CN106056153A - System and method for establishing region division remote damage assessment of different vehicle types based on artificial intelligence supervised learning AdaBoost method - Google Patents

Abstract

The invention relates to a system and method for establishing region division remote damage assessment of different vehicle types based on an artificial intelligence supervised learning AdaBoost method and belongs to the vehicle damage assessment field. The objective of the invention is to solve problems in region detection after a vehicle collision. According to the technical schemes of the invention, a region detection subsystem is adopted to judge collision regions in the vehicle collision; and the region detection subsystem learns region training data so as to generate a region model, wherein the region model is built by adopting the intelligent supervised learning AdaBoost method. With the system and method provided by the technical schemes of the invention adopted, region detection in the vehicle collision can be realized; and a machine learning method is used in the remote damage assessment technical field, so that the accuracy of judgment in a damage assessment process can be improved.

Description

Different automobile types subregion is set up based on artificial intelligence's supervised learning AdaBoost method Remotely loss assessment system and method

Technical field

The invention belongs to car damage identification field, relate to a kind of based on the foundation of artificial intelligence's supervised learning AdaBoost method The long-range loss assessment system in different automobile types subregion and method.

Background technology

During low-speed motion (including low speed links traveling, vehicle parking etc.), take place frequently collision accident for vehicle and lead The Claims Resolution dispute problem caused, long-range setting loss technology by multi-signal in collection vehicle driving process (as speed, acceleration, Angular velocity, sound etc.) and analyzed with signal processing and machine learning techniques, to judge whether collision occurs and collision rift The damage situation of vehicle.

After vehicle crashes, headend equipment is capable of detecting when the generation of collision and intercepts the signal of collision process, Being sent to high in the clouds by wireless network, remote server extracts the eigenvalue of design in advance from the signal received, and uses machine Learning algorithm is analyzed, and first judges the accuracy of crash data, then judges collision object and operating mode situation, to determine Collision Number Create the damage of which kind of grade according to what part of set pair, then go out with reference to amount for which loss settled concurrent according to part injury rating calculation Deliver to insurance company.The detection for vehicle, operating mode, target, part and region can be related to during this.

Summary of the invention

After solving vehicle collision, for the problem of region detection, the present invention proposes has supervision based on artificial intelligence Study AdaBoost method sets up the long-range loss assessment system in different automobile types subregion and method to realize the region inspection during setting loss Survey.

In order to solve above-mentioned technical problem, the technical scheme that the present invention provides is characterized by: including:

Truck type choice subsystem, selects the model data corresponding to vehicle as total data set；

Data classification subsystem, reads CAE emulation data and real vehicle data, and classifies data accordingly；

Collision detection subsystem, it is judged that the most whether vehicle collides；Described collision detection subsystem pair Collision training data carries out learning thus generates collision model, and described collision model is set up and used intelligence supervised learning AdaBoost method；

Operating mode detection subsystem, it is judged that all work informations that collision occurs；Operating mode is instructed by described operating mode detection subsystem White silk data carry out learning thus generate condition model, and described condition model is set up and used intelligence supervised learning AdaBoost side Method；

Region detection subsystem, judges hit region during vehicle collision；Described region detection subsystem, to regional training number According to carrying out learning thus formation zone model, described regional model is set up and is used intelligence supervised learning AdaBoost method.

Beneficial effect: technique scheme, it is possible to achieve for the region detection of vehicle collision, at this of long-range setting loss Technical field employs the method for machine learning, for machine learning method, during setting loss, it determines accuracy rate on To promote；The present invention is by selecting vehicle to import the data corresponding to this vehicle, and data classification is then for model training With the purpose of test and the step that adds；The detection in region is the purpose that the program realizes, and is to obtain through sequence of operations The result arrived.

Accompanying drawing explanation

Fig. 1 is the structural schematic block diagram of system of the present invention.

