceleration a, vehicle
Travel direction vDir, vehicle intersection turn to vMov, current vehicle position.
The roadway characteristic directly acquired includes track direction lDir, on the left of track can lane change quantity LAL, it is variable on the right side of track
Road amount R AL, the driving behavior SLD that track allows, the driving behavior PLD that crossing area in preparation track allows.
Crossroad collection ISet, road segment segment set RSegSet, the driver behavior that traffic light signals allow can directly be obtained
Sig (t), the road segment segment RSeg where track lane and each car where each car.
According to the definition of step 1, it includes vehicle and front crossroad distance VID, vehicle intersection to need the feature extracted
Allow go to action IAM, training sample label.
Step 211, vehicle is extracted with junction ahead distance feature
The track is obtained in front according to track direction lDir where track lane where vehicle and vehicle on map
The coordinate of two endpoints of stop line AB, i.e. A point coordinates (x at crossroadA, yA) and B point coordinates (xB, yB).From vehicle leading edge
Vertical line is done to straight line AB, acquires length of perpendicular length.Due to road approximation straight way in the data set of research, length can be used
Approximate substitution VID.
AB meets formula (19) in two-dimensional coordinate system.
(yA-yB)·x+(xB-xA)·y+(yB·xA-xB·yA)=0 (19)
Assuming that vehicle location P (t)=(x at this timeC, yC), then the distance length of vehicle to stop line AB meets formula
(20)。
It concentrates the data of each moment point of each car to be calculated according to formula (20) data, obtains feature VID.
Step 212, crossing allows go to action feature extraction
The table that crossroad signal lamp changes over time is obtained from data set, i.e., for arbitrary crossroad IiIt can be with
Obtain sigi(t).Track can be obtained from the map lane markings in data set allows driving behavior PLD.Then IAM features
Extraction sequentially finds the corresponding crossroad in every track according to defining 13, then does Hadamard products to sig (t) and PLD
Obtain IAM features
Step 213, tag extraction
Vehicle movement is denoted as disP, and (Δ x, Δ y) indicate vehicle in (displacement of the t+ Δ t) moment relative to t moment, such as public affairs
Shown in formula (21).
DisP (Δ x, Δ y)=P (x (t+ Δs t), y (t+ Δs t))-P (x (t), y (t)) (21)
Coordinate system is with unit with definition 3.
By feature (t) vectors that the character representation of acquisition is definition, it is by the training sample displacement tag definition of acquisition
Label (t), wherein t indicate the time.Feature (t) and label (t) are normalized using min-max method for normalizing
Processing, as shown in formula (22).
Wherein f indicates characteristic point value, fmaxIndicate the maximum value of this feature point value, fminIndicate this feature point value
Minimum value, fNIndicate the characteristic point value after normalization.Feature (t) and label (t) after normalized remember respectively
For featureN(t) and labelN(t)。
Step 22, vehicle movement prediction model
Step 221, training sample set defines
Aforementioned pretreated training sample set is denoted as train, including training sample feature set and training sample tally set.
Training sample label uses displacement label.featureN(t) splice label in sequenceN(t) it is train samples
Structure.Training sample feature set is denoted as train_x, and training sample tally set is denoted as train_y, is expressed as formula (23)
With formula (24).
Train_y and train_x is corresponded by row.
Step 222, vehicle movement prediction model is trained
Vehicle movement prediction model training step is as follows：
(4) be based on full Connection Neural Network structure (full Connection Neural Network belongs to existing algorithm frame, but input data,
The network number of plies, output layer structure are defined by the present invention), using training sample set train_x as input quantity, calculated using propagated forward
Method calculates (belong to and have algorithm) excitation value of each layer.
(5) full connection BP neural network is initialized using the network structure of study gained, in finally addition output layer, output
For vehicle movement disP (Δ x, Δ y).
