EP1480182A2 - Compteur d'essieux sans contact pour traffic routier - Google Patents

Compteur d'essieux sans contact pour traffic routier Download PDF

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Publication number
EP1480182A2
EP1480182A2 EP04450111A EP04450111A EP1480182A2 EP 1480182 A2 EP1480182 A2 EP 1480182A2 EP 04450111 A EP04450111 A EP 04450111A EP 04450111 A EP04450111 A EP 04450111A EP 1480182 A2 EP1480182 A2 EP 1480182A2
Authority
EP
European Patent Office
Prior art keywords
counting system
axle counting
unit
signal
classifier
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
Application number
EP04450111A
Other languages
German (de)
English (en)
Other versions
EP1480182B1 (fr
EP1480182A3 (fr
Inventor
Franz Dipl.-Ing. Graf
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.)
Joanneum Research Forschungs GmbH
Original Assignee
Joanneum Research Forschungs 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 Joanneum Research Forschungs GmbH filed Critical Joanneum Research Forschungs GmbH
Publication of EP1480182A2 publication Critical patent/EP1480182A2/fr
Publication of EP1480182A3 publication Critical patent/EP1480182A3/fr
Application granted granted Critical
Publication of EP1480182B1 publication Critical patent/EP1480182B1/fr
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

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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
    • 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
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors

Definitions

  • the invention relates to a contactless axle counting system for road traffic.
  • Non-contact axle counting systems are known in which induction loops are used in the Roadway are integrated. Such systems have the disadvantage that they are in the road must be installed. The road would also have to be dug up in the event of maintenance, which is not associated only with high costs, but also with long-lasting traffic obstructions is.
  • the object of the invention is therefore to provide a non-contact axle counting system Avoids the above disadvantages and a with little installation and maintenance reliable axle counting also allows for multi-lane carriageways.
  • At least one measuring device for Detection of a moving vehicle in a measuring range e.g. at or near the contact point from tire to road surface, generated changes in the Environmental parameters such as sound, temperature or vibration is provided.
  • Such Measuring device is suitable due to its focus on a suitable measuring range, to detect vehicles without contact even in multi-lane sections of the road.
  • the at least one measuring device is above the Roadway and above the height of the vehicle is fixed. That way you can Measuring equipment conveniently and inexpensively on existing superstructures the roadway, such as bridges or signage, assembled and serviced become, with an assignment of the measuring devices to lanes particularly simple can be realized.
  • the at least one the at least one measuring device is designed as a laser vibrometer.
  • Laservibrometer allow high-precision non-contact detection of road vibrations as they occur the weight and / or the natural vibrations of moving vehicles via the tires on the Lane are caused.
  • the at least one the at least one measuring device as a directional microphone, in particular as Row microphone system is formed.
  • a directional microphone in particular as Row microphone system
  • Tire rolling noises particularly easy to detect and localize.
  • an analysis unit for Analysis of measurement signals is provided. So the interpretation of the measurement signals be facilitated and improved.
  • the analysis unit has a Signal amplification device for amplifying the measurement signals, in particular by one Factor 100 to 10,000. This means that even very weak signals can be evaluated be made accessible.
  • the analysis unit has an A / D converter with a Sampling rate of in particular 1 kHz to 50 kHz, or even more specifically between 6 kHz and 16 kHz. Measuring signals digitized in this way are for subsequent data analysis particularly suitable.
  • the A / D converter has a resolution of 8 to 24 bits, preferably from 12 to 20 bits. Such a resolution is for subsequent ones Sufficient data analysis and at the same time leads to still manageable amounts of data.
  • the analysis unit comprises a computing unit which is arranged downstream of the A / D converter. So they can digitized measurement signals prepared for a classification of the vehicles to be recorded become.
  • the computing unit Memory and / or a user interface is connected.
  • the memory enables the Storage of parameters influencing the measurement while using the user interface corresponding measurement or environmental parameters can be changed.
  • the computing unit has a temporal Segmentation device for dividing discrete time signals into time signal blocks, in particular from a block length of 10 ms to 1,500 ms.
  • a temporal Segmentation device enables a higher resolution when analyzing the measurement signals.
  • the Computing unit a spectral analyzer for calculating the frequency spectrum of the measurement signals and / or an energy calculation unit for calculating the signal energy, especially the short-term signal energy. From the data calculated in this way Data patterns are generated which are particularly good for a subsequent classification are suitable.
  • the computing unit has a Classifier includes the spectral analyzer and / or the energy calculation unit is subordinate, and which from the corresponding calculation results of Spectral analyzer and / or the energy calculation unit resulting data pattern klassizifiert. Data patterns classified in this way enable a particularly reliable Determination of the number of axles of different vehicle types.
  • the classifier is in a learning mode, in particular using learning algorithms such as the Hebbian learning process, the Back propagation rule or the forward-backward algorithm, for generation mathematical models can be operated for certain vehicle classes.
  • learning algorithms such as the Hebbian learning process, the Back propagation rule or the forward-backward algorithm, for generation mathematical models can be operated for certain vehicle classes.
  • the classifier is in a recognition mode can be operated to assign a current measurement signal to a vehicle class. So can a safe, reliable and customized operation of the contactless Axle counting system can be achieved.
  • the analysis unit a communication unit is subordinate to the calculation result of the Classifier is passed. This can also be done at a distance, for example in a Central, the number of axles of vehicles that pass a measuring point is recorded and monitored become.
  • a vehicle 10 is mechanical via its tires 12 at contact points 11 Interaction with the road surface 20.
  • the own movement (vibration) and the relative movement of the vehicle 10 to the road 20 are now mainly in Area of contact points 11 vibrations excited.
  • Sound vibrations which are predominantly caused by the rolling of the tires 12 on the road 20, but also caused by engine and flow noises, on the other hand
  • Vibrations of the surface of the road 20 itself the amplitude of which in the Order of magnitude of up to a few millimeters.
  • the amplitude of the emitted sound waves can be described in different ways become - e.g. than the speed of movement of the air molecules (sonic speed) or as pressure, which is generally preferred for describing the amplitude.
  • the sound pressure results from the fluctuation of the air pressure above and below the atmospheric pressure.
  • Measuring devices 1, which are used to convert the sound pressure into an electrical signal all suitable sound sensors, e.g. electroacoustic transducers.
  • the measuring device 1 can also act as a directional microphone or be designed as a row microphone system, which leads to an improved spatial resolution of a measurement signal.
  • the vibrations of the surface of the traffic route can in principle with Accelerometers are measured according to the piezoelectric effect work - but not contactless. These can be achieved using optical methods Vibrations can also be measured without contact.
  • Laser vibrometers work on the principle of Doppler frequency shift. This provides a vibrating object (e.g. Surface of a roadway 20) back scattered laser light all information for the Determination of surface speed and absolute vibration amplitudes. in the In contrast to other optical methods (e.g. laser scanners), the distance is not here of an existing object of interest, but the vibration speed of its Surface. With the help of this high-precision measurement, the slightest vibration excitation the surface of the road 20 can be detected.