Detailed description of the invention

In order to the present invention being made apparent explanation, below the technical term that the present invention relates to is made definitions:

Operating mode: all collision informations such as collision angle, direction, target, region；

Vehicle: automobile model；

Target: collision target；

Region: position of collision；

Part: auto parts；

Operating mode detects: detect all collision informations such as this car collision angle, direction, target, region；

Vehicle detects: the automobile model that detection collides with this car；

Target detection: detect this car collision target；

Region detection: detect this car position of collision；

Piece test: detect this car auto parts.

Embodiment 1:

One sets up the long-range loss assessment system in different automobile types subregion based on artificial intelligence's supervised learning AdaBoost method, Including:

Truck type choice subsystem, selects the model data corresponding to vehicle as total data set；

Data classification subsystem, reads CAE emulation data and real vehicle data, and classifies data accordingly；

Collision detection subsystem, it is judged that the most whether vehicle collides；Described collision detection subsystem pair Collision training data carries out learning thus generates collision model, and described collision model is set up and used intelligence supervised learning AdaBoost method；

Operating mode detection subsystem, it is judged that all work informations that collision occurs；Operating mode is instructed by described operating mode detection subsystem White silk data carry out learning thus generate condition model, and described condition model is set up and used intelligence supervised learning AdaBoost side Method；

Region detection subsystem, judges hit region during vehicle collision；Described region detection subsystem, to regional training number According to carrying out learning thus formation zone model, described regional model is set up and is used intelligence supervised learning AdaBoost method.

Described collision detection subsystem includes, collision training module, crash tests module, collision authentication module, described in touch Hitting training module and generate collision model for learning collision training data, crash tests module is for surveying collision Examination data bring the result detecting collision model in collision model into, and collision authentication module uses true sport car data verification collision mould The reliability of type and accuracy rate；

Described operating mode detection subsystem includes, operating mode training module, working condition measurement module, operating mode authentication module, described work Condition training module generates condition model for learning operating mode training data, and described working condition measurement module is for by work Condition test data bring the result detecting condition model in model into, and operating mode authentication module uses true sport car data verification operating mode mould The reliability of type and accuracy rate；

Described region detection subsystem includes, regional training module, domain test module, area verification module, described district Territory training module is for carrying out learning thus formation zone model by regional training data, and domain test module is for surveying region Examination data bring the result of detection zone domain model in model into, and area verification module uses true sport car data verification regional model Reliability and accuracy rate.

Described intelligence supervised learning AdaBoost method includes:

S1. the weight initializing all training examples is 1/N, and wherein N is sample number；

S2. for m=1 ... M, m are the initial value of data volume M, and M is sample number；

A). training Weak Classifier ym () so that it is minimize weighted error function (weighted error function):

${\∈}_m=\underset{n=1}{\overset{N}{\Σ}}{w}_n^{\left(m\right)}I\left(y_m\right(x_n)\≠t_n)$

Wherein: X is sample, n is sample number, and tn is weighting function assigned value, and wn is the weight that sample is corresponding；

B) right of speech α of this Weak Classifier is calculated:

${\mathrm{\α}}_m=ln\left\{\frac{1-{\∈}_m}{{\∈}_m}\right\}.$

C) weight is updated:

$w_{m+1,i}=\frac{w_{mi}}{Z_m}\exp (-{\mathrm{\α}}_m{t}_i{y}_m(x_i\left)\right),i=1,2,...,N$

Wherein: α: right of speech, t: parameter, y: Weak Classifier, x: characteristic quantity, exp: exponential function, w: weight, Z is specification Change the factor；

Wherein:

It is standardizing factor, make all w's and be 1；

S3. last grader is obtained:

$Y_M\left(x\right)=sign\left(\underset{m=1}{\overset{M}{\Σ}}{\mathrm{\α}}_m{y}_m\right(x\left)\right).$

Wherein: α: right of speech, m is the initial value of data volume M, and M is sample number, y: Weak Classifier, sign: symbol letter Number.

