EP2054274A1 - Procédé et dispositif de commande de moyens de protection des personnes - Google Patents

Procédé et dispositif de commande de moyens de protection des personnes

Info

Publication number
EP2054274A1
EP2054274A1 EP07787284A EP07787284A EP2054274A1 EP 2054274 A1 EP2054274 A1 EP 2054274A1 EP 07787284 A EP07787284 A EP 07787284A EP 07787284 A EP07787284 A EP 07787284A EP 2054274 A1 EP2054274 A1 EP 2054274A1
Authority
EP
European Patent Office
Prior art keywords
classification
class
feature vector
personal protection
crash
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP07787284A
Other languages
German (de)
English (en)
Inventor
Josef Kolatschek
Joerg Breuninger
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Robert Bosch GmbH
Original Assignee
Robert Bosch GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Robert Bosch GmbH filed Critical Robert Bosch GmbH
Publication of EP2054274A1 publication Critical patent/EP2054274A1/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R21/00—Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
    • B60R21/01—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents
    • B60R21/013—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over
    • B60R21/0132—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over responsive to vehicle motion parameters, e.g. to vehicle longitudinal or transversal deceleration or speed value
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R21/00—Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
    • B60R21/01—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents
    • B60R21/013—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over
    • B60R21/0132—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over responsive to vehicle motion parameters, e.g. to vehicle longitudinal or transversal deceleration or speed value
    • B60R21/01332—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over responsive to vehicle motion parameters, e.g. to vehicle longitudinal or transversal deceleration or speed value by frequency or waveform analysis
    • B60R21/01338—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over responsive to vehicle motion parameters, e.g. to vehicle longitudinal or transversal deceleration or speed value by frequency or waveform analysis using vector analysis
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R21/00—Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
    • B60R21/01—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents
    • B60R21/015—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting the presence or position of passengers, passenger seats or child seats, and the related safety parameters therefor, e.g. speed or timing of airbag inflation in relation to occupant position or seat belt use
    • B60R21/01558—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting the presence or position of passengers, passenger seats or child seats, and the related safety parameters therefor, e.g. speed or timing of airbag inflation in relation to occupant position or seat belt use monitoring crash strength
    • B60R21/0156—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting the presence or position of passengers, passenger seats or child seats, and the related safety parameters therefor, e.g. speed or timing of airbag inflation in relation to occupant position or seat belt use monitoring crash strength by deceleration

