WO2004101300A1 - Procede pour detecter et surveiller le mouvement de vehicules - Google Patents

Procede pour detecter et surveiller le mouvement de vehicules Download PDF

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Publication number
WO2004101300A1
WO2004101300A1 PCT/DE2004/000976 DE2004000976W WO2004101300A1 WO 2004101300 A1 WO2004101300 A1 WO 2004101300A1 DE 2004000976 W DE2004000976 W DE 2004000976W WO 2004101300 A1 WO2004101300 A1 WO 2004101300A1
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WO
WIPO (PCT)
Prior art keywords
measured values
estimation
chassis
sensors
sensor
Prior art date
Application number
PCT/DE2004/000976
Other languages
German (de)
English (en)
Inventor
Jakob Schillinger
Thorsten Gollewski
Dietmar Kohn
Original Assignee
Conti Temic Microelectronic 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 Conti Temic Microelectronic Gmbh filed Critical Conti Temic Microelectronic Gmbh
Publication of WO2004101300A1 publication Critical patent/WO2004101300A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
    • B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
    • B60G17/018—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the use of a specific signal treatment or control method
    • B60G17/0185—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the use of a specific signal treatment or control method for failure detection
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2600/00—Indexing codes relating to particular elements, systems or processes used on suspension systems or suspension control systems
    • B60G2600/08—Failure or malfunction detecting means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2600/00—Indexing codes relating to particular elements, systems or processes used on suspension systems or suspension control systems
    • B60G2600/18—Automatic control means
    • B60G2600/187—Digital Controller Details and Signal Treatment
    • B60G2600/1875—Other parameter or state estimation methods not involving the mathematical modelling of the vehicle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2600/00—Indexing codes relating to particular elements, systems or processes used on suspension systems or suspension control systems
    • B60G2600/18—Automatic control means
    • B60G2600/187—Digital Controller Details and Signal Treatment
    • B60G2600/1878—Neural Networks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2600/00—Indexing codes relating to particular elements, systems or processes used on suspension systems or suspension control systems
    • B60G2600/18—Automatic control means
    • B60G2600/187—Digital Controller Details and Signal Treatment
    • B60G2600/1879—Fuzzy Logic Control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2800/00—Indexing codes relating to the type of movement or to the condition of the vehicle and to the end result to be achieved by the control action
    • B60G2800/80—Detection or control after a system or component failure
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2800/00—Indexing codes relating to the type of movement or to the condition of the vehicle and to the end result to be achieved by the control action
    • B60G2800/80—Detection or control after a system or component failure
    • B60G2800/802—Diagnostics