Embodiment 2:

One sets up the long-range damage identification method in different automobile types subregion based on artificial intelligence's supervised learning AdaBoost method, Comprise the following steps:

Step one. select the model data corresponding to vehicle as total data set；

Step 2. read CAE emulation data and real vehicle data, and accordingly data are classified；

Step 3. judge the most whether vehicle collides；Described collision detection subsystem is to collision training Data carry out learning thus generate collision model, and described collision model is set up and used intelligence supervised learning AdaBoost method；

Step 4. judge all work informations that collision occurs；Operating mode training data is entered by described operating mode detection subsystem Row learns thus generates condition model, and described condition model is set up and used intelligence supervised learning AdaBoost method；

Step 5. judge hit region during vehicle collision；Described region detection subsystem, to regional training data Practising thus formation zone model, described regional model is set up and is used intelligence supervised learning AdaBoost method.

Comprise the concrete steps that:

Step 3 includes:

S3.1. use collision detection subsystem that CAE collision simulation data are processed, then classify to produce collision to it Training data and crash tests data；

S3.2. in collision training module, collision training data learnt and produces collision model, carrying out simulated crash The effect of training data；

S3.3. crash tests data are used to carry out the result of test collisions model in crash tests module；

S3.4. use true sport car data as collision checking data and to bring collision authentication module into, verify collision mould The accuracy of type；

Step 4 includes:

S4.1. CAE operating mode emulation data are processed by applying working condition detection subsystem, then it is carried out classification generation operating mode instruction Practice data and working condition measurement data；

S4.2. in operating mode training module, operating mode training data learnt and produce condition model, carrying out simulated condition The effect of training data；

S4.3. in working condition measurement module, applying working condition test data carry out the result of measurement condition model；

S4.4. use true sport car data as operating mode checking data and to bring operating mode authentication module into, verify operating mode mould The accuracy of type；

Step 5 includes:

S5.1. use region detection subsystem that CAE simulation of domain data are processed, then classify to produce region to it Training data and domain test data；

S5.2. in regional training module, regional training data learnt and produce regional model, carrying out simulated domain The effect of training data；

S5.3. domain test data are used to carry out the result of test zone model in domain test module；

S5.4. use true sport car data as area validation data and to bring area verification module into, carry out validation region mould The accuracy of type.

Described intelligence supervised learning AdaBoost method includes:

S1. the weight initializing all training examples is 1/N, and wherein N is sample number；

S2. for m=1 ... M, m are the initial value of data volume M, and M is sample number；

A). training Weak Classifier ym () so that it is minimize weighted error function (weighted error function):

${\∈}_m=\underset{n=1}{\overset{N}{\Σ}}{w}_n^{\left(m\right)}I\left(y_m\right(x_n)\≠t_n)$

Wherein: X is sample, n is sample number, and tn is weighting function assigned value, and wn is the weight that sample is corresponding；

B) right of speech α of this Weak Classifier is calculated:

${\mathrm{\α}}_m=ln\left\{\frac{1-{\∈}_m}{{\∈}_m}\right\}.$

C) weight is updated:

$w_{m+1,i}=\frac{w_{mi}}{Z_m}\exp (-{\mathrm{\α}}_m{t}_i{y}_m(x_i\left)\right),i=1,2,...,N$

Wherein: α: right of speech, t: parameter, y: Weak Classifier, x: characteristic quantity, exp: exponential function, w: weight, Z is specification Change the factor；

Wherein:

It is standardizing factor, make all w's and be 1；

S3. last grader is obtained:

$Y_M\left(x\right)=sign\left(\underset{m=1}{\overset{M}{\Σ}}{\mathrm{\α}}_m{y}_m\right(x\left)\right).$

Wherein: α: right of speech, m is the initial value of data volume M, and M is sample number, y: Weak Classifier, sign: symbol letter Number.