Definitions

  • the invention relates to a method and a device for controlling personal protection means according to the preamble of the independent claims.
  • the inventive method for controlling personal protection devices or the inventive device for controlling personal protection means have the advantage that the application of the class boundaries is not limited to one, two or the three-dimensional feature space. In particular, dependencies of the features in rooms larger than the third dimension can be used.
  • the class membership is determined by a linear combination of a nonlinear function with feature values.
  • the invented The method according to the invention preferably uses a so-called support vector machine (SVM). This is well founded by the statistical learning theory.
  • SVM support vector machine
  • the determination of the class boundaries is given by an analytically solvable optimization problem, so that this process can be performed automatically without additional expert knowledge from a computing machine, in particular an evaluation circuit, which can be designed as a microcontroller.
  • a computing machine in particular an evaluation circuit, which can be designed as a microcontroller.
  • the classification quality of the method according to the invention is very high. Due to the setting in class boundary finding, the method according to the invention allows additional free space to be used.
  • the inventive method has a high generalization ability. Ie. there is no danger that the decision-making process will be optimized too much for the data record (training data record) used during the application and therefore offers a poor classification performance for previously unknown data not contained in the training data set.
  • the feature vector is at least two-dimensional according to the independent patent claims.
  • the feature vector is compared with the class boundary. If this is within a corresponding class, then the feature vector is assigned to this class.
  • the accident sensor system can contain several accident sensors, including different types.
  • the interface to the accident sensors can be implemented in terms of hardware or software.
  • a software executed interface on the evaluation in particular a microcontroller, be present.
  • other processors or ASICs can serve as an evaluation circuit.
  • the drive circuit can also be present as an integrated circuit in a control device for controlling personal protection devices.
  • the device can be arranged as a control device for controlling personal protection means or integrated in a control device for controlling safety means.
  • the last control unit can namely also drive a vehicle dynamics control.
  • the class boundary or limits can already be determined in advance.
  • a data-oriented modeling method of a support vector machine can be used. This method is, for example, from Bernhard Schölkopf and Alex Smole: Learning with Kernels, MIT
  • the class boundary is loaded from a memory.
  • the class boundary is determined by means of at least one training vector and by means of a kernel function. This special
  • Training vector is in the support vector machine a so-called support vector, which is shown below, to specific solutions in a constraint for determining the minimum of a function.
  • support vector For the determination of the solution, it is necessary to have the features such that they can be separated with a simple class boundary in the form of a straight line, or with higher-dimensional input data of a hyperplane (ie linear in both cases).
  • the kernel function implicitly allows the features to be brought into such a linearly separable representation without having to perform this step explicitly and thus with a high computational effort.
  • This method of the support vector machine provides an efficient and well reproducible way to design a driving algorithm. In particular, this allows complicated classification tasks to be solved.
  • the support vector machine method enables expert knowledge, which is necessary in the prior art solutions, to be minimized or even eliminated altogether. This also makes an algorithm easier to understand and interpret.
  • the resource requirement for displaying the class boundary (characteristic curve) is reduced and the application effort is reduced.
  • the classification is carried out in binary form. This is easy to implement and, by using a tree structure, allows for a grading with a successive refinement. In each case, binary classifiers are provided at the branches. Thus, it is then possible to realize complicated classification problems by modular assembly of such binary classifiers. By omitting unneeded binary classifiers within the tree, the inventive method or device according to the invention can be simplified to a meaningful level. In extreme cases, it is then reduced to a simple binary classifier.
  • one tree can for example determine the crash severity and the other tree regardless of the crash type. This can then be reconnected later to find the right drive.
  • Airbags If both trees use simple single binary classifiers, which only know the classes Fire and NoFire respectively, a different control of the restraining means can nevertheless be realized in this way.
  • the activation of the retaining means thus takes place on the basis of the accident situation determined by the classifier. For example, with a simple classifier Fire / NoFire, the drive signal, upon detection of the Fire class, causes the immediate activation of an associated personal protection device.
  • the drive signal uses z. B. the information about the crash type and the crash severity together. For this purpose, in a special table for each combination of these two size a specific set of personal protection deposited, which is then activated.
  • z. B. the information about the crash type and the crash severity together.
  • a specific set of personal protection deposited which is then activated.
  • a feature vector measured by measurement belongs to an event of that class.
  • the method according to the invention and in particular the support vector machine algorithm can be used in an advantageous manner.
  • the quality or sensitivity can then typically be set via a parameter V.
  • a misuse recognition system can be set up. Ie. all feature vectors that are outside this class are trigger cases.
  • a regression can be used to generate a continuous value for the classification.
  • This is particularly advantageous in the case of a classification which describes a steady increase of a certain property, for example when the class describes crash severity or crash speeds.
  • the advantage here is that in such a case the output of the system consists not only of the discrete numerical values but of a real numerical value of a continuous range of values.
  • the corresponding methods are also described in Schölkopf et al. as described above.
  • the numerical value (s) can then be Therefore, tables are assigned to a specific activation pattern of personal protective equipment.
  • the at least two features are determined from a time block of the at least one signal. These are about a specific
  • the feature vector consists of the signal characterizing variables within this block. This may be, for example, the mean of the sensor data, the variance or higher moments, the first integral, the second integral, the coefficients of a wavelet decomposition, a Fourier decomposition, the index values of a codebook, if a vector quantization is applied to the input data within the block applies. Likewise, the coefficients of a polynomial regression can be determined.
