WO2024104647A1 - Procédé d'identification d'un véhicule et système de mise en œuvre du procédé - Google Patents

Procédé d'identification d'un véhicule et système de mise en œuvre du procédé Download PDF

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Publication number
WO2024104647A1
WO2024104647A1 PCT/EP2023/077416 EP2023077416W WO2024104647A1 WO 2024104647 A1 WO2024104647 A1 WO 2024104647A1 EP 2023077416 W EP2023077416 W EP 2023077416W WO 2024104647 A1 WO2024104647 A1 WO 2024104647A1
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WIPO (PCT)
Prior art keywords
vehicle
data
data set
identifying
rfid
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Application number
PCT/EP2023/077416
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German (de)
English (en)
Inventor
Albertus Jacobus Pretorius
Jörn BERTRAM
Original Assignee
Tönnjes Isi Patent Holding 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 Tönnjes Isi Patent Holding Gmbh filed Critical Tönnjes Isi Patent Holding Gmbh
Publication of WO2024104647A1 publication Critical patent/WO2024104647A1/fr

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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
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles

Definitions

  • the invention relates to a method for identifying a vehicle according to claim 1. Furthermore, the invention relates to a system for carrying out the method for identifying a vehicle according to claim 20.
  • chassis number is another vehicle identification number that is usually attached to the vehicle frame by the vehicle manufacturer in the form of a barcode label.
  • the engine is also clearly identified by an engine number.
  • Another disadvantage of these numbers is that they are notoriously easy to forge. Such forgeries are used in vehicle crime, for example to legitimize stolen vehicles.
  • Tracking methods with a time stamp can be used to determine the location of a vehicle or license plate at a specific point in time. If this vehicle or license plate is seen in two different locations at a similar time, it can be assumed that only one of the vehicles has been legally registered by the relevant authority. However, this method cannot determine which of the two vehicles is registered and which is not. Stopping and checking a vehicle can only be carried out on a random basis. Apart from the fact that such a check is very labor-intensive, only a small fraction of the vehicles on the road can be checked.
  • Another problem with identifying and checking the authenticity of vehicle license plates is that it is difficult to intervene later when a vehicle with a suspicious identity is automatically detected. For example, if a speeding vehicle is recorded by a radar camera and the responsible police station identifies a fake license plate, the vehicle has already disappeared by that point and can no longer be identified.
  • contactless transponders particularly RFID or NFC transponders or tags
  • RFID passive UHF radio identification
  • RAIN RFID also known as RAIN RFID or simply RAIN
  • RAIN RFID is increasingly becoming the standard for marking any part during production. Such marking can, for example, be used to track and control the life cycle of the part by regularly reading it. This includes manufacturing, testing, distribution, installation, monitoring during use and end-of-life management.
  • Vehicle manufacturers have now also discovered RAIN RFID as a method to improve transparency in connection with new and retrofitted parts.
  • RAIN RFID transponders are also increasingly used to identify vehicles for regulatory compliance and road pricing purposes, i.e. collecting fees such as parking fees and tolls.
  • Vehicle identification is used for road compliance and enforcement services, as well as payment and other vehicle management and vehicle retention services such as fleet management.
  • Fleet managers (car rentals, taxis, construction companies, etc.) can link license plates of specific parts or vehicle parts in their fleet management systems to a vehicle identifier, usually attached by the fleet owner, to manage the parts of a vehicle.
  • These groups of tags are usually read during maintenance and when passing through gates under the control and documentation of the fleet owner.
  • the number of license plates or tags/transponders that can be recognized by a reader on a vehicle is constantly increasing, as more and more parts are being tagged. In Europe, vehicles usually have more than 20 such part markings.
  • Devices for reading license plates or transponders or tags can be, for example, road readers that are permanently installed above or next to or integrated into the roadway. These devices can also be a transportable or portable reader that is used in a roadblock during stop-and-go checks and when controlling flowing traffic.
  • RAIN RFID is a radar technology where the reader sends a modulated signal to the license plate or transponder or tag. This signal is used to power the transponder and send commands to the transponder.
  • the transponder reflects the reader's signal.
  • the reader stops modulating the signal i.e. only delivers an unmodulated signal, the so-called constant wave or CW signal
  • the transponder responds with a modulation of the signal reflection. This means that the communication between the reader and the transponder takes place within line of sight and is limited in range and scope.
  • the reading area is the size of a typical limousine with a length of 6 m and a height and width of 4 m.
  • Known RAIN RFID transponders have three storage locations, namely 1.) Inventory data, which is read when the tag is identified.
  • the inventory data is usually an identification number of the tagged item, e.g. the part number assigned by the parts manufacturer. 2.)
  • the transponder identification which is a guaranteed unique number. This data must be additionally read after the transponder is inventoried. Reading this data is not common practice.
  • 3.) User data which are additional attributes of the tagged object. Such user data is also uncommon. Regulatory vehicle identification tags usually insert authentication data into this storage location. This data must be additionally read after the transponder is inventoried. This reading is not common for non-regulatory vehicle identification tags.
  • the object of the present invention is to provide a method for identifying a vehicle and a system for carrying out the method, with which the identity of the vehicle can be verified in a fast and reliable manner.
  • a method for solving this problem has the measures of claim 1. It is provided that a plurality of RFID transponders, at least one RFID transponder on which data about the vehicle to be identified and/or the vehicle owner is stored and/or an RFID transponder that identifies a component of the same vehicle and at least one further RFID transponder that is attached to the same vehicle, are read by at least one RFID reader.
  • the data read in this way from all RFID transponders of the same vehicle are stored in a central or decentralized data storage device, together with the location and time at which the data was read.
  • This set of read data is referred to below as a data set.