(6) mini-batch gradient descent methods (belong to and have algorithm) are used, network is calculated using training tally set train_y
Error, backpropagation, the gradient of counting loss function pair weight matrix and bigoted item, the network parameter of each layer of fine tuning, until
Trigger the end condition of training.(computational methods, which belong to, has method)
Step 3, vehicle drive behavior prediction model
Step 31, Gaussian component defines
5 kinds of driving behaviors are set, i.e. vehicle is kept straight on, and is turned left, and is turned right, and u-turn is as you were, corresponds to 5 Gausses point respectively
Amount.Definition according to the present invention to vehicle movement, each Gaussian component are binary Gaussian Profile.
Step 32, gauss hybrid models are trained
Steps are as follows：
(4) mean value to each Gaussian component and covariance matrix carry out random initializtion, the priori of each Gaussian component
Probability is set as 1/5.
(5) using training sample displacement tally set as input quantity, model is instructed using EM algorithms (belong to and have algorithm)
Practice.
(6) weight of each Gaussian component, mean value and covariance matrix are obtained.
Step 4, vehicle drive behavior prediction
(1) sample characteristics collection to be predicted is defined first, is denoted as test_x, structure and training sample feature set train_x
It is identical.
(2) displacement prediction model for obtaining sampling feature vectors input step 2 to be predicted, the displacement predicted.
(3) the driving behavior prediction model that the displacement input step 3 of prediction gained is obtained calculates sample and belongs to each high
The probability of this distribution, highest probability is driving behavior obtained by the sample predictions.
Beneficial effects of the present invention
The present invention use machine learning techniques, digging vehicle self attributes, road information, running environment and driving behavior it
Between relationship, propose vehicle drive behavior prediction model, achieve the purpose that improve vehicle drive behavior prediction accuracy.
Description of the drawings
Fig. 1 road areas divide schematic diagram
The full Connection Neural Network structural schematic diagrams of Fig. 2
Fig. 3 the method for the present invention flow charts
Specific implementation mode
The specific implementation process of the present invention is as shown in figure 3, including following 4 aspects：
1. vehicle characteristics, roadway characteristic, vehicle running environment characterizing definition
2. vehicle movement prediction model
3. vehicle drive behavior prediction model
4. vehicle drive behavior prediction method
Step 1, defined feature collection, including vehicle characteristics definition, roadway characteristic definition, vehicle running environment characterizing definition.
Step 11, vehicle characteristics define
Vehicle characteristics include length of wagon L, body width W, and car speed, acceleration, current driving direction, crossing turns to
Action, wherein t moment car speed accelerate such as to be respectively labeled as v (t) and a (t), remaining feature is defined respectively as：
Define 1 vehicle heading vDir (t), indicate t moment direction of vehicle movement, between direct north clockwise
Angle indicates, meets：
0≤vDir (t) ＜ 360 ° (7)
Define 2 vehicle intersections and turn to vMov (t), indicate driving behavior of t moment vehicle when by crossroad, with to
The form of amount characterizes, since crossroad does not allow to reverse end for end, the case where being presently considered vehicle straight trip, turn left, turn right, such as
Formula (2).
3 current vehicle position P (t) are defined, indicate t moment vehicle in CA State Plane III in NAD83 coordinates
Two-dimensional coordinate vector in system, vector items unit is foot (ft).Location information is defined as follows：
P (t)=(x (t), y (t)) (3)
CA State Plane III in NAD83 coordinate systems are 1983 North America reference plane (NAD) coordinate systems.
To sum up, the feature set feature of t moment vehiclev(t) it is defined as follows：
featurev(t)={ L, W, v (t), a (t), vDir (t), vMov (t), P (t) } (4)
Step 12, roadway characteristic defines
It is classified as three parts shown in FIG. 1, including crossing area according to urban road feature, keep straight on area and crossing preparation
Area.With a distance from next crossing farther out, road broken line representation, vehicle can free lane change for road straight trip offset.Crossing prepares
Crossroad or T-shaped road junction, road solid line are closed on by area, and vehicle is unable to changing Lane.Track has specific crossing to turn
To limitation, vehicle can only be limited as defined in track in do crossing steering.