  • the one or more selected feature quantities can be determined in one step at the time of the end of the block or can also be determined continuously or recursively with the arrival of the data, ie the signal.
  • the block length T may also contain appropriately processed data from various sensors with optionally different sensing principles. If different blocks are used, these blocks may also overlap or be separated in time.
  • the use of a feature vector allows features of the feature vector to be formed from signals from different sensors. This allows a comprehensive description of the accident event.
  • a computer program which runs on the control unit, in particular of the evaluation circuit, such as a microcontroller, for example.
  • This computer program may be written in an object-oriented language or other common computer languages.
  • this computer program can be used as a computer program product on a data carrier.
  • ger which is machine-readable, such as a hard disk, an electronic memory such as an EEPROM or on a magneto-optical disk or an optical disk such as a DVD or CD.
  • FIG. 1 shows a block diagram of the device according to the invention
  • FIG. 2 shows a software structure on the microcontroller
  • FIG. 3 shows a flow chart of the method according to the invention
  • FIG. 4 shows a data flow diagram of the method according to the invention
  • FIG. 5 shows a further data flow diagram of the method according to the invention
  • FIG. 6 shows a block structure
  • Figure 7 is a tree structure.
  • Support Vector Machine Algorithm The basic idea of the Support Vector Machine Algorithm is explained below, which can preferably be used for the method according to the invention.
  • the SVM algorithm is, in the simplest case, able to perform a binary classification, ie to assign an unknown data vector to one of the two classes on the basis of a training data set of data vectors. Since the two classes are not necessarily separable by a simple class boundary in the form of a straight line, or in the case of higher-dimensional input data, a hyperplane (ie linear in both cases), they would have to be mapped into a higher-dimensional feature space by a transformation. where possible. In the original space, in turn, this separating hyperplane would correspond to a nonlinear separation surface.
  • the SVM algorithm With the aid of a kernel function, the SVM algorithm now allows to calculate this separation surface without first performing the mapping into the feature space. This significantly reduces the demands on the computing power, makes the implementation of the classification technically possible in many cases (above all those with a high-dimensional feature space). Since the non-linear separation surface in the input data space can in each case be traced back to a linear one in the feature space, the generalizability of the classification method can be set well.
  • Each of the 1 feature vectors Xj of a training data set can be combined with the class information yj to form a data pair z ⁇ .
  • the two classes are assigned the values +1 and -1, where z. For example, + l can mean Fire crash and -1 can be NoFire crash.
  • the normalized edge can be expressed as
  • the scalar product ( ⁇ ⁇ ⁇ ⁇ )) can be replaced by a so-called kernel function k ( xj , xp), which yields the same result, thus leaving out the projection into the higher-dimensional space
  • the solution is calculated directly in the lower dimensional input space, which is called a "core trick.”
  • kernel function kernel, for example, is:
  • the method according to the invention or the device according to the invention is trained in an off-line phase, ie before use in the vehicle, ie. H. the core, class boundaries, or support vectors are determined based on training data.
  • This information is then stored in the control unit in a suitable form, that is stored in a memory and then forms the classifier for the online operation of the device according to the invention or of the method according to the invention.
  • the device according to the invention can determine the class limits online via the listed equations or calculate the class affiliation directly. Of course it is also possible to store the class boundaries directly.
  • the data processing takes place as follows: First, a recording of the signals of the accident sensors is made and there is a feature extraction. Then the classification is carried out with the inventive
  • FIG. 1 illustrates in a block diagram the device according to the invention.
  • the device according to the invention is embodied here by way of example as a control device for the activation of personal protection devices.
  • the control unit SG a the control unit for the activation of personal protection devices.
  • Control unit which is configured only for controlling personal protection means PS, but it is alternatively possible that it is a control device for controlling safety means in general and can also make interventions in a vehicle dynamics control or a braking system.
  • the control unit SG has as a central element a microcontroller ⁇ C.
  • This microcontroller ⁇ C is an evaluation circuit according to the independent device claim. Alternatively, it is possible to use other processor types or an ASIC. It may even be possible to use a discretely constructed circuit.
  • the microcontroller .mu.C is connected to a memory S via a data input / output.
  • This memory can be a permanently descriptive be a volatile memory, as it is a so-called RAM in the usual way. However, combinations of memories which can also record data permanently under the name S are also possible. In particular, such memories from which according to the invention the class boundary or the core function and the support vectors can be loaded in order to determine this class boundary.
  • two interfaces IF1 and IF2 are connected to the microcontroller .mu.C, which are embodied here as discrete components. Ie. they are integrated circuits and convert signals from sensors located outside of the SG control unit into a data format that the microcontroller ⁇ C can process efficiently.
  • an acceleration sensor system BS1 which is located within the control unit SG, is connected to the microcontroller .mu.C via a data input. This acceleration sensor can detect accelerations at least in the vehicle longitudinal direction. Usually, however, it is possible that this acceleration sensor system BS1 also detects accelerations transversely or obliquely to the vehicle longitudinal direction. An acceleration sensor in vehicle vertical direction is possible.
  • the microcontroller .mu.C now has a software interface via which the sensor system BS1 is connected to the microcontroller .mu.C.
  • the sensor system BS1 can transmit its data analog or digital to the microcontroller .mu.C.
  • the acceleration sensor is usually constructed, d. H. a micromechanical element provides for the detection of accelerations. Alternatively, it is possible that further sensor types, such as a structure-borne noise sensor or a rotation rate sensor are arranged in the control unit SG.
  • the pressure sensor P is preferably arranged in the side parts of the vehicle to detect a side impact.
  • the pressure sensor P detects an air pressure, the by a
  • the acceleration sensor BS2 may be installed in the vehicle front, for example, to detect a pedestrian impact or a frontal impact. In this case, the acceleration sensor BS2 is installed behind the bumper, for example or on the radiator grille. It is additionally or instead possible that the acceleration sensor system BS2 is installed in the sides of the vehicle. Thus, the acceleration sensor BS2 then serves for the detection or plausibility of a side impact. In this case, the acceleration sensor BS2 can also be sensitive in various directions in order to serve for the plausibility check or detection of specific impact types.