  • the read data set is analyzed by an analysis device.
  • the one read data set is evaluated by the analysis device with a quality factor.
  • This quality factor is assigned to this Data set is assigned and saved together with the data set on the data storage device. If at a later point in time further RFID transponders of a vehicle are read out and corresponding data sets are created, these data sets are compared with the previously recorded data sets and their quality factors. Based on this comparison of the data sets and/or the quality factors, the analysis device generates a plausibility factor that indicates the plausibility or probability with which two data sets recorded at different times belong to the same vehicle. A high plausibility value represents a high probability that the vehicles recorded at different times are the same vehicle and that falsification of the identity can be almost ruled out. The quality factor plays a decisive role in evaluating or determining plausibility.
  • the quality factor can also be increased or improved by repeatedly detecting or re-identifying and reading the transponders of the same vehicle at different times and locations.
  • the quality factor assigned to a specific vehicle can also decrease or worsen if individual transponders are missing when the transponders of this vehicle are read again. If the reader detects missing or new transponders, this is evaluated by the analysis device.
  • the analysis unit detects two data sets that were read from the transponders of a vehicle at different times and locations, and the data sets at least largely match or have essential information, the plausibility factor is determined taking the quality factors into account.
  • the invention thus utilizes the increasing number of RAIN transponders on a vehicle to automatically create a profile of the identity of the vehicle without violating the privacy of the persons using the vehicle, with the aim of detecting potential vehicle identity fraud, i.e. vehicles with a suspicious identity.
  • the invention also provides that when reading the plurality of RFID transponders, in particular the above-mentioned transponders, of a vehicle at a later point in time, the quality factor of a data set belonging to the same vehicle is updated. It should be expressly pointed out that an update does not necessarily take place when the vehicle is recorded again. If, for example, additional or new properties of the vehicle or additional RFID transponders are recorded when the vehicle is recorded again, which were not previously stored in the saved data set, this new data can be added to the existing data set. By supplementing the data set with further information about the vehicle, the quality factor of the data set can be improved.
  • the invention can also provide that at least one camera determines further visible data of the vehicle (optical information), namely the color, the brand, the type, the vehicle components, the color of the trim, the shape and color of the dashboard, the tires and/or the shape of the vehicle, and that this data is also stored in the data set and analyzed.
  • at least one further sensor in particular a microphone or a sensor for detecting vibrations, determines noises (acoustic information) of the tires, the shock absorbers, the exhaust and/or the drive chain or the like, and that this data is also stored in the data set and analyzed.
  • the quality factor of the data set can be further improved. It is particularly advantageous that some of this information, such as noise, is difficult to falsify.
  • a particular embodiment of the invention provides that data sets that do not reach a specified value, in particular a threshold value, for a quality factor because too few transponders were recorded or other data or Information is missing and cannot be used for further identification of the vehicle, in particular for generating a plausibility factor. If, for example, only one RFID transponder is read or only one piece of optical information is recognized, and the quality factor is therefore very low, the comparison between two vehicles recorded at different times can only be given little significance. To compare data sets from vehicles recorded at different times, a minimum amount of data or a minimum quality factor is therefore required.
  • This threshold value can be freely selected by the user in the analysis device. However, a high quality factor does not necessarily mean a large data set. If, for example, a vehicle with two, three, four or more transponders is regularly recorded and all transponders are always read and the same number of transponders are recognized each time the data is recorded, this data set can have a high quality value.
  • the invention also provides for the determination of the calculated plausibility factor from which two consecutively recorded data sets can be assigned to the same vehicle. By determining such a value, the quality of the information as to whether it is really the same vehicle can be changed.
  • a high plausibility factor means a high probability of authentication or identification of the vehicle.
  • a lower plausibility factor means a lower probability that the recognized similarity of the data sets actually belongs to the same vehicle.
  • a particularly preferred embodiment of the invention provides that the analysis unit searches the data sets for known RFID transponders that were already recorded at an earlier point in time and are assigned to a vehicle, and if an RFID transponder that has already been recorded is missing from the data set, the analysis unit determines which part was identified by this RFID transponder. If an RFID transponder is recorded on a vehicle for the first time, i.e. the data for this transponder was not stored in the data set, the analysis unit determines what is identified by this RFID transponder.
  • the analysis unit decides whether this missing RFID transponder can be dispensed with in order to assign the data set to a specific vehicle. For example, the missing RFID transponder can be added to the data set using pattern recognition, preferably artificial intelligence. The lack of the transponder can thus be compensated for if necessary, which means that the recorded data set can still be assigned to a vehicle and compared with previously recorded data sets. If an RFID transponder is recorded for the first time, the pattern recognition, which can also be artificial intelligence, can also determine whether this RFID transponder matches the already known data set and, if necessary, this transponder can be added to the data set.
  • pattern recognition which can also be artificial intelligence
  • this data is not added to the data set.
  • the newly recorded RFID transponder is a license plate for a new navigation system, it can be added to the already known data set.
  • the quality factor of this data set also changes. With this change or modification of the quality factor, it can be either reduced, i.e. decreased, or increased, i.e. improved.
  • the quality factor in the best case, increases with increasing detection of the vehicle. The more often a vehicle or data set is recorded, the more meaningful the comparison between two data sets recorded at different times is for identity.
