WO2018091160A1 - Système de surveillance d'une fonction d'un dispositif de détection d'un véhicule à moteur - Google Patents

Système de surveillance d'une fonction d'un dispositif de détection d'un véhicule à moteur Download PDF

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Publication number
WO2018091160A1
WO2018091160A1 PCT/EP2017/072846 EP2017072846W WO2018091160A1 WO 2018091160 A1 WO2018091160 A1 WO 2018091160A1 EP 2017072846 W EP2017072846 W EP 2017072846W WO 2018091160 A1 WO2018091160 A1 WO 2018091160A1
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WO
WIPO (PCT)
Prior art keywords
object information
time
measure
vehicle
sensor device
Prior art date
Application number
PCT/EP2017/072846
Other languages
German (de)
English (en)
Inventor
Mohamed ABBAZ
Axel Klekamp
Original Assignee
Valeo Schalter Und Sensoren 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 Valeo Schalter Und Sensoren Gmbh filed Critical Valeo Schalter Und Sensoren Gmbh
Publication of WO2018091160A1 publication Critical patent/WO2018091160A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/52004—Means for monitoring or calibrating
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/66—Sonar tracking systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/86—Combinations of sonar systems with lidar systems; Combinations of sonar systems with systems not using wave reflection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/87—Combinations of sonar systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes
    • G01S15/931—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles

Definitions

  • the invention relates to a method for monitoring a function of a
  • the invention also relates to a corresponding monitoring device for monitoring a function of a sensor device of a motor vehicle.
  • Arithmetic unit for example, an electronic control unit (ECU) of
  • a sensor device with a plurality of sensor units whose data are fused to provide, and thus meet the demands on a functional safety of the sensor device.
  • the invention relates to a method for monitoring a function of a
  • Steps include capturing object information about at least one
  • the internal object information may include or be the detected object information and / or one of the detected object information
  • Object information include or be derived object information.
  • Object information can be stored, for example, in the form of a table or a database in which one or more associated information, for example about a position and / or a distance and / or a speed, is stored for respective vehicle-external objects.
  • the deposit can take place in particular before the second time.
  • the above-mentioned distance d (t n ) may be stored as internal object information before the second time in the arithmetic unit, for example in the form of a list in which one or more off-vehicle objects are kept.
  • Another part of the method is a prediction of object information for the second time point on the basis of the internal object information for the first time, which is therefore assigned to the first time, by the arithmetic unit. This can
  • the forecasting can take place in particular before the second time.
  • additional parameters such as a speed, a speed change and other aspects of the stored object information or object information can be taken into account.
  • Another step in the process is updating the forecast
  • the non-updated predicted object information can continue to be stored in the arithmetic unit.
  • the internal object information stored as or with a distance d (t n ) can be updated as a function of the object information acquired at the second time, in this case the distance d (t n + i).
  • the distance d (t n + i) for the time t n + i can be set to 3.0 m.
  • the innovation measure can be the so-called innovation known in the context of prediction using a Kalman filter, or it can also generally be a measure of a difference, for example also in a Euclidean difference of object information, for example as a value or vector, for a specific vehicle external Object to be used.
  • the method described can also be referred to as "in-sequence” monitoring or “in-sequence monitoring”.
  • Plausibility is checked. This can be done, for example, by means of a stored in the arithmetic unit information such as a list of plausible values for the innovation measure or calculation rule as a comparison with a stored comparison value.
  • a unique identification number can be assigned to each vehicle-external object, so that the components of the object information acquired at different times and / or by different sensor units can be assigned to the respective objects external to the vehicle without errors.
  • a real world Application (of course, depending on the two times t n , t n + i, or a difference between the two times) such a large change in distance in a short time interval, for example, as a motor vehicle external object of all experience is impossible.
  • checking the plausibility of the innovation measure by the computing device would thus yield a negative result.
  • Processor overhead and can be implemented in a particularly simple manner, without additional implementation effort or with little additional implementation effort.
  • The is based on the fact that the invention essentially utilizes already existing data, in this case the detected and estimated, predicted object information or data, which is used by algorithms of the sensor device, in particular algorithms
  • Sensor data fusion of a sensor device with a plurality of sensors already be calculated independently of the described method. This prevents or minimizes the generation of an additional processor load in a control unit or an ECU or a computing unit of the sensor device
  • the method is carried out iteratively or continuously.
  • the second time of an iteration or an iteration stage of the method corresponds to the first time of a
  • the object information relates to a plurality of vehicle-external objects, that is to say refers to a plurality of vehicle-external objects or represents the properties of a plurality of vehicle-external objects.
  • the plausibility checking statistics can be improved by, for example, checking a pool of several respective innovation measures for plausibility, as explained below.
  • this is also favorable for a sensor device with a plurality of different sensor units, since these can often detect different or at least partially different objects.
  • the functional safety of the sensor device can be particularly simple and without, for example, a selection process, which includes the
  • Object information limited to a single vehicle external object performed.
  • Object information is detected via a plurality of different sensor units, which in particular have one or more different modalities, and preferably each have a different modality.
  • the sensor units which in particular have one or more different modalities, and preferably each have a different modality.
  • the sensor units which in particular have one or more different modalities, and preferably each have a different modality.
  • Sensor device in each case one or more sensor units of a lidar, for example a lidar with a laser scanner or a lidar without a laser scanner, and / or a radar and / or a camera system (with one or more
  • the proposed method is particularly advantageous here, since the conventional known approaches for monitoring the function of a sensor device in a scenario with a plurality of different sensor units are particularly expensive, that is, the above-mentioned disadvantages of a high implementation effort or additionally required sensors.
  • the respective information may be stored in the form of a vector for one or more, in particular all, vehicle-external objects that are represented in the object information.
  • the choice of a vector as a representation is particularly advantageous here, since a change or a difference between different vectors is particularly easy to handle and quantify mathematically.
  • the above information has the advantage that they characterize the vehicle-external objects particularly well and especially for this important information should be ensured in the context of functional safety in each case that they are correct, that is, for example, the updating of the object information works as intended.