  • the measuring devices are preferably 1 above the carriageway 20 on superstructures such as bridges, signaling devices or Signs attached (see Fig. 2) so that the monitoring of several parallel Lane is easily possible.
  • the measuring devices 1 convert the sound or vibration signals into electrical ones Energy around.
  • the measuring devices 1 generate analog or digital measurement signals as Function of time.
  • the measurement signals generated by the measuring devices 1 are transmitted via separate signal lines or after modulation or coding via a common one Signal line forwarded to the analysis unit 2.
  • the measurement signals can also through a wireless connection (e.g. radio, infrared, ...) or via local area networks (LAN) or wireless LAN (WLAN) are transmitted to the analysis unit 2.
  • the analysis unit 2 in turn communicates via a communication unit 8 for example with a central unit (not shown) for collection, evaluation or Further processing of the results delivered by the analysis unit 2.
  • the connection can be wired or between the communication unit 8 and the central unit done wirelessly.
  • FIG. 3 shows the block diagram of the hardware structure of the analysis unit 2.
  • the Measurement signals are forwarded to the signal amplification device 3, which contains the measurement signals by a fixed factor, preferably between 100 and 10,000, or with an automatic setting.
  • the subsequent analog / digital converter 4 sets the analog signals into discrete values around.
  • the sampling rate of the A / D converter 4 can differ from system to system and is generally between 1 kHz and 50 kHz. Are particularly suitable Sampling frequencies between 6 kHz and 16 kHz.
  • the resolution of the A / D converter 4 is in the Range from 8 to 24 bits, the range from 12 to 20 bits being preferred.
  • the System has a computing unit 5, which with the A / D converter 4 and Data storage 6 is connected.
  • the computing unit 5 is used to execute the Calculation steps that are applied to the digitized measurement signals.
  • the User interface 7 and memory 6 are connected to computing unit 5. By the user interface 7 can be carried out by a user.
  • the User input can be made using any suitable device, e.g. a keyboard, one Mouse, a touchscreen, or any combination of these Devices.
  • the result of the analysis is sent via an output to the interface of the Transfer communication unit 8 and processed there.
  • FIG. 4 shows the analysis of the measurement signals in the form of a block diagram they take place in the computing unit 5.
  • the measurement signals are from the A / D converter 4 a time segmentation 51 is supplied to convert the discrete time signals into time blocks to subdivide, the block length being between 10 ms and 1,500 ms.
  • the individual blocks are also extracted from the signal with an overlap. This Overlap is used to increase the resolution and can have values between 20% and 70% accept.
  • the data become the one hand Transfer energy calculation unit 53 and the spectral analyzer 52.
  • the functionality of the analysis unit 2 is described below.
  • the variable t stands for the time, T for the length of a signal block and i stands for the number of the block within the entire acoustic signal.
  • s i (t) denotes the measurement signal in the time domain of the i th block, S i (w) the frequency spectrum of the i th block.
  • the variable w corresponds to the instantaneous frequency.
  • the analysis unit 2 consists of a spectral analyzer 52 and an energy calculation unit 53 for calculating the signal energy.
  • the spectral analyzer 52 transforms the individual signal blocks from the time domain into the frequency domain. By default, these transformations are performed using Fourier transformation.
  • the so-called Fast Fourier Transformation (FFT) is particularly well suited for this.
  • the Fast Fourier Transformation corresponds to a digital approximation of the Fourier transformation.
  • the output of the spectrum analyzer 52 corresponds to the power density spectrum of the signal at the entrance and describes the amount of energy at a certain frequency support point.
  • the number of frequency sampling points depends on the number of discrete samples, which are taken from the temporal signal for the Fourier transformation, and is in directly related to the block length T mentioned above.
  • the Fourier transform others can also be used to calculate the spectral content of a time signal Methods such as the linear prediction coding can be used, which from the literature is known.
  • Another component of the analysis unit 2 is the energy calculation unit 53.
  • the relationship for determining the signal energy is described by the following equation:
  • the course of the signal energy obtained can be significant by the choice of the window length to be influenced.
  • the use of large window lengths corresponds to low-pass filtering with a low cut-off frequency and has the consequence that short-term fluctuations of the Signals are not reflected in the signal energy.
  • Short window lengths result on the other hand, a course of the signal energy which largely depends on the temporal structure of the based on the acoustic signal.
  • the block lengths T for calculating the energy correspond to above values and can be between 10 ms and 1,500 ms.
  • classifier 54 Those obtained from the energy calculation and the spectral analyzer 52 Calculation results are then processed in classifier 54.
  • the Classifier 54 is able to measure certain data patterns by the Calculation results are formed to classify. That when passing by Vehicles 10 with different number of axles result both for the calculated Energy as well as data spectra determined for the spectrum, which the classifier 54 through the can classify learning patterns presented to him before.
  • the classifier 54 can be in two Modes are operated.
  • the first mode is also called the learning mode.
  • the lem mode is used to generate a mathematical model for each class to be recognized.
  • a learning algorithm is a sequence of mathematical calculation steps to approximate a mathematical model in iterative form. Learning algorithms such as the Hebbian learning method, the back propagation rule and the forward backward algorithm are known from the literature.
  • the second mode is referred to as the recognition mode and represents the mode that is used in normal operation of the recognition system.
  • one of the K signal classes is assigned to a signal that is currently present, and the number of axles of the vehicle 10 that is currently passing is thus determined. The result of the classification is then transferred to the communication unit 8.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
  • Length Measuring Devices By Optical Means (AREA)
  • Length Measuring Devices With Unspecified Measuring Means (AREA)
EP04450111A 2003-05-20 2004-05-19 Compteur d'essieux sans contact pour traffic routier Expired - Lifetime EP1480182B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
AT7772003 2003-05-20
AT0077703A AT412595B (de) 2003-05-20 2003-05-20 Berührungsloses achszählsystem für den strassenverkehr