  • the analysis unit additionally searches the data sets for known optical and/or acoustic information that was already recorded at an earlier point in time and is associated with a vehicle, and if optical and/or acoustic information that has already been recorded is missing in the data set, the analysis unit determines what was identified by this information. If optical and/or acoustic information is recorded for the first time, the analysis unit can also determine what is identified by this information.
  • the analysis unit decides whether the information can be dispensed with in order to assign the data set to a specific vehicle. In addition, it is checked whether or not the missing information can be added to the data set using pattern recognition, preferably by artificial intelligence. If newly recorded acoustic or optical information is used, pattern recognition, preferably by artificial intelligence, determines whether this information fits the data set and, if necessary, adds it to the data set. Based on this information, the analysis unit decides whether the newly recorded information indicates an unauthorized exchange of vehicle components. The recording and recognition of optical and acoustic information can thus be used in addition to the recording of RFID transponders to check the identity of the vehicle.
  • the quality factor of the data set can be modified, preferably reduced or increased.
  • the invention can provide that the location and/or time of the data set being recorded is also taken into account when determining the plausibility factor.
  • This location and/or time stamp provides the analysis device with additional valuable data to determine the plausibility of whether two to determine whether data sets recorded at different times belong to the same vehicle.
  • RFID transponders of the same type are read at the same time and at the same location, it is determined whether these data sets can be assigned to different vehicles. Particularly on busy roads, it cannot be ruled out that the reader simultaneously recognizes several RFID transponders that belong to different vehicles. By using pattern recognition or artificial intelligence, however, the different transponders can be assigned to different vehicles.
  • an RFID transponder if it is not complete or cannot be fully read out, it can be completed by a pattern recognition system, preferably an artificial intelligence, based on the data records stored on the data storage device.
  • the RFID transponders and/or the properties of a vehicle are recorded by a large number of RFID readers and/or other data recording instruments, with all RFID readers and/or other data recording instruments being electronically networked with one another. It is conceivable that the readers are arranged at different positions next to, above and in the road, preferably to cover the entire road. It is also conceivable that several readers are arranged at a certain distance from one another in order to directly verify recorded data. By networking all readers and the analysis unit(s) and central or decentralized data storage, the recorded data can be analyzed regardless of location and at least almost simultaneously and, if necessary, measures can be initiated to counter irregularities.
  • a system for solving the above-mentioned problem has the features of claim 20. Accordingly, it is intended to create a system for carrying out a method for identifying a vehicle according to claim 1.
  • This system has at least one analysis unit, at least one RFID reader for reading RFID transponders and/or at least one camera for capturing optical information of the vehicle.
  • the system can have at least one microphone with which acoustic information such as vibrations of the tires can be captured.
  • the block diagram shown in the figure shows the method according to the invention in a highly schematic manner. Accordingly, a reader or another sensor unit first reads a large number of RFID transponders or records information from a vehicle (data acquisition 12). This data acquisition 12 takes place at a location at a specific time. Both the location and the time are temporarily stored together with the data from the RFID transponders and the vehicle as a data record in a data storage device 11.
  • This data storage device 11, which serves as documentation, can be positioned and managed centrally or decentrally.
  • An analysis device 10 has access to this data storage 12. This analysis device 10 can also be arranged centrally or decentrally. The analysis device 10 initially records and manages all data sets from various vehicles. Through the continuous further data recording 13 from a large number of vehicles with a large number of RFID transponders, a large database of data sets for various vehicles can be created over time.
  • each data set contains the data of a large number of RFID transponders that are assigned to a Vehicle.
  • At least one data set contains the data of an RFID transponder on which data about the vehicle and/or the vehicle owner is stored and/or data of an RFID transponder that identifies a component of the same vehicle and at least one other RFID transponder that is attached to the same vehicle.
  • further information such as optical or acoustic information, about the vehicle is stored in the data set.
  • Data sets with a large amount of such data and information tend to have a higher quality factor than data sets with only a small amount of data from a few RFID transponders.
  • the quality factor also increases if a vehicle is recorded more often and the data and information about this vehicle are read out and confirmed more often. If there is also a lot of data and information about a vehicle and this is regularly confirmed by re-recording the vehicle, this data set has a high quality factor.
  • Such a data set is particularly suitable for identifying or authenticating the vehicle or license plate.
  • the analysis device 10 can use pattern recognition, which can be artificial intelligence, for example, to check whether the unrecognized RFID transponder can be dispensed with in order to create a data set.
  • the quality factor of a data set is therefore subject to constant modification or improvement.
  • the analysis device 10 checks the nature of the differences in the data sets. If, for example, the data of all RFID transponders and other information match, but not the data of the RFID transponder assigned to the license plate, it can be assumed that the vehicle has a fake license plate or number plate. This can reveal potential forgeries of license plates 17. In the further process, this data is compared with a list of already known and suspicious vehicles 16 stored in the analysis device 10 or the data storage device 11. If this results in changes with regard to a potential forgery or it turns out that it is not a If the vehicle is found to be counterfeit, the list of suspected vehicles is updated 18. However, if the analysis shows that the vehicle is counterfeit, the relevant authority is immediately alerted so that appropriate measures can be taken.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)