Crossroad or T-shaped road junction are abstracted as quadrangle, with its four apex coordinates since the angle of direction northwest by
According to identifying successively clockwise, as shown in Figure 1, label is as follows：
I=(x1, y1, x2, y2, x3, y3, x4, y4) (5)
It defines 4 crossroad set ISet to indicate in survey region, the set of all crossroad compositions.
ISet=(I1, I2..., Im...) and (6)
It defines 5 road segment segments and refers to a section of the road between two adjacent crossroads, by road segment segment both sides four crossway
Mouthful identify the road segment segment.The road segment segment set for defining road i is as follows：
RSegSet (i)=(I1I2, I2I3..., IkIm...) and (7)
Wherein Ik(1≤k≤n, n are maximum crossroad number) indicates the crossroad that number is k.
Every road segment segment includes several tracks, defines crossroad Ik, ImBetween road segment segment track set it is as follows：
IkIm=(lid1, lid2..., lidn) (8)
Wherein lidk(1≤k≤n) indicates lane number.
It defines 6 track direction lDir (x, y) and indicates that the direction of travel that track allows, wherein x, y indicate the position in track
Coordinate.Angle defines same vDir.Vehicle is identical as track direction in the travel direction vDir of current lane straight way, even current vehicle
It is in the tracks i, then has
VDir (t)=lDiri(x, y), wherein (x, y)=P (t) (9)
Define on the left of 7 tracks can lane change quantity LAL (left available lanes) be under certain track current location,
The number of lanes that vehicle can change to the left.By the current location (x, y) of current lane i Lane Searches in the same direction to the left, until meeting
Indicate that vehicle can not lane change to the left or until searching leftmost side track in the same direction to solid line.The number of lanes searched is vehicle
It can lane change quantity LAL on the left of roadi(x, y).
Define on the right side of 8 tracks can lane change amount R AL (right available lanes) be in certain track current location
Under, number of lanes that vehicle can change to the right.By current location (x, the y) Lane Searches in the same direction to the right of current lane i, until
It encounters solid line and indicates that vehicle can not lane change to the right or until searching rightmost side track in the same direction.The number of lanes searched is
It can lane change amount R AL on the right side of tracki(x, y).
Defining 9 straight trip area tracks allows driving behavior to include straight trip, lane change to the left and to the right lane change.Definition vector sld tables
Show that the basic driving behavior that track allows is as follows：
The driving behavior SLD that straight trip area track allows has
The driving behavior for defining the permission of 10 crossing area in preparation tracks includes straight trip, is turned left, right-hand bend and u-turn.Define to
It is as follows to measure the basic driving behavior that pld indicates that track allows：
The driving behavior PLD that crossing area in preparation track allows can be expressed as：
PLD (x, y)=β1(x, y) pldst+β2pldtl+β3pldtr+β4pldta+β5pldsp (13)
Wherein { β1, β2, β3, β4, β5|βi=0V βi=1,1≤i≤5, i ∈ N }
βiFor the probability coefficent of certain driving behavior, it is 1 to choose, and is otherwise 0.
To sum up, the roadway characteristic collection feature at position (x, y)r(x, y) can be defined as follows：
Step 13, vehicle running environment defines
Vehicle running environment such as vehicle and junction ahead distance, traffic light signals attribute etc. to driving behavior have directly or
Person influences indirectly.
Define 11 crossing distances, vehicle i and front crossroad ImDistanceIndicate with the fronts vehicle i along with work as
The distance between crossroad stop line in front of preceding track.
Define 12t moment, traffic lights TLiSignal allows action to use vector sigi(t) it indicates.
Defining the crossing of 13 vehicle t moments allows go to action to be expressed as IAM (t).This feature is limited by track permission
Driving behavior PLD and traffic light signals allow to act sigi(t).The Hadamard products of matrix are expressed as, such as formula (16).