Publications (3)

Publication Number Publication Date
EP1480182A2 true EP1480182A2 (fr) 2004-11-24
EP1480182A3 EP1480182A3 (fr) 2006-01-18
EP1480182B1 EP1480182B1 (fr) 2007-06-06

Family

ID=32932025

Family Applications (1)

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EP04450111A Expired - Lifetime EP1480182B1 (fr) 2003-05-20 2004-05-19 Compteur d'essieux sans contact pour traffic routier

Country Status (3)

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EP (1) EP1480182B1 (fr)
AT (2) AT412595B (fr)
DE (1) DE502004004010D1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102006054981B3 (de) * 2006-11-22 2008-06-19 Rtb Gmbh & Co. Kg Klassifikationseinrichtung für Fahrzeuge
WO2022058578A3 (fr) * 2020-09-21 2022-06-16 Deutsches Zentrum für Luft- und Raumfahrt e.V. Procédé et dispositif pour surveiller le trafic au moyen des vibrations de la chaussée, par vibrométrie laser
DE102022102006A1 (de) 2022-01-28 2023-08-03 Deutsches Zentrum für Luft- und Raumfahrt e.V. Erfassen von Verkehrsaktivitäten mit Schwingungssensoren von einem Fahrzeug aus

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102014012285A1 (de) 2014-08-22 2016-02-25 Jenoptik Robot Gmbh Verfahren und Achsenzähl-Vorrichtung zur berührungslosen Achsenzählung eines Fahrzeugs sowie Achsenzählsystem für den Straßenverkehr
CN114575206A (zh) * 2022-03-03 2022-06-03 中国测绘科学研究院 一种铁路轨道姿态测量方法、装置及作业系统

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4789941A (en) * 1986-07-18 1988-12-06 Bennett Nunberg Computerized vehicle classification system
EP0612050B1 (fr) * 1993-02-19 1997-11-05 Mitsubishi Jukogyo Kabushiki Kaisha Système de détection de véhicules
US5528234A (en) * 1994-02-01 1996-06-18 Mani; Siva A. Traffic monitoring system for determining vehicle dimensions, speed, and class
US5821879A (en) * 1996-08-05 1998-10-13 Pacific Sierra Research Corp. Vehicle axle detector for roadways
KR100459475B1 (ko) * 2002-04-04 2004-12-03 엘지산전 주식회사 차종 판단 시스템 및 그 방법

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102006054981B3 (de) * 2006-11-22 2008-06-19 Rtb Gmbh & Co. Kg Klassifikationseinrichtung für Fahrzeuge
WO2022058578A3 (fr) * 2020-09-21 2022-06-16 Deutsches Zentrum für Luft- und Raumfahrt e.V. Procédé et dispositif pour surveiller le trafic au moyen des vibrations de la chaussée, par vibrométrie laser
DE102022102006A1 (de) 2022-01-28 2023-08-03 Deutsches Zentrum für Luft- und Raumfahrt e.V. Erfassen von Verkehrsaktivitäten mit Schwingungssensoren von einem Fahrzeug aus
DE102022102006B4 (de) 2022-01-28 2023-11-02 Deutsches Zentrum für Luft- und Raumfahrt e.V. Erfassen von Verkehrsaktivitäten mit Schwingungssensoren von einem Fahrzeug aus

Also Published As

Publication number Publication date
ATE364213T1 (de) 2007-06-15
EP1480182B1 (fr) 2007-06-06
DE502004004010D1 (de) 2007-07-19
ATA7772003A (de) 2004-09-15
EP1480182A3 (fr) 2006-01-18
AT412595B (de) 2005-04-25

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