Embodiment 3:There is the technical scheme identical with embodiment 1 or 2, more specifically:

Conceptual data collection in such scheme: be entirely CAE emulation data and sport car data；Be divided into three parts as follows

1. training dataset: be used to training pattern or determine model parameter (CAE emulation data and sport car data).

2. checking data set: be used to do Model Selection (model selection), i.e. does the final optimization pass and really of model Fixed (CAE emulation data and sport car data).

3. test data set: the Generalization Ability of the model then trained for purely test.(CAE emulates data With sport car data).

In the present embodiment also to the filtering related to during setting loss, weighting choose, feature extraction, normalization, eigentransformation Have been described.

1. wave filter technology: the filtering method realized includes FIR filtering, FIR Chebyshev approximation, Chebyshev's filter Ripple, butterworth filter etc., the Filtering.m file in mastery routine realizes.Each wave filter is common wave filter, Matlab has corresponding function to realize, and specific algorithm refers to signal processing professional book.Provide the interior of FIR filter herein Hold and the introduction of flow process.

Limited impulse response digital filter (FIR, Finite Impulse Response) be a kind of full zero point be System, the design of FIR filter is ensureing that amplitude characteristic meets the colleague that technology requires, it is easy to accomplish that strict linear phase is special Property, so being the outstanding advantages of FIR filter according to there being stable and linear phase characteristic.Chebyshev approximation is the ripples such as one Approximatioss, it is possible to make error frequency band be uniformly distributed, to same technical specification, this filter order sending out needs shoulder to shoulder is low, For the wave filter of same exponent number, this approximatioss maximum error is minimum, and the key step of its design is as follows:

Step 1: the setting of filter parameter

The parameter of wave filter includes: cut-off frequecy of passband, stopband cut-off frequency, passband maximum attenuation and stopband minimum decline Subtract；

Step 2: be arranged on passband and the amplitude-frequency response of stopband coideal

Step 3: be scheduled on the weighting on cut-off frequecy of passband and stopband cut-off frequency point

Step 4: utilize Equation for Calculating Chebyshev approximation filter coefficient

Step 5: preserve coefficient

Step 6: extraction coefficient carries out data filtering

Wherein: the guarantee signal that is disposed to of filter parameter does not haves distortion now during processing As, the cut-off frequency of filtered signal and sample frequency need to meet Nyquist's theorem, the most after the filtering signal Highest frequency not can exceed that the 1/2 of original signal sample frequency, otherwise arises that Lou frequency phenomenon.According to the signal in current project The sample frequency of collection plate is mainly 50Hz and 1KHz, according to formula F as a example by 50Hz_Cut-off< 50/2, therefore select filter cutoff Frequency is below 25.

2. Feature Extraction Technology: feature extraction is carried out on collision alarm.Judge that the feature that collision uses includes window Acceleration in difference between acceleration maxima and minima, window in the maximum of acceleration absolute value, window in mouthful In average energy (in window the quadratic sum of acceleration a little divided by counting), window, the absolute value of each point slope is average Value.

Judge the feature that part category is used include the average energy between speed, acceleration peak to peak, Amplitude between maximum and minima/width between the two, acceleration maximum, acceleration minima, maximum place The width of half-wave, minima place half-wave width, maximum and minima between difference, between peak to peak Span, the meansigma methods of absolute value of each point slope, signal carry out each of the signal after Fourier transform in 0～38 frequency ranges The amplitude of frequency component.

3. normalization technology: that causes classification task to eliminate the dimension between feature or order of magnitude difference is unfavorable Impact, needs to be normalized characteristic so that have comparability between each eigenvalue, it is to avoid the spy that numerical value is bigger Levy and flood the feature that numerical value is less.Original characteristic is after normalized, and each feature is in identical codomain scope. Owing to the performance of Z-Score is more preferable, use Z-Score as method for normalizing.