IAM (t)=PLD (P (t)) * sigi(t) (16)
To sum up, t moment vehicle running environment feature set featuree(t) it is defined as follows：
featuree(t)={ VID (t), IAM (t) } (17)
Combining step 11, step 12 and step 13, t moment influence feature set feature (t) definition of vehicle drive behavior
For
Feature (t)=featurev(t)∪featurer(P(t))∪featuree(t) (18)
Step 2, vehicle movement prediction model
Vehicle drive behavior is by vehicle self attributes, travel attribute and running environment attribute etc. in urban road
Many factors influence.The present invention utilizes advantage of the neural network in terms of excavating high dimensional nonlinear data, from a variety of influence vehicles
Vehicle movement prediction network model is trained in the feature of traveling behavior.Model training is divided into three parts：Feature extraction, data are pre-
Processing, displacement prediction network training.
Step 21, feature extraction and data prediction
The vehicle characteristics directly acquired include Vehicle length L, vehicle width W, car speed v, vehicle acceleration a, vehicle
Travel direction vDir, vehicle intersection turn to vMov, current vehicle position.
The roadway characteristic directly acquired includes track direction lDir, on the left of track can lane change quantity LAL, it is variable on the right side of track
Road amount R AL, the driving behavior SLD that track allows, the driving behavior PLD that crossing area in preparation track allows.
Crossroad collection ISet, road segment segment set RSegSet, the driver behavior that traffic light signals allow can directly be obtained
Sig (t), the road segment segment RSeg where track lane and each car where each car.
According to the definition of step 1, it includes vehicle and front crossroad distance VID, vehicle intersection to need the feature extracted
Allow go to action IAM.Since the present invention using machine learning techniques when training, network is adjusted using supervised learning mode
Parameter, it is also necessary to extract training sample label.
Step 211, vehicle is extracted with junction ahead distance feature
The track is obtained in front according to track direction lDir where track lane where vehicle and vehicle on map
The coordinate of two endpoints of stop line AB, i.e. A point coordinates (x at crossroadA, yA) and B point coordinates (xB, yB).From vehicle leading edge
Vertical line is done to straight line AB, acquires length of perpendicular length.Due to road approximation straight way in the data set of research, length can be used
Approximate substitution VID.
AB meets formula (19) in two-dimensional coordinate system.
(yA-yB)·x+(xB-xA)·y+(yB·xA-xB·yA)=0 (19)
Assuming that vehicle location P (t)=(x at this timeC, yC), then the distance length of vehicle to stop line AB meets formula
(20)。
It concentrates the data of each moment point of each car to be calculated according to formula (20) data, obtains feature VID.
Step 212, crossing allows go to action feature extraction
The table that crossroad signal lamp changes over time is obtained from data set, i.e., for arbitrary crossroad IiIt can be with
Obtain sigi(t).Track can be obtained from the map lane markings in data set allows driving behavior PLD.Then IAM features
Extraction sequentially finds the corresponding crossroad in every track according to defining 13, then does Hadamard products to sig (t) and PLD
Obtain IAM features
Step 213, tag extraction
Vehicle movement is denoted as disP, and (Δ x, Δ y) indicate vehicle in (displacement of the t+ Δ t) moment relative to t moment, such as public affairs
Shown in formula (21).
DisP (Δ x, Δ y)=P (x (t+ Δs t), y (t+ Δs t))-P (x (t), y (t)) (21)
Coordinate system is with unit with definition 3.
By feature (t) vectors that the character representation of acquisition is definition, it is by the training sample displacement tag definition of acquisition
Label (t), wherein t indicate the time.Feature (t) and label (t) are normalized using min-max method for normalizing
Processing, as shown in formula (22).
Wherein f indicates characteristic point value, fmaxIndicate the maximum value of this feature point value, fminIndicate this feature point value
Minimum value, fNIndicate the characteristic point value after normalization.Feature (t) and label (t) after normalized remember respectively
For featureN(t) and labelN(t)。
Step 22, vehicle movement prediction model
Step 221, training sample set defines
Aforementioned pretreated training sample set is denoted as train, including training sample feature set and training sample tally set.
Training sample label uses displacement label.featureN(t) splice label in sequenceN(t) it is train samples
Structure.Training sample feature set is denoted as train_x, and training sample tally set is denoted as train_y, is expressed as formula (23)
With formula (24).
Train_y and train_x is corresponded by row.
