EP1299870A1 - Method and device for classifying vehicles - Google Patents
Method and device for classifying vehiclesInfo
- Publication number
- EP1299870A1 EP1299870A1 EP01955423A EP01955423A EP1299870A1 EP 1299870 A1 EP1299870 A1 EP 1299870A1 EP 01955423 A EP01955423 A EP 01955423A EP 01955423 A EP01955423 A EP 01955423A EP 1299870 A1 EP1299870 A1 EP 1299870A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- data
- vehicles
- electromagnetic
- signature
- signals
- 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.)
- Granted
Links
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Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/042—Detecting movement of traffic to be counted or controlled using inductive or magnetic detectors
Definitions
- the invention relates to the field of techniques for collecting road data, and in particular to counting and / or classifying motor vehicles during their journey on a roadway, for example on a highway.
- the invention relates in particular to a method and a device for classifying vehicles into silhouette categories on the basis of their electromagnetic signature.
- a measurement point on a traffic lane comprises at least two electromagnetic loops 2, 4.
- Each loop consists of a few turns (usually 3 or 4) of conductive wire arranged in the roadway to form a coil, and is placed in a groove, a few centimeters deep.
- Each coil thus formed generally has an inductance of the order of 100 ⁇ H.
- a metallic mass passes through the field, induced currents modify the latter and consequently lead to a variation in the self-inductance of the coil.
- the detection of this phenomenon of variation of inductance is ensured by a detector 6. It can be done by measurement of phase variation, or variation amplitude, or frequency variation or even by measurement of impedance variation.
- a classification can only be carried out in at most 6 length categories.
- a third sensor When a more detailed classification is desired, for example into 14 categories of silhouettes, a third sensor must be added to the two preceding loops, the role of which is to detect the axles when vehicles pass.
- This additional sensor generally consists of a piezoelectric cable.
- a special loop also called “narrow loop” is sometimes used.
- This type of device gives classification results which are generally satisfactory for road operators, but it has a cost which is high. Indeed, such a site is roughly equivalent, in terms of price cost, at 3 sites equipped to assess speeds (including civil engineering and detectors in this price).
- the invention firstly relates to a device for obtaining data of electromagnetic signatures of vehicles, from electromagnetic signals, comprising:
- - Means for determining whether such a digitized signal is an electromagnetic signature signal of a vehicle - Means for calculating, from the digitized signal, electromagnetic signature data for a vehicle, and for dating each electromagnetic signature.
- the device according to the invention therefore implements the measurement of the electromagnetic signature of a vehicle, in order to deduce therefrom digitized, sequenced and dated data. Each digital sample is therefore associated with an instant or an identified time value.
- the invention makes it possible to sequence and date, in a synchronized manner, each signal of electromagnetic signature and each data point of this electromagnetic signature.
- the invention therefore makes it possible to precisely date the passage of each vehicle, or even to associate a time stamp with each electromagnetic signature datum.
- the device includes means which make it possible to determine whether a received signal corresponds to the signature of a vehicle or whether it is only noise.
- the device according to the invention makes it possible to implement only one loop per lane on the roadway. An additional loop is therefore not necessary. One loop per lane is therefore sufficient to carry out measurements such as flow, occupancy rate, speed, vehicle interval, inter-vehicle distance and silhouette category. In the case of two juxtaposed channels, two loops may be used, but with only one loop per channel.
- the device according to the invention makes it possible to identify the category of silhouette of the vehicles, and / or to measure the speed of the vehicles.
- the invention therefore also relates to a system for acquiring electromagnetic signature data of vehicles, comprising:
- the invention also relates to a signal processing device or a data acquisition system according to the invention and as set out above, further comprising classification means for classifying vehicles into at least two categories in function of digitized and sequence electromagnetic signature data or signals.
- classification means which process these electromagnetic signature signals are based on the path of a plurality of decision trees.
- this type of classification is compatible with a number of categories greater than 6, for example 14.
- the invention also relates to a method for processing electromagnetic signature signals from vehicles, comprising:
- the device, the system and the method according to the invention implement a procedure for processing the electromagnetic signature of a vehicle which makes it possible, in particular, to identify its silhouette category in a classification profile which can contain 14 silhouettes.
- a single conventional loop sensor per traffic lane is therefore sufficient to develop the main parameters of road traffic, and in particular the speed, the occupancy rate, the distance between vehicles, the speed of the vehicles, the length of the vehicles and the vehicle silhouette category.