Definitions

  • the invention relates to a method for detecting and monitoring movement in vehicles, according to the preamble of patent claim 1.
  • the chassis condition has been measured by various different sensors, which record the various parameters in a wide variety of ways.
  • One of the measured variables is determined while driving, the other measured variables are determined when the vehicle is at a standstill.
  • the tire pressure of a tire is detected by a pressure sensor that measures the tire pressure while driving.
  • the condition of the road and thus the bumps in the road can be measured using ultrasound.
  • Various methods are also known to record the weather conditions.
  • DE 4218087 A1 discloses a method for regulating the damping of the chassis of a motor vehicle and / or for diagnosing the chassis.
  • process variables that are related to a vertical movement of the vehicle are fed to a mathematical model that determines the relationship between the vertical acceleration of a wheel or a structure of the vehicle Vehicle on the one hand and other process variables and parameters on the other.
  • the parameters of the model are estimated using a parameter estimation method known per se and used to generate a manipulated variable for setting the damping and / or as diagnostic data.
  • Another disadvantage here is the complex calculation.
  • several equations are set up that describe the physical relationships between the measured values and the parameters. These equations contain several unknown parameters. Since the number of equations is not sufficient to calculate each unknown parameter exactly, a parameter estimation method is used. The elaborately estimated parameters are then used in turn to calculate the physical process variables that are required for the control or diagnosis of the chassis.
  • EP 0554131 A1 discloses methods for sensing the tire condition by means of a sensor attached to the tire.
  • the sensors record the speed of rotation of each tire.
  • Another sensor detects the speed or acceleration of the vehicle. By comparing these values with each other, conclusions can be drawn about the tire pressure.
  • the disadvantage here is that a large number of sensors are required to detect a state.
  • the object of the invention is to monitor the chassis condition, in particular the tire pressure, the wheel and tire condition, the damper condition and the damper settings, the condition of the roadway or rail, and the body movement quickly, reliably and inexpensively.
  • Another object of the invention is to optimize the setting parameters of the damping system in the field of road, rail and aircraft.
  • the object is achieved by the features in claim 1.
  • sensors for detecting accelerations, speeds, distances and / or forces are attached to the chassis and the body.
  • the sensor signals are evaluated using a parameter estimation method.
  • the parameters obtained from this are evaluated with the aid of methods for pattern recognition, the fuzzi techniques and / or neural networks.
  • the chassis state is formed from the individual, different measured values. These vectors, which not only reflect individual measured values themselves, but also the relationship of several measured values to one another, can then be compared with stored vectors or vector fields to which a vehicle state is assigned.
  • the advantage of the invention is that, with a small amount of data compared to previous methods, very reliable statements can be made about the chassis status, since the different constellations of the measured values relative to one another also have meaningfulness about the vehicle status.
  • the data processing for previous methods is also very low, since no complicated calculations are required, but only result vectors are compared with one another.
  • the evaluation takes place very quickly, so that in the event of malfunctions, the system can be intervened at an early stage to prevent major damage, for example.
  • Cost-effective multiple use of the sensors used is also an advantage, which increases safety, the early detection of dangerous conditions (eg aging or loss of pressure in the tires) and improves driving comfort. This also forms a way of recognizing the road condition as an input signal for intelligent braking algorithms.
  • the presented method is now based on a completely different approach: The sensor signals are processed without direct reference to mathematical or physical models using the speech recognition method and the comparison of the quantized states is used to determine the condition of the road surface and the overall chassis as well as individual components closed.
  • sensors are attached to suitable points of the vehicle, which directly or indirectly record the required measured variables.
  • Sensors attached in the area of the wheel hub, the subframe or the damper dome primarily detect the excitation which, generated by the road surface, acts on the body via the tires and spring / damper strut.
  • Acceleration sensors are preferably used here, but sensors for speed, structure-borne noise, pressure, force, stroke, travel or angle can also be used.
  • the number and spatial distribution depends on the requirements.
  • the minimum equipment is one sensor per vehicle, the maximum equipment is one sensor per measurement variable per wheel.
  • at least one sensor can be attached to the chassis to detect the excitation that acts on the body via the chassis.
  • a measure of the load or the adjustment of the dampers can be obtained from the movement of the chassis .
  • acoustic sensors such as a microphone or structure-borne sound sensor, vibrations of the chassis, which result from brakes, mechanical bearings, tire tread, stone chips or splash water, are recorded.
  • Critical brake conditions wear, rubbing, squeaking
  • An acoustic sensor, attached to or near the wheel arch, can use voice analysis to identify critical road surface conditions, such as roll split or water film.
  • Certain measured variables can be recorded in an amplified or weakened manner via the spatial arrangement of the sensor or sensors. These measuring principles are known and described in the specialist literature. In conventional practice, one or more sensors are assigned to each measurement task. The algorithms, based on digital filters, statistical methods and / or RMS calculations, are tailored to the measurement task.
  • the new approach assumes multiple use of the sensors using speech recognition methods. This results in a significant cost, space, u. Weight advantage. The technical effort for speech recognition remains within reasonable limits.