  • the checking comprises a comparison of the calculated innovation measure with a limit value or a limit measure, for example also a vector or limit vector, and
  • a result of checking is negative if the calculated
  • Innovation measure is smaller than the limit.
  • the comparison can be realized here, for example, by differentiating as in the Kalman filter or by forming a Euclidean difference.
  • This has the advantage that the plausibility of the innovation measure can be checked with a simple, low-resource-binding comparison.
  • provision is made for the innovation measure to be calculated for a plurality of vehicle-external objects which relate to the object information, in particular for all vehicle-external objects which concern the object information and / or for a plurality of points in time for which the object information is updated is, in particular for all times for which the object information is updated.
  • a plurality of respective innovation dimensions l k for a plurality of respective objects are thus calculated k.
  • Sensor device can be detected particularly reliable and efficient, since in all probability a functional malfunction does not occur only once at a single time and / or in a single vehicle external object, but typically repeats systematically and / or occurs simultaneously for several vehicles external objects.
  • l k (t) By means of corresponding correlations in the respective innovation measures l k (t), a malfunction of the sensor device can thus be detected particularly well.
  • the checking delivers a negative result, if the respective innovation measure
  • Minimum proportion of the vehicle-external objects represented in the object information is greater than a predetermined limit or the predetermined limit, and / or if the innovation measure for an object repeated, for example according to a recognizable pattern or even irregularly, is greater than the predetermined limit and / or if the number of vehicle-external objects changes by more than a predetermined number (which can also be zero) for which the respective innovation measure is greater than the predetermined limit value. It has been found that a malfunction of the sensor device can be monitored particularly reliably with the conditions mentioned for the degree of innovation, since systemic errors in the sensor device are reflected very rapidly or in a very obvious manner in a change of the named criteria.
  • an additional comparison of the object information stored for the first point in time, ie the internal object information of the first time, with the updated object information (of the second time) takes place and calculating a deviation measure for the at least one vehicle external object Dependence of a result of the
  • the deviation measure serves this purpose Reviewing the forecast in a similar way as the innovation measure serves to validate the update.
  • the deviation measure will therefore generally be greater than the innovation measure for a given updated object information.
  • Deviation measure is not plausible.
  • a difference as known in the Kalman filter for the innovation or a Euclidean difference can be calculated. Since the comparison is based on object information from different points in time or with different timestamps, this can be referred to as "out-of-sequence" monitoring or “out-of-sequence monitoring” in contrast to the checking based on the innovation measure.
  • the out-of-sequence monitoring would also be feasible independently of the in-sequence monitoring, but since the deviation measure itself provides a coarser monitoring as explained, the implementation together with the in-sequence monitoring makes sense, since errors in the forecasting process and in the Update process can be observed separately from each other in their influence.
  • the checking of the plausibility of the deviation measure is a comparison of the calculated
  • Deviation measure with a further limit in particular a limit which is greater than the threshold for the innovation measure, and in particular a result of the check is negative if the calculated deviation measure is greater than the further limit, and positive if the calculated deviation measure is smaller as the further limit.
  • the deviation measure is calculated for a plurality of, in particular all, vehicle-external objects which relate to the object information, and / or for a plurality, in particular all, times for which the object information is updated.
  • a plurality of respective deviation measures A k for the respective objects k are calculated.
  • the check provides a negative result if the respective deviation measure (in particular for one or more times) for a predetermined minimum number of vehicle-external objects and / or a predetermined minimum proportion of the vehicle-external objects is greater than another predetermined further limit value or the predetermined further limit value and / or if the deviation measure for an object is repeated, for example according to a predetermined pattern or also irregularly, is greater than the predetermined further limit value and / or if the number of objects by more than one changed predetermined number for which the respective deviation measure is greater than the predetermined further limit.
  • both times, the first and the second time, are directly successive points in time. This not only has the advantage of close monitoring of the function of the
  • the invention also relates to a sensor device of a motor vehicle.
  • the sensor device has a sensor unit for detecting object information about at least one vehicle-external object at a first time and at a further second time following the first time. It is the
  • Sensor device designed to store the object information detected at the first time as an internal object information in a computing unit of
  • the arithmetic unit is designed to predict object information for the second time based on the internal object information, as well as to update the predicted object information to the new internal
  • the computing device is designed to check a plausibility of the innovation measure and to issue an error message if the check provides a negative result.
  • the invention also relates to a sensor device which is suitable for carrying out a
  • inventive method is formed.
  • the invention also relates to a motor vehicle with such a sensor device.
  • Fig. 1 is a schematic representation of a motor vehicle with a
  • Fig. 2 is a schematic representation of a flowchart of an exemplary
  • Embodiment of a method for monitoring a function of a sensor device of a motor vehicle Embodiment of a method for monitoring a function of a sensor device of a motor vehicle.