- the invention also relates to a method for generating a program for classifying vehicles into at least two predetermined categories, as a function of digitized signals representative of the electromagnetic signatures of these vehicles, comprising:
- Such a method makes it possible to generate decision trees which can be used in a system and a method according to the invention, as set out above.
- Random data selection can be repeated, and a tree can be generated for each selection. We can thus generate five, ten or even thirty trees.
- a classification method according to the invention which is particularly advantageous since it allows classification into 14 vehicle categories, uses thirty decision trees thus determined.
- FIG. 1 represents a structure of loop sensors according to the prior art for a measurement point on a flow lane of speed / speed type
- FIG. 2 represents a structure of loop sensors according to the invention for a measurement point on a flow lane of the speed / speed type
- FIG. 3 represents a functional diagram of a detection and processing assembly according to the invention
- FIG. 4 represents more precisely means for extracting and shaping signals from a device according to the invention
- FIG. 5 represents an extraction method which can be implemented in the context of the present invention
- FIGS. 6A to 6C represent various examples of electromagnetic signature obtained with a device according to the invention
- FIG. 7 schematically represents vehicles according to a classification in 14 categories of silhouette
- FIG. 8 represents a classification flow diagram
- FIG. 9 shows processing means of a device according to the invention.
- FIG. 10A and 10B respectively show the use of a device according to the invention, on two channels and with a single sensor per channel, and of a device according to the prior art, with two sensors per channel.
- FIG. 11 A to 11C are examples of signatures for various positions of a vehicle with respect to one or two loops.
- - Figure 12 shows an overlay of a signature of a stationary vehicle and a passing vehicle.
- FIG. 13 shows an algorithm for adapting the acquisition scale of a signature.
- Figure 2 shows a loop sensor structure according to the invention.
- a single loop 10, or a single loop sensor, is placed in or on the lane of vehicles.
- an electromagnetic loop sensor consists of a few turns (generally 3 or 4) of conductive wire placed in the roadway to form a coil.
- the loop sensor therefore constitutes the inductive part of an oscillator. In the case of long-term installations, it is placed in a groove, a few centimeters deep, most generally forming a rectangle of 2m x 1.50m with a return 12 (a twisted pair) of a few tens of meters to the detection unit 14. Other geometries and sizes of loops can also be produced, such as the circular geometry illustrated in FIG. 2. With the configuration given as an example, the coil thus formed has an inductance of the order of 100 ⁇ H. The value of the loop takes into account the tuning range of the detector.
- the loop sensor 10 When the detector to which it is connected is energized, the loop sensor 10 becomes the seat of a magnetic field proportional to the inductance of the coil and to the current flowing through it.
- FIG. 3 represents the structure of a device according to the invention, for the extraction and the exploitation of a signal.
- a device for the extraction and the exploitation of a signal.
- Such a device makes it possible to produce an electromagnetic signature, and to digitize it, sequence it and date it. This provides the electromagnetic signature, in real time, for its operation.
- the digitized signal is the set of digital values reflecting the analog evolution of the amplitude of the signal.
- the dating gives the time stamp of the "signature" event.
- the detection box 14 includes detection means 16
- means 18 for processing the detected signals such as one or more CPU cards for processing a microcomputer.
- processing means 18 in turn comprise means 20 for extracting and shaping the signals, as well as means 22 for processing and classification.
- All of these means provide a signal or signals representative of traffic data, on a data bus 19.
- a database 24 of signatures can also be formed.
- the detector 16 includes an internal oscillator, which is associated with the loop 10. The variations in inductance of the loop 10 during the passage of a vehicle 9 tend to modify the frequency of this internal oscillator.
- the variations of the signal obtained are the result, at each instant, of opposite effects due to the metallic mass which transits above the loop: a) effect of the currents induced in the metallic mass which crosses the magnetic field around of the loop, which leads to an increase in frequency and a decrease in the apparent inductance L measured; b) effect of a plunger core in an inductance coil (for example, when passing axles and wheels), which results in a decrease in frequency and an increase in apparent inductance L.
- a digital detector (with microprocessor) counts the number of periods of the internal oscillator to determine the frequency variations.
- ⁇ L / L ValueRead * FACT 000 (1)
- ValueRead is the value given by the detector each time the loop signal is read (this value read is proportional to the frequency variation) and where FACT is a factor which depends on the setting detector sensitivity.