Step 222, vehicle movement prediction model is trained
Technical solution of the present invention trains displacement prediction model using full Connection Neural Network, as shown in Fig. 2, input layer receives
Shaped like featureN(t) feature vector, 4-10 layers of the network number of plies, it is one two that activation primitive, which uses Relu functions, output layer,
Dimensional vector, that is, the vehicle movement predicted define same disP (Δ x, Δ y).Training process includes propagated forward and backpropagation.Before
To propagate when first using train_x as input quantity, successively train.After current layer is trained, the hidden layer come will be currently trained
Continue to train as next layer of visible layer.The data of input layer are so transmitted into calculating layer by layer by the node in hidden layer,
Output layer is traveled to always, is made comparisons with final output valve and actual value.If the error that propagated forward is finally calculated
Desired value is not achieved, then enters back-propagation process.Backpropagation is based on mini-batch gradient descent methods, first with training
Tally set train_y calculates network error, successively finds out local derviation of the error function to each weights from back to front by chain rule
Number, i.e., error function calculates the modification amount of each weights to the gradient of weights in conjunction with the pace of learning of setting.It is primary reversed
After propagation, then by propagated forward calculating error, if error reaches desired value, otherwise deconditioning continues next round
Backpropagation, propagated forward process, iteration continues always, until triggering training end condition until.
Vehicle movement prediction model training step is as follows：
(7) propagated forward is utilized using training sample set train_x as input quantity based on full Connection Neural Network structure
Algorithm calculates the excitation value of each layer.
(8) full connection BP neural network is initialized using the network structure of study gained, in finally addition output layer, output
For vehicle movement disP (Δ x, Δ y).
(9) mini-batch gradient descent methods are used, network error is calculated using training tally set train_y, it is reversed to pass
It broadcasts, the gradient of counting loss function pair weight matrix and bigoted item, the network parameter of each layer of fine tuning, until the end of triggering training
Only condition.
The vehicle movement prediction model that training is completed is denoted as H (x).The same feature of definition of xN(t), it is normalized
Feature set afterwards.H (x) is the vehicle movement of prediction, defines same disP (Δ x, Δ y).
Step 3, vehicle drive behavior prediction model
Technical solution of the present invention uses gauss hybrid models, based on the vehicle movement prediction result that step 2 obtains, to vehicle
Driving behavior is clustered.
Step 31, Gaussian component defines
5 kinds of driving behaviors are set, i.e. vehicle is kept straight on, and is turned left, and is turned right, and u-turn is as you were, corresponds to 5 Gausses point respectively
Measure N1(μ1, ∑1)~N5(μ5, ∑5).Wherein each Gaussian component is binary Gaussian Profile, and mean value is bivector, association
Variance matrix is the matrix that size is 2x2.
Step 32, gauss hybrid models are trained
Steps are as follows：
(7) to the mean μ of each Gaussian component1、μ2、μ3、μ4、μ5With covariance matrix ∑1、∑2、∑3、∑4、∑5It carries out
Random initializtion, the prior probability π of each Gaussian componentiIt is set as 1/5, i=1,2,3,4,5.
(8) using training sample displacement tally set as input quantity, model is trained using EM algorithms.
(9) weight of each Gaussian component, mean value and covariance matrix are obtained.
After the completion of model training, calculates sample and belong to some Gaussian Profile NiProbability be
Wherein | ∑i| indicate ∑iDeterminant, Σi -1Indicate ΣiInverse matrix.
Step 4, vehicle drive behavior prediction
(1) sample characteristics collection to be predicted is defined first, is denoted as test_x, structure and training sample feature set train_x
It is identical.
(2) each feature vector feature for concentrating sample characteristics to be predictedN(t) displacement that input step 2 obtains
Prediction model, the displacement H (feature predictedN(t))。
(3) the displacement H (feature of prediction gainedN(t)) the driving behavior prediction model that input step 3 obtains calculates
Sample belongs to the probability P of each Gaussian Profilei, highest probability is driving behavior obtained by the sample predictions, i.e. sample predictions
Gained driving behavior
Ct∈ { 1,2,3,4,5 } corresponds to five kinds of driving behaviors respectively.