  • the motor vehicle 1 comprises a sensor device 2 which has at least one, in the present case two sensor units 3, 4 of different modality.
  • the first sensor unit 3 is designed as an ultrasonic sensor unit for detecting a distance d to an object external to the vehicle k
  • the second sensor unit 4 as a camera for detecting the distance d and a position of a vehicle-external object k.
  • the sensor device 2 also has a computing unit 5, in which an internal object information O k (FIG. 2), which corresponds to a detected object information o k (FIG. 2), can be stored.
  • the arithmetic unit 5 for predicting an object information 0 ' k (FIG. 2) is designed on the basis of the internal object information O k and for
  • the sensor device can thus be suitable for one of the methods as explained with reference to FIG. 2.
  • FIG. 2 shows a schematic flowchart of the example method for monitoring a function of a sensor device.
  • First takes place here detecting 10a - 10g object information o k (t n t n + i) on at least one vehicle external object k.
  • Detecting 10a-10g takes place at least at a first time t n and at a second time t n + i following the first time t n .
  • a first detection 10a can take place by a first sensor unit 4 (FIG. 1), for example a camera, and, for example, a further, in this case sixfold, detection 10b-10g can be performed by a second sensor unit 3 (FIG can be designed as an ultrasonic sensor unit.
  • a multimodal data fusion can be realized in the further process.
  • This updated object information 0 (t n + i) can be provided by the computing unit 5 to further units, as symbolized by the arrow 14.
  • the updated internal object information 0 (t n + i) can also be used as new output object information for the next iteration, as symbolized by the arrow 15.
  • both detected object information o (t n ), o (t n + i) are always available from the time t n and t n + i, as symbolized by the arrow 1 6.
  • the update 13 may be calculated based on the Kalman algorithm.
  • the object information 0 '(t n + i) predicted for the second time instant t n + i is then compared with the updated object information 0 (t n + i) and an innovation measure l k [0' k (t n + i) is calculated. ;
  • predicted object information 0 '(t n + i) and updated object information 0 (t n + i) are compared with a predetermined limit value and an error message is output if the deviation is greater than the predetermined limit value.
  • other criteria can also be checked or specified.
  • the additional expense which must be operated for monitoring the sensor device 2 only a simple comparison 1 7 with a minimum of additional computational effort. Since the comparison 17 here refers to object information O, O 'of the same time t n + i, this monitoring can be referred to as "in-sequence" monitoring.
  • the innovation measure l k [0 ' k (t n + i); O k (t n + i)] is available for every detected object k for each sub-step t.
  • O k (t n + i)] of an object k at time t n + i can now be checked, for example, whether it is above a limit value Di.
  • a counter N may count the number of objects k at a time t n + i for which the innovation measure l k [0 ' k (t n + i); O k (t n + i)] is greater Di.
  • the innovation measure l k [0 ' k (t n + i); O k (t n + i)] is greater than Di.
  • Object information o k (t n t n + i) is carried out correctly or not. This can be recorded and quantified both for individual time steps or times and over time. Thus, a gradual deterioration of a processing of the object information o k (t n ; t n + i), for example, a sensor fusion over time can be monitored and detected.
  • Deviation measure A k [O k (t n ); O k (t n + i)] and innovation measure l k [0 ' k (t n + i); O k (t n + i)] can be used for the
  • Error handling or error analysis can be evaluated in combination.
  • Association information of updating 13 done which is already known for example from a Kalman filter.
  • other combinations of available data for the comparison for calculating the innovation measure lk [ 0'k (tn + i); O k (t n + i)] and the deviation measure A k [O k (t n ); O k (t n + i)] conceivable. These then depend on specific conditions of the respective environment and on the available computing capacity of the computing device 5.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé permettant d'assurer la surveillance d'une fonction d'un dispositif de détection (2) d'un véhicule à moteur (2), ledit procédé comprenant les étapes suivantes : (a) détecter (10a-10g) une information d'objet (Ok(tn;tn+1)) concernant au moins un objet (k) extérieur au véhicule à un premier instant (tn) et à un second instant (tn+1) suivant le premier instant (tn) au moyen d'au moins une unité de détection (3, 4) du dispositif de détection (2), (b) mémoriser (11 ) une information interne d'objet (Ok(tn)), laquelle correspond à l'information d'objet (Ok(tn)) détectée au premier instant (tn), dans une unité de calcul (5) du dispositif de détection (2), (c) pronostiquer (12) une information d'objet (O'k(tn+1)) pour le second instant (tn+1) sur la base de l'information interne d'objet (Ok(tn)), par l'unité de calcul (5), (d) actualiser (13) l'information d'objet pronostiquée ((O'k(tn+1)) en une nouvelle information interne d'objet (Ok(tn+1)), en fonction de l'information d'objet (Ok(tn+1)) détectée au second instant (tn+1), par l'unité de calcul (5), (e) comparer (17) l'information d'objet (O'k(tn+1)) pronostiquée pour le second instant (tn+1) avec l'information d'objet actualisée (Ok(tn+1)) et calculer une valeur d'innovation (lk[O'k(tn+1); Ok(tn+1)]) pour le au moins un objet (k), en fonction d'un résultat de la comparaison (17), par l'unité de calcul (5) et (f) vérifier (18) une plausibilité de la valeur d'innovation (lk[O'k(tn+1); Ok(tn+1)]), par l'unité de calcul (5) et (g) sortir (19) un message d'erreur, par l'unité de calcul (5), dans le cas où la vérification (18) fournit un résultat négatif, de manière à fournir une surveillance, la plus simple possible et n'affectant pas les ressources, d'une fonction du dispositif de détection (2), en particulier un dispositif de détection comportant plusieurs unités de détection (3, 4) dont les données sont fusionnées et ce, de sorte à satisfaire aux exigences d'une sécurité fonctionnelle du dispositif de détection (2).
PCT/EP2017/072846 2016-11-18 2017-09-12 Système de surveillance d'une fonction d'un dispositif de détection d'un véhicule à moteur WO2018091160A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102016122193.7 2016-11-18
DE102016122193.7A DE102016122193A1 (de) 2016-11-18 2016-11-18 Funktionsüberwachung einer Sensoreinrichtung eines Kraftfahrzeugs