- the detector 16 is a standard detector, which performs analog-to-digital conversion of the frequency variation signals from the internal oscillator. According to one embodiment, it provides:
- Communication from the detector to an external system can be via a serial link or a parallel link.
- a device is chosen for the detector allowing: - detect a vehicle traveling very slowly (less than 1 km / h) or very fast (more than 250 km / h), with a response time of less than 100 ms,
- the detector provides information enabling certain parameters to be determined or calculated, and in particular the sensitivity adjustment, the oscillator frequency, the value of the loop inductance and finally its state (detection or rest) .
- the detector is a standard detector with serial link, of the MTS38Z type manufactured by PEEK. This detector is associated with programmed or specially programmed means so as to process and process the signals.
- the example above concerns a detector which supplies a frequency variation signal, from which the electromagnetic signature can be deduced.
- this signature can be obtained from variations in phases, or amplitudes, or variations in impedance.
- the extraction means 20 cyclically interrogate the detector
- the latter responds by supplying the information on variations in frequency of the oscillator (or variations in phase or in amplitude or impedance) which make it possible to calculate the relative variation ⁇ L / L.
- FIG. 4 shows functionally the means 20 (programmed CPU card) which calculate the variations ⁇ L / L, and which filter these variations, date them, and store them in memory.
- These means 20 include a microprocessor 36, a set of RAM memories 34 (for data storage), a ROM memory 36 (for storage of program instructions).
- a data acquisition card 42 (input / output interface) puts the data supplied by the detector in the format required by the card 20.
- These various elements are connected to a bus 40.
- In the means 20, and in particular in the memory 36 are loaded with the data or the instructions for implementing data processing according to the invention, and in particular for calculating the variations ⁇ L / L.
- These data or instructions for processing the data can be transferred to the memory area 36 from a floppy disk or from any other medium which can be read by a microcomputer or a computer (for example: hard disk, ROM read-only memory, dynamic random access memory DRAM or any other type of RAM memory, compact optical disk, magnetic or optical storage element).
- a microcomputer or a computer for example: hard disk, ROM read-only memory, dynamic random access memory DRAM or any other type of RAM memory, compact optical disk, magnetic or optical storage element.
- the means 20 are further provided with a real-time clock 26, a timer 28, and buffer memories 30, 32.
- the clock and the timer are synchronized, which allows to associate with each data of a signature signal a precise moment
- One of the memories is a rotating buffer which makes it possible to temporarily store the last signal data corresponding to a duration t1, which is of the order of magnitude of the response time of the detector used.
- the rest of the signal is recorded in memory 32.
- the rest of the signal concerns the subsequent or following signal data, corresponding to instants after t1 .
- each value of ⁇ L / L is, in fact, associated with the value corresponding timer. This eliminates the need for an additional sensor for detecting the passage of a vehicle, which simplifies the measurement device which requires only a loop 10, without an additional sensor (see FIG. 1).
- FIG. 5 An example of the operating method of the extraction and shaping means 20 is given in FIG. 5.
- the main steps of this process are as follows (E1 - E6).
- a first step (E1) the timer 28 is synchronized with the real time clock 26, and the basic parameters are acquired.
- the following data are acquired at this stage:
- a second step (E2) the detector data is acquired, for a time t1.
- TR corresponds to the setting on the highest sensitivity, for example on the sensitivity 0.01.
- step (E3) It is then tested (E3) if the detection threshold (set by manual adjustment of the detector) is crossed. If not, there is a return to step (E2).
- Each sample ⁇ L / L is stored in memory 32 with the corresponding value of the timer.
- step E5 the values of the buffer memories 30 and 32 are recovered to form a complete signature of the vehicle, while respecting the timestamp of the timer.
- the correspondence with the clock real time 26 and the timer 28 allows to accurately date the passage of the vehicle.
- step E6 the signature data are formatted and transferred from the means 20 to the analysis means 22.
- the response is retrieved and the individual measurements can then be transferred to the application (for speed calculation, classification in category, etc.).
- the intelligence of the loop detectors can be increased by adding part of the extraction means to it, while respecting the above characteristics.
- the timer 28 (on 4 bytes), as well as the buffers 30 and 32, can usefully reside on the card of the detector 16 to improve the transfer time of the information from the detector and thus increase the resolution of the signature.