  • the application of the methods of speech recognition for the assessment of the chassis and the road is based on the idea that time and.
  • the sequence of quasi-steady-state driving conditions results in sections of steady-state signal signatures on the sensors, comparable to the sequence of sounds within a word, each sound corresponding to such a quasi-stationary driving state.
  • Layers 1 and 2 are sensor-specific, layer 3 represents the overall context with regard to the vehicle.
  • the sensors are preferably used optionally as sensors for the measured variables: acceleration, structure-borne noise, speed, force, pressure, stroke, path, angle, distance, pitching movement or rolling movement.
  • estimation vectors for defined class names are preferably mapped using methods of neural networks, fuzzy logic, assignment tables, linear discriminant analysis or hidden Markov models.
  • the measurement values are preferably sampled both equidistant in time and equidistant in distance.
  • Monitoring and detection using a classifier is preferably carried out on the basis of the methods of neural networks, fuzzy logic, assignment tables, linear discriminant analysis or hidden Markov models, which carries out the overall evaluation from the comparison of the class names for each measurement channel.
  • An orthogonal estimation method is preferably used for the feature generation.
  • the estimation vectors are preferably calculated using AR-Lattice (autoregressive), ARMA-Lattice (autoregressive moving average) or joint lattice / ladder methods.
  • the sensor signals, the estimation vectors, the class names and / or the overall evaluation via bus coupling are preferably passed on to other diagnostic, reporting or control systems or generated in at least one of these systems and a defined action is started there.
  • driving over the edge strip is also recognized.
  • the invention can be used for road vehicles, rail vehicles and aircraft.
  • the functionality of individual sensors can also preferably be inferred from the comparison of the estimation vectors.
  • results are preferably stored and the aging condition is evaluated from the time comparison.
  • the invention is to be illustrated using exemplary embodiments and the figures.
  • Figure 1 shows the model for the detection and monitoring of movements on vehicles.
  • it is a vehicle with four wheels, four dampers, a chassis and five sensors, which generally measure accelerations.
  • speed, distance, length, force or noise sensors can also be used.
  • Two transmission systems are shown in the upper part of the figure.
  • One transmission system is the unit of road and wheel.
  • the other transmission system is represented by dampers and chassis. Both transmission systems are interconnected.
  • Four chassis sensors - one on each subframe - are arranged between the transmission systems roadway / wheel and damper / chassis.
  • the fifth sensor, the chassis sensor is located on the chassis.
  • the transmission systems change a signal WR present at the input of the transmission systems to change the white noise.
  • measurement signals are recorded at suitable points, which were selected on the basis of empirical values.
  • the second stage of this model illustration shows five inputs for the sensor signals.
  • the measurement results of the four chassis sensors and the chassis sensor are fed into the pre-filter.
  • the values together with the white noise are fed to one or more estimation methods.
  • Measurement series are a number of measurement results that are recorded during a time period.
  • the components of vectors are calculated from the measurement series of a sensor using an orthogonal estimation method.
  • the AR autoregressive, ARMA (autoregressive moving average) or joint ladder method
  • Two vectors K c and K d are calculated from this number of measured values, these vectors each having ⁇ elements with:
  • the number ⁇ of elements depends on the change in the measured values over time.
  • two vectors are then formed from the measured values of the chassis sensor using the AR estimation method, as described above, and from the measured value series:
  • these vectors are compared with one another and on the other hand with stored values, element by element, and then they are classified.
  • For the classification it is necessary that values, ranges of values, and possibly relations of the elements of different vectors with one another, are already stored in a memory for the most varied driving situations and conditions, which are reflected in vectors and there in particular in the elements.
  • the results are fed to a map.
  • the map in turn influences the prefilter.
  • the estimation vectors are converted into physical quantities. Functions result from the conversion.
  • a function H (z) B (z) / A (z) results from the joint ladder estimation of a chassis sensor.
  • the physical values then result, which are then fed to control loops, diagnostic systems or driver information systems in the third stage of the model in order to influence an undesired driving state, to recognize errors at an early stage or at least to report errors.
  • Figure 2 describes the course of the estimation.
  • the white noise is fed into the function 1 / A (z).
  • the chassis sensor detects what this function or the transmission system does with the white noise.
  • this series of measured values reaches a pre-filter, and on the other hand it becomes another Transmission system or function B (z) fed.
  • the chassis sensor detects what the second transmission system does with the white noise.
  • These series of measured values go into two different pre-filters, if necessary.
  • three calculation strings are formed, which result from two different measurement series. Two of the calculation strings are linked to each other and influence each other by using the value or vector from one strand as the basis for calculating the value or vector in the other strand.
  • a first value arrives at point T and then in a first circle. Between T and the first circle, this branches to a second circle. The first value also goes directly to the second circle and branches back to the first circle
  • a diagnostic table is shown in FIG. If you want to detect a fault on the wheel at the front right on the chassis sensor F1, you have to, for example, of the corresponding vector K F1 with the corresponding elements k 2 k f F lk, which were recorded by the other chassis sensors F2, F3, F4. Is now it can be concluded from this that there is a different condition on the wheel, for example insufficient air pressure.