Innovative point
A kind of vehicle drive behavior prediction method based on machine learning is proposed, reaches and promotes vehicle drive behavior prediction
The purpose of accuracy.For traffic environment complicated in urban road, existing prediction technique based on vehicle historical track, or
It is aided with simple traffic information, prediction result is simultaneously inaccurate.Patent of the present invention has considered vehicle self attributes, road information
And running environment information, innovatively full Connection Neural Network and gauss hybrid models are combined, utilize full connection nerve
Network solves the ability of the ability and the multi-class cluster of gauss hybrid models of regression problem, from promotion vehicle drive behavior prediction
Accuracy.
Claims (1)
1. a kind of vehicle drive behavior prediction method based on machine learning, which is characterized in that pre- using full Connection Neural Network
Vehicle movement is surveyed to cluster driving behavior using gauss hybrid models according to the displacement of prediction；Specific method includes as follows
Step：
Step 1, defined feature collection, including vehicle characteristics definition, roadway characteristic definition, vehicle running environment characterizing definition.
Step 11, vehicle characteristics define
Vehicle characteristics include length of wagon L, body width W, and car speed, acceleration, current driving direction, crossing turns to dynamic
Make, wherein t moment car speed, acceleration is such as respectively labeled as v (t) and a (t), remaining feature is defined respectively as：
1 vehicle heading vDir (t) is defined, t moment direction of vehicle movement is indicated, with angle clockwise between direct north
It indicates, meets：
0≤vDir (t) ＜ 360 ° (1)
It defines 2 vehicle intersections and turns to vMov (t), driving behavior of t moment vehicle when by crossroad is indicated, with vector
Form characterizes, since crossroad does not allow to reverse end for end, the case where being presently considered vehicle straight trip, turn left, turn right, and such as formula
(2)：
3 current vehicle position P (t) are defined, indicate t moment vehicle in CA State Plane III in NAD83 coordinate systems
Two-dimensional coordinate vector, vector items unit be foot (ft)；Location information is defined as follows：
P (t)=(x (t), y (t))； (3)
CA State Plane III in NAD83 coordinate systems are 1983 North America reference plane (NAD) coordinate systems；
To sum up, the feature set feature of t moment vehiclev(t) it is defined as follows：
featurev(t)={ L, W, v (t), a (t), vDir (t), vMov (t), P (t) } (4)
Step 12, roadway characteristic defines
Crossroad or T-shaped road junction are abstracted as quadrangle, with its four apex coordinates according to suitable since the angle of direction northwest
Clockwise identifies successively, and label is as follows：
I=(x1, y1, x2, y2, x3, y3, x4, y4) (5)
It defines 4 crossroad set ISet to indicate in survey region, the set of all crossroad compositions；
ISet=(I1, I2..., Im...) and (6)
Define 5 road segment segments and refer to a section of the road between two adjacent crossroads, by road segment segment both sides crossroad Lai
Identify the road segment segment；The road segment segment set for defining road i is as follows：
RSegSet (i)=(I1I2, I2I3..., IkIm...) and (7)
Wherein Ik(1≤k≤n, n are maximum crossroad number) indicates the crossroad that number is k；
Every road segment segment includes several tracks, defines crossroad Ik, ImBetween road segment segment track set it is as follows：
IkIm=(lid1, lid2..., lidn) (8)
Wherein lidk(1≤k≤n) indicates lane number；
It defines 6 track direction lDir (x, y) and indicates that the direction of travel that track allows, wherein x, y indicate the position coordinates in track；
Angle defines same vDir.Vehicle is identical as track direction in the travel direction vDir of current lane straight way, even at current vehicle
In the tracks i, then have
VDir (t)=lDiri(x, y), wherein (x, y)=P (t) (9)
Define on the left of 7 tracks can lane change quantity LAL (left available lanes) be the vehicle under certain track current location
The number of lanes that can be changed to the left；By the current location (x, y) of current lane i Lane Searches in the same direction to the left, until encountering reality
Line indicates that vehicle can not lane change to the left or until searching leftmost side track in the same direction；The number of lanes searched is a track left side