- the processing of the analysis and classification system can be partially or totally transferred to the detector card or to an independent processing CPU card.
- the invention is therefore not limited to the single embodiment given as an example because the constituent elements can be on physical supports, distinct or not.
- the timer 28 has, for example, an accuracy of the order of a microsecond. According to one embodiment, this precision can be adapted according to the duration of the signature signal.
- a dynamic scale is used for this purpose, which saves memory space.
- the adaptation of the scale is explained in connection with FIG. 13.
- the algorithm consists in filling two tables Ti and T 2 , at two different speeds, with data of the signature, and in a cyclic manner.
- the filling speed of the table Ti is first selected as being twice that of the table T 2 (steps Se and S). When Ti is filled (test in Se), Ti is emptied and a part of the values of T 2 is transferred there, itself half filled (step S 10 ). The filling speed of Ti is then modified, the filling rate of T 2 remaining unchanged.
- the electromagnetic signature provided by the extraction means 20 is therefore presented in digital and sequenced form, that is to say in the form of a series of values of ⁇ L / L at constant time intervals, each value of ⁇ L / L being associated with a corresponding value of the timer. .-
- each electromagnetic signature signal and each data point of this electromagnetic signature makes it possible to precisely date the passage of the vehicle, or even to associate a time stamp to each datum of electromagnetic signature. This makes it possible to identify on what exact date each vehicle has passed, and in particular to identify precisely all the points or all of the signature data, which is particularly advantageous for discriminating in traffic with several lanes of traffic. simultaneous passage of several vehicles, overlapping of two adjacent sensors by the same vehicle and spurious detections. The known methods or devices do not allow such direct and precise identification.
- the dating is done continuously, or successively for each digitized data, and this from the start of the signature.
- FIG. 6A represents the electromagnetic signature of a light vehicle
- - Figure 6B represents the electromagnetic signature of a three-axle truck
- - Figure 6C represents the electromagnetic signature of a semi-trailer truck.
- the ordinate represents ⁇ L / L and the time is given on the abscissa in units of 1/10 sec.
- a classification method according to the invention which can be implemented using the analysis means 22, is based on the route of a plurality of decision trees.
- a decision tree is a set of tests, organized so as to quickly classify a new object (signature).
- the tree has nodes and branches and each node is made up of a test on a variable.
- the terminal nodes are the classification categories.
- Such a tree is binary, that is to say that it includes tests of the "if ... then ... if not" type which make the progression take place from node to node via the branches.
- the node When the node is terminal, it is then a sheet whose content is the category of the object to be classified.
- the construction of a tree is done from a learning set containing the objects to be classified, with an algorithm for construction or automatic classification generation which aims to minimize the number of tests to be performed to classify.
- the principle of this construction algorithm is to start from a set of examples (learning base) to create a classification tree which aims to minimize the number of tests to perform to classify a new object.
- test variable is the one that best separates the objects present in 2 homogeneous subsets.
- the selection criterion which is used for the "best" separation is based on a Shannon entropy measure. The separation operation is repeated until the subsets now contain only individuals of the same category.
- - category 5 five-axle trucks, with or without trailer
- - category 6 six-axle trucks, with trailer
- - category 9 eight-axle trucks, with trailer, - category 10: heavy vehicles with five or six axles (with semi-trailer),
- category 13 bicycles or motorcycles
- category 14 public works or agricultural machinery.
- K 2 (which corresponds to questions such as: is the vehicle type C14 or not? Or: is the vehicle type C1 or not?).
- each signature was described by a set of temporal variables and so-called frequency variables.
- the other variables contain information concerning the first harmonics, for example the first 8 harmonics: amplitude, phase, harmonic ratio and amplitude ratios. They are obtained after frequency analysis of the signature, for example by Fourier transform.
- N 50
- frequency variables comprising:
- the frequency variables are therefore 51 in number. There are in fact only 50 independent since the ratio of richness in harmonics A7 / A6 is only the inverse of the ratio A6 / A7 already present in the 28 variables of amplitude reports. These variables are used by the automatic classification generation algorithm for the development of decision trees.
- each tree is obtained from a random selection of variables characteristic of the electromagnetic signatures.
- the development of such a set of trees ultimately provides a reliable classification method, giving a deterministic classification result.
- an identifier between 1 and 100 is randomly associated with each variable, and only the variables randomly selected and whose identifier is less than n are retained.