Abstract

La présente invention concerne un procédé d'identification d'un véhicule et un système pour la mise en œuvre du procédé, qui peut être utilisé pour vérifier l'identité du véhicule rapidement et de manière fiable. Ceci est obtenu grâce à une pluralité de transpondeurs RFID, mais au moins un transpondeur RFID sur lequel des données concernant le véhicule à identifier et/ou le détenteur du véhicule sont stockées, et/ou un transpondeur RFID qui identifie une partie du même véhicule et au moins un autre transpondeur RFID monté sur le même véhicule étant lu par au moins un lecteur RFID. Les données ainsi lues à partir de tous les transpondeurs RFID du même véhicule sont stockées dans une mémoire de données centrale ou locale (11), en particulier conjointement avec l'emplacement et le moment où les données ont été lues. L'ensemble de données lu est ensuite analysé par un dispositif d'analyse (10). Le premier ensemble de données qui a été lu est évalué avec un facteur Q par le dispositif d'analyse (10).
PCT/EP2023/077416 2022-11-17 2023-10-04 Procédé d'identification d'un véhicule et système de mise en œuvre du procédé WO2024104647A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102022130403.5A DE102022130403A1 (de) 2022-11-17 2022-11-17 Verfahren zur Identifizeriung eines Fahrzeugs und System zur Durchführung des Verfahrens
DE102022130403.5 2022-11-17

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WO2024104647A1 true WO2024104647A1 (fr) 2024-05-23

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DE (1) DE102022130403A1 (fr)
TW (1) TW202427398A (fr)
WO (1) WO2024104647A1 (fr)

Citations (3)

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WO2015157814A1 (fr) * 2014-04-14 2015-10-22 Licensys Australasia Pty Ltd Système d'identification et/ou de surveillance de véhicule
CN113744535A (zh) * 2021-08-24 2021-12-03 武汉光电工业技术研究院有限公司 针对rfid标签的动态坐标同步方法、装置及视频巡检车
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CN113744535A (zh) * 2021-08-24 2021-12-03 武汉光电工业技术研究院有限公司 针对rfid标签的动态坐标同步方法、装置及视频巡检车

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DE102022130403A1 (de) 2024-05-23

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