  • the data transmission to the interface IF1 of the pressure sensor P and the acceleration sensor BS2 is usually done digitally. It is possible to use a sensor bus, but in the present case point-to-point
  • an environment sensor system U Connected to the interface IF2 is an environment sensor system U which records data from the surroundings of the vehicle.
  • other collision objects are detected, detected and characterized, for example via a trajectory or the
  • Radar, ultrasound, infrared, lidar or video sensors are possible as environment sensors.
  • Other external sensors, such as indoor sensors, are possible.
  • the microcontroller .mu.C now controls a drive circuit FLIC in response to these sensor signals and its activation algorithm, which serves to activate the personal protection means PS.
  • the drive circuit FLIC has output stages, which are turned on when a drive signal from the microcontroller ⁇ C comes. Also, a logic that the signal of the microcontroller ⁇ C with a signal plausibility or a parallel evaluation, which is not shown here for simplicity, may be present.
  • the personal protective equipment is, for example, airbags, belt tensioners, roll bars, external airbags, a liftable front hood and other possible personal protective equipment for occupant or pedestrian protection. These can be controlled pyrotechnically or reversibly, for example by an electric motor.
  • the microcontroller .mu.C forms a feature vector from the signals of the sensors BS1, P, BS2 and U and determines, based on, for example, stored linear class boundaries, which class this feature vector belongs to. As stated above, it is alternatively possible that the class boundaries in operation can be determined based on support vectors and a kernel function.
  • the microcontroller .mu.C decides whether a drive signal is generated and what content it has. This drive signal is then transmitted to the drive circuit FLIC.
  • the transmission within the control unit SG is usually carried out via the so-called SPI bus.
  • FIG. 2 illustrates important software modules used by the microcontroller ⁇ C.
  • the above-mentioned interface IF3 is shown, which is used for the application of the acceleration sensor BS1.
  • the interface IF3 has the same function as the hardware interfaces IF1 and IF2 to provide the sensor signals.
  • the software module 20 With the software module 20, a feature vector is then formed from the signals of the sensors. This is classified in the manner according to the invention with the software module 21 and based on the classification is then optionally generated with the software module 22, a control signal indicating which personal protection means are to be controlled.
  • Other software modules are possible, but not shown here for the sake of simplicity.
  • FIG. 3 explains in a flow chart the sequence of the method according to the invention.
  • a feature vector is formed from the signals of the sensors. This is classified in method step 301, based on the class boundaries. These are either loaded or determined by the support vectors and the kernel function.
  • a control signal is generated in method step 302, which indicates which personal protection devices are to be controlled.
  • FIG. 4 illustrates in a data flow diagram the function of the device according to the invention or the sequence of the method according to the invention which takes place on the device according to the invention.
  • Block 40 identifies the individual processing steps.
  • the sensors 41, 42 and 43 generate their signals, which are then available as measurement data 48.
  • a feature extract from these signals is generated. tion, since it may be, for example, the signals themselves or filtered, integrated, derived, averaged, etc. processed signals.
  • the feature vector 49 is present.
  • the classification is performed by block 46. This block 46 classifies the feature vector in the manner described above so that the class information 400 is present at the output, which then enters block 47, which is considered to be processing step 405 and the drive signal 401 generated.
  • FIG. 5 illustrates the formation of a feature vector 508.
  • the sensors are located
  • the features which have been extracted are then arranged in a vector, wherein a multiplicity of features can be present per sensor, here four for the sensor 1 and five for the sensor N, for example.
  • the number of sensors is not fixed, but there must be at least one sensor.
  • the installation location of the sensors can also be selected in a variety of ways. The method according to the invention can operate, for example, with centrally installed sensors, but also with peripheral sensors in the vehicle side, in the rear of the vehicle or in the front of the vehicle. Also, a combination, as shown above, this installation locations is possible. Examples of suitable sensors are acceleration sensors, pressure sensors, body sound sensors, temperature sensors or sensors with other physical properties
  • the conversion of the measured individual data in a feature vector is preferably done in time blocks.
  • Feature vectors of more than one time block can be calculated.
  • the block length T which is denoted by Bl, B2 and B3
  • the data of individual sensors which may already be subjected to a certain preprocessing considered in a context.
  • the feature vector consists of the signal within these blocks of characterizing quantities. This can be, for example, the mean value of the sensor the variance or higher moments, the first integral, the second integral, the coefficients of a wavelet decomposition, a Fourier decomposition, the index values of a codebook, if one applies a vector quantization to the input data within the block.
  • the coefficients of a polynomial regression can be determined. The one or more chosen
  • Feature sizes can be determined in one step at the time of the end of the block or can also be determined continuously or recursively with the arrival of the data. In extreme cases, it is also possible to match the block length T of the scanning side of the sensor so that only one data value is contained in each data block, which is then correspondingly converted into a feature vector.
  • the feature vector may also contain correspondingly processed data from various sensors with optionally different sensing principles.
  • the classification then has the task of classifying the event which has generated the feature vector into specific classes.
  • a classification can z. B. from the two classes Fire and NoFire consist (Example 1). In this case, it is a binary classifier. But it is also the accident event more precisely characterizing class divisions conceivable:
  • Ci no triggering event
  • C2 soft barrier crash
  • C3 hard barrier crash
  • Ci no triggering event
  • C2 symmetric crash event
  • Ci crash speed between Okm / h and 10km / h
  • C2 crash speed between 10km / h and 20km / h
  • C3 crash speed between 20km / h and 30km / h
  • C4 crash speed between 30km / h and 40km / h
  • C5 crash speed between 40km / h and 50km / h
  • CQ crash speed between 50km / h and 60km / h
  • such a refined classification can be successively performed.
  • the classification level 70 it is determined with the classifier 74 whether the crash severity is less than the value 4. If this is the case, then it goes to the classifier 75, which determines whether the crash severity ⁇ 2, where we are in the classifier level 71. If so, then the crash severity is determined to be 1 with the classification result as indicated in block 700. If this is not the case, then another classifier level 72 is inserted, so that the classifier 79 determines whether the