- the quantity of variables chosen may be slightly different from n.
- the selected variables are introduced into the automatic classification generation algorithm so that the latter develops a first decision tree making it possible to carry out a predetermined sort, that is to say making it possible to answer the question "is the vehicle it of the type Ci1 or or of the type Cip (p> 1) ", where the Cip represent p classes or categories chosen from the K initial categories in the set of which all the vehicles are classified.
- new variables are drawn at random to build a second tree, in order to perform the same type of sorting.
- This procedure is repeated until k decision trees are obtained, each tree therefore being constructed from a set of variables drawn at random from the initial variables.
- the k trees are traversed in parallel.
- the classification decision chosen is the category with the highest occurrence at the end of the course of the trees.
- the classification process or the structure of the course of the trees which makes it possible to classify a vehicle is that of FIG.
- the method After sampling the signature and calculating the frequency variables, the method first implements a test to determine whether the vehicle is of type C14 or not. If it is not of category C14, a test (in fact, the result of the combination of 10 trees) determines whether the vehicle is of category C1, or not.
- a test determines the category of the vehicle among categories C5 to C12.
- a test determines the category of the vehicle among categories C2, C3, C4 or C13.
- - no tree is to be covered if it is a C14 type vehicle, - 10 trees are to be covered if it is a C1 type vehicle, - 30 trees are to be covered for other vehicles.
- This process can be adapted for a classification to be carried out in K categories, with K different from 14.
- the signature of a vehicle is introduced into the classification process or algorithm in a format imposed by the processing means 20, 22 (for example: in the form of tables of values the number of which is between 500 and 1000 for long vehicles). These values are representative of the relative variation of inductance ( ⁇ L / L) at constant and regular time intervals. They are for example expressed in multiples of 10 "5 The sampling period is expressed in microseconds. It is for example 0.6 ms, so that the accuracy of the velocity estimate is sufficient for vehicles driven over 100 km / h.
- the algorithm that has been developed is made to work in association with an electromagnetic loop detector whose role is to develop the signature of vehicles.
- This algorithm can also be self-adapting.
- Codes 15 and 16 respectively indicate category C1, with uncertainty, and category "long vehicle”, also with uncertainty.
- decision trees implanted in the code were obtained for a certain sensor geometry (1.5m x 2m loop). Other trees can be adapted for different configurations.
- Estimating vehicle speed is optional.
- the signature curves produced have, at least in the first part, an exponential appearance.
- the calculation of the speed is done according to a particular process which consists in finding the instant when the pace of the signature ceases to follow an exponential law.
- the time between the start of the signature and this particular instant is inversely proportional to the speed of the vehicle.
- FIG. 9 shows functionally the means 22 (programmed CPU card) which in particular implement the sorting methods described above, as well as the processing by Fourier transformation and the extraction of the variables for each signature.
- These means 22 include a microprocessor 50, a set of RAM memories 52 (for data storage), a ROM memory 54 (for storage of program instructions).
- a data acquisition card 58 (input / output interface) puts the digitized and sequenced data supplied by the card 20 into the required format.
- a bus 56 In the means 22, and in particular in the memory 54 are loaded the data or the instructions for implementing a data processing according to the invention (spectral analysis, extraction of the variables for each signature, sorting process).
- This data or instructions for processing the data can be transferred into the memory area 54 from a floppy disk or from any other medium which can be read by a microcomputer or a computer (for example: hard disk, ROM read-only memory, dynamic random access memory DRAM or any other type of RAM memory, compact optical disk, magnetic or optical storage element).
- the data obtained by sorting can also be displayed on peripheral display means such as the screen 23 of a microcomputer 21.
- An operator can then carry out any processing of this data using a keyboard 25, a mouse 27 and any program residing in the microcomputer 21.
- vehicle counting information for example by vehicle category after classification.
- the sorting trees are obtained using a microcomputer, such as the microcomputer 21, programmed to implement an algorithm such as that of J.R. QUILAN already mentioned above.
- the microcomputer has a structure similar to that of FIG. 9.
- the time and frequency variables are obtained from the digital signature signals developed and transmitted (via the link 19) by the card 20.
- Each sorting tree is obtained under form of program whose instructions are stored in a memory area of the microcomputer 21.
- the sorting algorithm such as that of FIG. 8 can then be performed by an operator by calling on these different programs.