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DE102022212107A1 (de) 2022-11-15 2024-05-16 Robert Bosch Gesellschaft mit beschränkter Haftung Sensorsystem und Fahrerassistenzsystem

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070073473A1 (en) * 2005-09-26 2007-03-29 Altan Osman D System and method of target tracking using sensor fusion
US20110025548A1 (en) * 2009-07-31 2011-02-03 Gm Global Technology Operations, Inc. System and method for vehicle sensor fusion
DE102010063984A1 (de) * 2010-02-11 2011-08-11 Continental Teves AG & Co. OHG, 60488 Fahrzeug-Sensor-Knoten
WO2015090849A1 (fr) * 2013-12-21 2015-06-25 Valeo Schalter Und Sensoren Gmbh Procédé pour faire fonctionner une pluralité de capteurs à ultrasons d'un véhicule automobile, dispositif de détection à ultrasons et véhicule automobile

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102004003868A1 (de) * 2004-01-26 2005-08-11 Ibeo Automobile Sensor Gmbh Verfahren zur Verfolgung von Objekten
DE102006051091B4 (de) * 2006-06-26 2021-10-28 Volkswagen Ag Verfahren zur Objekterkennung von Fahrzeugen mittels Nahbereichsdetektion
DE102009006113B4 (de) * 2008-03-03 2019-03-28 Volkswagen Ag Vorrichtung und Verfahren zur Sensorfusion mit dynamischen Objekten
US8775063B2 (en) * 2009-01-26 2014-07-08 GM Global Technology Operations LLC System and method of lane path estimation using sensor fusion
DE102014218703A1 (de) * 2014-09-17 2016-03-17 Continental Teves Ag & Co. Ohg Redundante Lokalisierung mittels GNSS-Signal

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070073473A1 (en) * 2005-09-26 2007-03-29 Altan Osman D System and method of target tracking using sensor fusion
US7460951B2 (en) 2005-09-26 2008-12-02 Gm Global Technology Operations, Inc. System and method of target tracking using sensor fusion
US20110025548A1 (en) * 2009-07-31 2011-02-03 Gm Global Technology Operations, Inc. System and method for vehicle sensor fusion
DE102010063984A1 (de) * 2010-02-11 2011-08-11 Continental Teves AG & Co. OHG, 60488 Fahrzeug-Sensor-Knoten
WO2015090849A1 (fr) * 2013-12-21 2015-06-25 Valeo Schalter Und Sensoren Gmbh Procédé pour faire fonctionner une pluralité de capteurs à ultrasons d'un véhicule automobile, dispositif de détection à ultrasons et véhicule automobile

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