It side can lane change quantity LALi(x, y)；
Define on the right side of 8 tracks can lane change amount R AL (right available lanes) be the vehicle under certain track current location
The number of lanes that can be changed to the right；By current location (x, the y) Lane Searches in the same direction to the right of current lane i, until encountering
Solid line indicates that vehicle can not lane change to the right or until searching rightmost side track in the same direction；The number of lanes searched is track
It right side can lane change amount R ALi(x, y)；
Defining 9 straight trip area tracks allows driving behavior to include straight trip, lane change to the left and to the right lane change；Definition vector sld indicates vehicle
The basic driving behavior that road allows is as follows：
The driving behavior SLD that straight trip area track allows has：
The driving behavior for defining the permission of 10 crossing area in preparation tracks includes straight trip, is turned left, right-hand bend and u-turn；
Definition vector pld indicates that the basic driving behavior that track allows is as follows：
The driving behavior PLD that crossing area in preparation track allows can be expressed as：
PLD (x, y)=β1(x, y) pldst+β2pldtl+β3pldtr+β4pldta+β5pldsp
(13)
Wherein { β1, β2, β3, β4, β5|βi=0 ∨ βi=1,1≤i≤5, i ∈ N }
βiFor the probability coefficent of certain driving behavior, it is 1 to choose, and is otherwise 0；
To sum up, the roadway characteristic collection feature at position (x, y)r(x, y) can be defined as follows：
Step 13, vehicle running environment defines
Define 11 crossing distances, vehicle i and front crossroad ImDistanceIt indicates with the fronts vehicle i edge and current vehicle
The distance between crossroad stop line in front of road；
Define 12t moment, traffic lights TLiSignal allows action to use vector sigi(t) it indicates；
Defining the crossing of 13 vehicle t moments allows go to action to be expressed as IAM (t).This feature is limited by the driving of track permission
Behavior PLD and traffic light signals allow to act sigi(t).The Hadamard products of matrix are expressed as, such as formula (16).
IAM (t)=PLD (P (t)) * sigi(t) (16)
To sum up, t moment vehicle running environment feature set featuree(t) it is defined as follows：
featuree(t)={ VID (t), IAM (t) } (17)
Combining step 11, step 12 and step 13, the feature set feature (t) that t moment influences vehicle drive behavior are defined as
Feature (t)=featurev(t)∪featurer(P(t))∪featuree(t) (18)
Step 2, vehicle movement prediction model
Step 21, feature extraction and data prediction
The vehicle characteristics directly acquired include Vehicle length L, vehicle width W, car speed v, vehicle acceleration a, vehicle traveling
Direction vDir, vehicle intersection turn to vMov, current vehicle position；
The roadway characteristic directly acquired includes track direction lDir, on the left of track can lane change quantity LAL, can lane change number on the right side of track
Measure RAL, the driving behavior SLD that track allows, the driving behavior PLD that crossing area in preparation track allows；
Crossroad collection ISet, road segment segment set RSegSet, the driver behavior sig that traffic light signals allow can directly be obtained
(t), the track lane where each car and road segment segment RSeg where each car；
According to the definition of step 1, it includes vehicle and front crossroad distance VID to need the feature extracted, and vehicle intersection allows
Go to action IAM, training sample label；
Step 211, vehicle is extracted with junction ahead distance feature
The track is obtained in front cross according to track direction lDir where track lane where vehicle and vehicle on map
The coordinate of two endpoints of stop line AB, i.e. A point coordinates (x at crossingA, yA) and B point coordinates (xB, yB)；From vehicle leading edge to straight
Line AB does vertical line, acquires length of perpendicular length；Due to road approximation straight way in the data set of research, length can be used approximate
Substitute VID；
AB meets formula (19) in two-dimensional coordinate system；
(yA-yB)·x+(xB-xA)·y+(yB·xA-xB·yA)=0 (19)
Assuming that vehicle location P (t)=(x at this timeC, yC), then the distance length of vehicle to stop line AB meets formula (20)；
It concentrates the data of each moment point of each car to be calculated according to formula (20) data, obtains feature VID；
Step 212, crossing allows go to action feature extraction