  • Fig. 4 outlines a modified evaluation.
  • 5 illustrates the two-stage basic principle of parameter estimation and pattern recognition, as it is ultimately used in speech processing, here applied to the analysis of the vehicle and road condition.
  • Layers 1 and 2 are sensor-specific, layer 3 represents the overall context with regard to the vehicle.
  • Layer 1 is used to generate features.
  • the measurement signals are sampled at time and / or path equidistant and processed separately.
  • the known methods are optionally used on these samples without any claim to completeness: Linear Prediction Analysis (LPC), Cepstrum Analysis, Short Time Fourier Analysis, Filter Bank Analysis, Sinusoidal Analysis, Frequency-Domain Pitch Estimation, combinations thereof or further developments. Methods that lead to orthogonal estimation vectors are advantageous.
  • the LPC method estimates the coefficients of the equivalent transfer function.
  • the order of the transfer function is determined by the underlying physical model. Ladder / lattice processes are preferably used here. There are different approaches for modeling the transfer function using LPC:
  • the AR (autoregressive) lattice method is used when a pulse train or white noise has to be used as the input signal of the transmission system to be modeled. Because of the generality of the approach, this method can be applied to any sensor output.
  • the estimation vector describes the coefficients of the approximated AR model.
  • ARMA autoregressive moving average
  • Lattice methods require two measurement signals that can be interpreted as the input and output of a transfer function.
  • the estimate vector describes the coefficients of the ARMA transfer function between these two measurement channels.
  • Joint ladder / lattice methods can be used to advantage if the signal on channel 1 using AR lattice estimation and the Transmission path between channel 1 and channel 2 can be simulated using an MA model.
  • the signal from the chassis sensor can be connected to channel 1 and the signal from the chassis sensor to channel 2.
  • the AR estimate vector describes the entire transmission path between road and tire
  • the MA estimate vector describes the spring / damper transmission path.
  • Filter Bank Analysis determines the RMS values of the output signals of a series of bandpass filters with specific passband ranges that are adapted to the application. Cepstrum analysis and the different Fourier analysis methods determine the weighted spectral components in the recorded sensor signal.
  • sample values as well as the estimation vectors can be forwarded to higher-level control loops if required.
  • the infinite number of solutions are mapped to a finite number of class names in the second layer using the classification methods known from speech recognition.
  • speech recognition this corresponds to the assignment to sounds.
  • classification methods are not exhaustive: Neural networks, fuzzy logic, linear discriminant analysis, assignment tables or hidden Markov models.
  • the number of class names ("sounds") is determined by the desired classification depth. This in turn is determined by the downstream diagnosis and control systems. This means that both the estimation vectors and the quantized class names, to which controller parameters are assigned in a table, are available
  • the classification takes place as part of a learning process, whereby both simulation methods and driver testing are used.
  • these class names of the individual sensors are transferred to the third layer. This takes place in this layer mutual comparison of class names.
  • classification methods of speech recognition are used (without claiming to be complete): Neural networks, fuzzy logic, linear discriminant analysis, assignment tables or hidden Markov models.
  • class names are derived from the path-equidistant scanning is helpful in distinguishing between the condition of the road surface and the tire For which tire properties are independent of the wheel speed, this makes it possible, for example, to clearly distinguish between tire imbalance and a wavy road surface, in which the class names change depending on the vehicle speed, with the former there being essentially no influence
  • Another criterion relates to the speed at which the class names change: sizes dependent on the road surface or tire pressure can change very briefly. Wear-dependent variables, such as tire tread depth, or damping coefficient usually change very slowly. Trend tracking of class names or estimation vectors is required here.
  • the vehicle's load can be deduced from the vehicle's own movement, detected by a suitable chassis sensor.
  • a suitable chassis sensor In order to avoid evaluation errors, it is advantageous to take into account the excitation frequency of the undercarriage.
  • Acoustic sensors attached to the chassis or chassis, are particularly suitable for signal analysis using speech recognition. With this, the profile properties of the tires, which influence the braking process, can be detected, bearing damage due to the characteristic spectral components can be detected and damage to the brake can be recognized.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Vehicle Body Suspensions (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

L'invention concerne un procédé pour détecter et surveiller le mouvement d'un véhicule. Selon ladite invention, plusieurs capteurs destinés à enregistrer des valeurs de mesure d'accélérations, de vitesses, de distances, de forces ou de bruits sont installés sur le cadre de châssis ainsi que la carrosserie du véhicule et les valeurs de mesure enregistrées par les capteurs sont traitées par un procédé de reconnaissance de formes, en particulier un procédé connu utilisé dans le traitement de la parole.
PCT/DE2004/000976 2003-05-08 2004-05-10 Procede pour detecter et surveiller le mouvement de vehicules WO2004101300A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE10320809.7 2003-05-08
DE2003120809 DE10320809A1 (de) 2003-05-08 2003-05-08 Verfahren zur Erkennung und Überwachung der Bewegung bei Fahrzeugen

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WO2004101300A1 true WO2004101300A1 (fr) 2004-11-25

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CN111767933A (zh) * 2019-05-17 2020-10-13 北京京东尚科信息技术有限公司 一种识别车辆行驶状态的方法和装置
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