- the sorting method according to the invention operates in near real time mode.
- the response time depends mainly on the processor used.
- this response time is less than 50 ms.
- results may appear insufficient for certain categories. This may be due to the fact that the test sample is insufficient. There are vehicles that are rare. But it is also due to the fact that the same vehicle can circulate sometimes with all its axles, sometimes with an axle raised. This state of affairs implies that this vehicle can belong to 2 categories depending on whether or not it has a raised axle.
- Figures 10A and 10B respectively show the use of a device according to the invention, on two channels and with a single sensor per channel, and of a device according to the prior art, with two sensors per channel.
- the data acquisition chain for each sensor is of the type described above in relation to FIGS. 3 and 4 and able to operate using the process described in connection with FIG. 5.
- the dating and synchronization are the same for the two loops or sensors.
- a vehicle may be straddling the two lanes.
- the synchronized dating with the sequencing all in real time, makes it possible to differentiate the case of a single vehicle passing astride the two lanes from the case of two vehicles each passing on one of the lanes .
- FIG. 11A represents the case of a vehicle passing through the center of a single loop.
- FIG. 11B represents the case of a vehicle passing in an offset manner relative to the axis of the single loop.
- FIG. 11C represents the case of a vehicle overlapping on 2 loops arranged as in FIG. 10A. As seen in this figure, the signature is very unbalanced between the two loops.
- FIG. 12 represents the signature of a vehicle which has stopped over a single loop, and to which is superimposed a peak corresponding to the signature of a vehicle which passes but does not stop.
- the system according to the invention therefore makes it possible to discriminate, with a single loop, a vehicle stopped from a passing vehicle.
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Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
FR0009189A FR2811789B1 (en) | 2000-07-13 | 2000-07-13 | METHOD AND DEVICE FOR CLASSIFYING VEHICLES INTO SILHOUETTE CATEGORIES AND FOR DETERMINING THEIR SPEED, FROM THEIR ELECTROMAGNETIC SIGNATURE |
FR0009189 | 2000-07-13 | ||
PCT/FR2001/002292 WO2002007126A1 (en) | 2000-07-13 | 2001-07-13 | Method and device for classifying vehicles |
Publications (2)
Publication Number | Publication Date |
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EP1299870A1 true EP1299870A1 (en) | 2003-04-09 |
EP1299870B1 EP1299870B1 (en) | 2010-05-26 |
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Application Number | Title | Priority Date | Filing Date |
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EP01955423A Expired - Lifetime EP1299870B1 (en) | 2000-07-13 | 2001-07-13 | Method and device for classifying vehicles |
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US (1) | US6865518B2 (en) |
EP (1) | EP1299870B1 (en) |
AT (1) | ATE469412T1 (en) |
AU (1) | AU2001277593A1 (en) |
CA (1) | CA2418938A1 (en) |
DE (1) | DE60142234D1 (en) |
ES (1) | ES2346402T3 (en) |
FR (1) | FR2811789B1 (en) |
WO (1) | WO2002007126A1 (en) |
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- 2000-07-13 FR FR0009189A patent/FR2811789B1/en not_active Expired - Fee Related
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- 2001-07-13 AU AU2001277593A patent/AU2001277593A1/en not_active Abandoned
- 2001-07-13 EP EP01955423A patent/EP1299870B1/en not_active Expired - Lifetime
- 2001-07-13 AT AT01955423T patent/ATE469412T1/en not_active IP Right Cessation
- 2001-07-13 WO PCT/FR2001/002292 patent/WO2002007126A1/en active Application Filing
- 2001-07-13 DE DE60142234T patent/DE60142234D1/en not_active Expired - Lifetime
- 2001-07-13 US US10/332,665 patent/US6865518B2/en not_active Expired - Fee Related
- 2001-07-13 ES ES01955423T patent/ES2346402T3/en not_active Expired - Lifetime
- 2001-07-13 CA CA002418938A patent/CA2418938A1/en not_active Abandoned
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FR2811789B1 (en) | 2003-08-15 |
ES2346402T3 (en) | 2010-10-15 |
ATE469412T1 (en) | 2010-06-15 |
FR2811789A1 (en) | 2002-01-18 |
AU2001277593A1 (en) | 2002-01-30 |
EP1299870B1 (en) | 2010-05-26 |
WO2002007126A1 (en) | 2002-01-24 |
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