4. feature transform technique: in the case of feature is more, for the dependency eliminated between feature and reduce redundancy Feature, needs to convert feature, carrys out reflected sample information with the fewest new feature.In the less situation of experiment sample Under (practical situation of this project) reduce too much intrinsic dimensionality, moreover it is possible to avoid sending out of over-fitting or poor fitting to a certain extent Raw.According to actual needs, the eigentransformation the most realized is PCA.Being found through experiments, PCA divides for improving this project Class performance there is no help, has declined, and this is that the feature owing to being used at present is less, does not has redundancy feature, therefore PCA wouldn't be used, but be as being stepped up of subsequent characteristics, however not excluded that use the probability of PCA later.

In accompanying drawing 1, record: the Truck type choice subsystem that Truck type choice is in the present invention；Data categorization module is Data classification subsystem in the present invention；The collision detection subsystem that collision judgment module is in the present invention；Operating mode detection mould Block is the operating mode detection subsystem of the present invention；Vehicle detection module is the vehicle detection subsystem of the present invention；Piece test Module i.e. piece test subsystem；Module of target detection is the target detection subsystem of the present invention, and region detection module is The region detection subsystem of the present invention.

The above, only the invention preferably detailed description of the invention, but the protection domain of the invention is not Being confined to this, any those familiar with the art is in the technical scope that the invention discloses, according to the present invention The technical scheme created and inventive concept thereof in addition equivalent or change, all should contain the invention protection domain it In.

Claims (6)

1. set up the long-range loss assessment system in different automobile types subregion based on artificial intelligence's supervised learning AdaBoost method, its It is characterised by, including:

Truck type choice subsystem, selects the model data corresponding to vehicle as total data set；

Data classification subsystem, reads CAE emulation data and real vehicle data, and classifies data accordingly；

Collision detection subsystem, it is judged that the most whether vehicle collides；Described collision detection subsystem is to collision Training data carries out learning thus generates collision model, and described collision model is set up and used intelligence supervised learning AdaBoost side Method；

Operating mode detection subsystem, it is judged that all work informations that collision occurs；Operating mode is trained number by described operating mode detection subsystem Generating condition model according to carrying out learning, described condition model is set up and is used intelligence supervised learning AdaBoost method；

Region detection subsystem, judges hit region during vehicle collision；Regional training data are entered by described region detection subsystem Row learns thus formation zone model, and described regional model is set up and used intelligence supervised learning AdaBoost method.

2. set up different automobile types subregion based on artificial intelligence's supervised learning AdaBoost method as claimed in claim 1 remote Journey loss assessment system, it is characterised in that

Described collision detection subsystem includes, collision training module, crash tests module, collision authentication module, described collision is instructed Practicing module and generate collision model for learning collision training data, crash tests module is for by crash tests number According to bringing the result detecting collision model in collision model into, collision authentication module uses true sport car data verification collision model Reliability and accuracy rate；

Described operating mode detection subsystem includes, operating mode training module, working condition measurement module, operating mode authentication module, and described operating mode is instructed Practicing module and generate condition model for learning operating mode training data, described working condition measurement module is for surveying operating mode Examination data bring the result detecting condition model in model into, and operating mode authentication module uses true sport car data verification condition model Reliability and accuracy rate；

Described region detection subsystem includes, regional training module, domain test module, area verification module, and described region is instructed Practicing module to be used for carrying out learning thus formation zone model by regional training data, domain test module is for by domain test number According to bringing the result of detection zone domain model in model into, area verification module uses the reliable of true sport car data verification regional model Property and accuracy rate.