The table that crossroad signal lamp changes over time is obtained from data set, i.e., for arbitrary crossroad IiIt can obtain
sigi(t)；Track can be obtained from the map lane markings in data set allows driving behavior PLD；The then extraction of IAM features
According to defining 13, the corresponding crossroad in every track is sequentially found, then doing Hadamard products to sig (t) and PLD obtains
IAM features
Step 213, tag extraction
Vehicle movement is denoted as disP, and (Δ x, Δ y) indicate vehicle in (displacement of the t+ Δ t) moment relative to t moment, such as formula
(21) shown in；
DisP (Δ x, Δ y)=P (x (t+ Δs t), y (t+ Δs t))-P (x (t), y (t)) (21)
Coordinate system is with unit with definition 3；
By feature (t) vectors that the character representation of acquisition is definition, it is by the training sample displacement tag definition of acquisition
Label (t) 0, wherein t indicate the time；Feature (t) and label (t) are normalized using min-max method for normalizing
Processing, as shown in formula (22)；
Wherein f indicates characteristic point value, fmaxIndicate the maximum value of this feature point value, fminIndicate the minimum of this feature point value
Value, fNIndicate the characteristic point value after normalization；Feature (t) and label (t) after normalized are denoted as respectively
featureN(t) and labelN(t)；
Step 22, vehicle movement prediction model
Step 221, training sample set defines
Aforementioned pretreated training sample set is denoted as train, including training sample feature set and training sample tally set；Training
Sample label uses displacement label；featureN(t) splice label in sequenceN(t) be train samples knot
Structure；Training sample feature set is denoted as train_x, and training sample tally set is denoted as train_y, is expressed as formula (23) and public affairs
Formula (24)；
Train_y and train_x is corresponded by row；
Step 222, vehicle movement prediction model is trained
Vehicle movement prediction model training step is as follows：
(1) based on full Connection Neural Network structure, (full Connection Neural Network belongs to existing algorithm frame, but input data, network
The number of plies, output layer structure are defined by the present invention), using training sample set train_x as input quantity, utilize propagated forward algorithm
(belong to and have algorithm) calculates the excitation value of each layer；
(2) full connection BP neural network is initialized using the network structure of study gained to export as vehicle in finally addition output layer
Displacement disP (Δ x, Δ y)；
(3) mini-batch gradient descent methods (belong to and have algorithm) are used, calculating network using training tally set train_y misses
Difference, backpropagation, the gradient of counting loss function pair weight matrix and bigoted item, the network parameter of each layer of fine tuning, until touching
Send out the end condition of training；(computational methods, which belong to, has method)
Step 3, vehicle drive behavior prediction model
Step 31, Gaussian component defines
5 kinds of driving behaviors are set, i.e. vehicle is kept straight on, and is turned left, and is turned right, and u-turn is as you were, corresponds to 5 Gaussian components respectively；Root
Definition according to the present invention to vehicle movement, each Gaussian component are binary Gaussian Profile；
Step 32, gauss hybrid models are trained
Steps are as follows：
(1) mean value to each Gaussian component and covariance matrix carry out random initializtion, the prior probability of each Gaussian component
It is set as 1/5；
(2) using training sample displacement tally set as input quantity, model is trained using EM algorithms (belong to and have algorithm)；
(3) weight of each Gaussian component, mean value and covariance matrix are obtained；
Step 4, vehicle drive behavior prediction
(1) sample characteristics collection to be predicted is defined first, is denoted as test_x, and structure is identical as training sample feature set train_x；
(2) displacement prediction model for obtaining sampling feature vectors input step 2 to be predicted, the displacement predicted；
(3) the driving behavior prediction model that the displacement input step 3 of prediction gained is obtained calculates sample and belongs to each Gauss point
The probability of cloth, highest probability is driving behavior obtained by the sample predictions.
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