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Automotive Seat Belt Assembly (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Complex Calculations (AREA)
  • Feedback Control In General (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un dispositif et un procédé de commande de moyens de protection des personnes, sachant qu'un vecteur caractéristique est formé par un montage évaluateur d'au moins deux caractéristiques provenant au moins d'un signal d'un système de détection des accidents. Le montage évaluateur classifie le vecteur caractéristique dans la dimension correspondante au moyen d'au moins une limite de classe. Le circuit de commande génère un signal de commande, sachant qu'un circuit de commande commande les moyens de protection des personnes en fonction du signal de commande.
EP07787284A 2006-08-16 2007-07-10 Procédé et dispositif de commande de moyens de protection des personnes Withdrawn EP2054274A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102006038151.3A DE102006038151B4 (de) 2006-08-16 2006-08-16 Verfahren und Vorrichtung zur Ansteuerung von Personenschutzmitteln
PCT/EP2007/057008 WO2008019915A1 (fr) 2006-08-16 2007-07-10 Procédé et dispositif de commande de moyens de protection des personnes

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EP2054274A1 true EP2054274A1 (fr) 2009-05-06

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US (1) US8374752B2 (fr)
EP (1) EP2054274A1 (fr)
JP (1) JP2010500227A (fr)
CN (1) CN101506001B (fr)
DE (1) DE102006038151B4 (fr)
WO (1) WO2008019915A1 (fr)

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WO2008019915A1 (fr) 2008-02-21
CN101506001B (zh) 2012-08-01
US20090306858A1 (en) 2009-12-10
US8374752B2 (en) 2013-02-12
CN101506001A (zh) 2009-08-12
DE102006038151A1 (de) 2008-02-21
JP2010500227A (ja) 2010-01-07
DE102006038151B4 (de) 2020-06-10

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