3. such as claim 1 or 2, to set up different automobile types subregion based on artificial intelligence's supervised learning AdaBoost method long-range Loss assessment system, it is characterised in that described intelligence supervised learning AdaBoost method includes:

S1. the weight initializing all training examples is 1/N, and wherein N is sample number；

S2. for m=1 ... M, m are the initial value of data volume M, and M is sample number；

A). training Weak Classifier ym () so that it is minimize weighted error function (weighted error function):

Wherein: X is sample, n is sample number, and tn is weighting function assigned value, and wn is the weight that sample is corresponding；

B) right of speech α of this Weak Classifier is calculated:

C) weight is updated:

Wherein: α: right of speech, t: parameter, y: Weak Classifier, x: characteristic quantity, exp: exponential function, w: weight, Z for standardization because of Son；

Wherein:

It is standardizing factor, make all w's and be 1；

S3. last grader is obtained:

Wherein: α: right of speech, m is the initial value of data volume M, and M is sample number, y: Weak Classifier, sign: sign function.

4. set up the long-range damage identification method in different automobile types subregion based on artificial intelligence's supervised learning AdaBoost method, its It is characterised by, comprises the following steps:

Step one. select the model data corresponding to vehicle as total data set；

Step 2. read CAE emulation data and real vehicle data, and accordingly data are classified；

Step 3. judge the most whether vehicle collides；Collision training data is learnt thus generates and touch Hitting model, described collision model is set up and is used intelligence supervised learning AdaBoost method；

Step 4. judge all work informations that collision occurs；Operating mode training data is learnt thus generates condition model, Described condition model is set up and is used intelligence supervised learning AdaBoost method；

Step 5. judge hit region during vehicle collision；Regional training data are learnt thus formation zone model, described Regional model is set up and is used intelligence supervised learning AdaBoost method.

5. set up different automobile types subregion based on artificial intelligence's supervised learning AdaBoost method as claimed in claim 4 remote Journey damage identification method, it is characterised in that comprise the concrete steps that:

Step 3 includes:

S3.1. use collision detection subsystem that CAE collision simulation data are processed, then classify to produce collision training to it Data and crash tests data；

S3.2. in collision training module, collision training data learnt and produces collision model, carrying out simulated crash training The effect of data；

S3.3. crash tests data are used to carry out the result of test collisions model in crash tests module；

S3.4. use true sport car data as collision checking data and to bring collision authentication module into, verify collision model Accuracy；

Step 4 includes:

S4.1. CAE operating mode emulation data are processed by applying working condition detection subsystem, then it is carried out classification generation operating mode training number According to working condition measurement data；

S4.2. in operating mode training module, operating mode training data learnt and produce condition model, carrying out simulated condition training The effect of data；

S4.3. in working condition measurement module, applying working condition test data carry out the result of measurement condition model；

S4.4. use true sport car data as operating mode checking data and to bring operating mode authentication module into, verify condition model Accuracy；

Step 5 includes:

S5.1. use region detection subsystem that CAE simulation of domain data are processed, then classify to produce regional training to it Data and domain test data；

S5.2. in regional training module, regional training data learnt and produce regional model, carrying out simulated domain training The effect of data；

S5.3. domain test data are used to carry out the result of test zone model in domain test module；

S5.4. use true sport car data as area validation data and to bring area verification module into, carry out validation region model Accuracy.

6. as described in claim 4 or 5, set up different automobile types subregion based on artificial intelligence's supervised learning AdaBoost method The long-range damage identification method in territory, it is characterised in that described intelligence supervised learning AdaBoost method includes:

S1. the weight initializing all training examples is 1/N, and wherein N is sample number；

S2. for m=1 ... M, m are the initial value of data volume M, and M is sample number；

A). training Weak Classifier ym () so that it is minimize weighted error function (weighted error function):

Wherein: X is sample, n is sample number, and tn is weighting function assigned value, and wn is the weight that sample is corresponding；

B) right of speech α of this Weak Classifier is calculated:

C) weight is updated:

Wherein: α: right of speech, t: parameter, y: Weak Classifier, x: characteristic quantity, exp: exponential function, w: weight, Z for standardization because of Son；

Wherein:

It is standardizing factor, make all w's and be 1；

S3. last grader is obtained:

Wherein: α: right of speech, m is the initial value of data volume M, and M is sample number, y: Weak Classifier, sign: sign function.