EP3899588A1 - Procédé pour déterminer un domaine d'intégrité - Google Patents

Procédé pour déterminer un domaine d'intégrité

Info

Publication number
EP3899588A1
EP3899588A1 EP19817669.5A EP19817669A EP3899588A1 EP 3899588 A1 EP3899588 A1 EP 3899588A1 EP 19817669 A EP19817669 A EP 19817669A EP 3899588 A1 EP3899588 A1 EP 3899588A1
Authority
EP
European Patent Office
Prior art keywords
integrity
integrity information
sensor
determining
information
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.)
Pending
Application number
EP19817669.5A
Other languages
German (de)
English (en)
Inventor
Lena SCHINDLER
Marco Limberger
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.)
Robert Bosch GmbH
Original Assignee
Robert Bosch 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 Robert Bosch GmbH filed Critical Robert Bosch GmbH
Publication of EP3899588A1 publication Critical patent/EP3899588A1/fr
Pending legal-status Critical Current

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0205—Diagnosing or detecting failures; Failure detection models
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/20—Integrity monitoring, fault detection or fault isolation of space segment
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0205—Diagnosing or detecting failures; Failure detection models
    • B60W2050/0215—Sensor drifts or sensor failures
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/20—Data confidence level
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/35—Data fusion

Definitions

  • the invention relates to a method for determining an integrity area, a computer program for carrying out a corresponding method, a machine-readable storage medium on which the computer program is stored and a control device for a vehicle, the control device being set up to carry out a corresponding method.
  • the invention is particularly suitable in connection with the
  • the autonomous vehicle usually has sensors, such as inertial sensors, wheel sensors,
  • the vehicle can estimate its own position.
  • information about its (expected) estimation accuracy is also output for a determined own position.
  • the confidence of the determined own position can be represented by a so-called “Protection Level” (short: "PL").
  • the PL can describe a statistical error limit, the calculation of which is usually based on statistical considerations and, if necessary, additionally on a suitable coordination of the estimation algorithms.
  • a method for determining an integrity area of a parameter estimate, the integrity area describing the area in which an estimated parameter is associated with a
  • the minimum probability is (actually), the method comprising at least the following steps: a) determining first integrity information based on at least data from at least one first sensor or a first method for
  • Steps a) and b) can preferably be carried out at least partially in parallel or even simultaneously. Steps a) and b) are furthermore preferably carried out at least partially before step c).
  • the integrity area describes the area in which an estimated
  • the estimated parameter (value) basically describes a (single, in particular momentary) estimation result of the parameter estimation.
  • the integrity area describes the area in which a real or actual value of an estimated parameter lies with a minimum probability.
  • Integrity area can also be referred to as the so-called “protection level”.
  • the minimum probability is usually a predefined minimum probability. The is preferably
  • Minimum probability 90% particularly preferably 95% or even 99%.
  • the minimum probability with which a real or actual value of an estimated parameter is actually in a protection level is still much higher than with "usual" integrity areas.
  • the minimum probability here is usually over 99.99%, particularly preferably over 99.999% or even over 99.9999%.
  • the minimum probability can also not be expressed in percent, but in possible errors in a certain time interval.
  • a protection level can, for example, be defined in such a way that the parameter in question is outside the protection level at most once every 10 years.
  • the protection level can be, for example, either as a unitless probability or as a rate, i.e. as
  • the probability of an error occurring over a time interval can be expressed.
  • the method is preferably used to determine an integrity range which describes the integrity of an estimate of an own position or another driving operation parameter. In other words, this means
  • the parameter is preferably a
  • Driving operating parameters such as a vehicle's own position.
  • the method can (thus) for example determine a
  • Integrity range serve a position estimate of a vehicle position.
  • the area of integrity can describe the area in which an estimated own position of a vehicle with a minimum probability (actually) lies.
  • the data can be recorded with sensors of the vehicle.
  • the method can also be used to estimate the vehicle's own speed, orientation, own movement or the like.
  • the solution proposed here has the particular advantage that the determination
  • a Kalman filter for example, can be used as the main navigation solution. The determined with it
  • integrity information is also subject to a certain smoothing, which is characteristic of (Kalman) filters.
  • smoothing may be undesirable for integrity information, for example, in an environment where integrity can change rapidly, such as in urban areas where, for example, canyons can be shaded in urban canyons.
  • Integrity information that was determined (with regard to the same estimated parameter) on the basis of different sensors and / or with different methods is now proposed for the first time in order to thus merge from the (different types, e.g. smoothed and unsmoothed)
  • Integrity information to obtain a merged range of integrity that can dynamically describe the integrity of the (overall) estimate as
  • the parameter estimation can include one or more methods for estimating a (the same) parameter.
  • the parameter estimation can comprise at least two methods that differ from one another, for example a first method and a second method for estimating the parameter that differs from the first method.
  • Methods for estimating the parameter are preferably used, which can also provide and / or determine integrity information about the integrity of the estimate.
  • a first piece of integrity information is determined, in particular via the parameter estimate or for an estimated parameter, on the basis of data from a first sensor and / or a first method for determining the integrity information.
  • the first sensor is preferably a sensor of a motor vehicle.
  • the first sensor can be a GNSS sensor, an (optical and / or acoustic) environment sensor (such as a radar sensor, lidar sensor, ultrasonic sensor and / or camera sensor) Act inertial sensor and / or a wheel sensor (such as wheel speed sensor and / or wheel circumference speed sensor).
  • an (optical and / or acoustic) environment sensor such as a radar sensor, lidar sensor, ultrasonic sensor and / or camera sensor
  • Act inertial sensor and / or a wheel sensor such as wheel speed sensor and / or wheel circumference speed sensor.
  • the first method can basically be the same method (-nart) or one of the methods (-art) on which the parameter estimation is based. However, this is not absolutely necessary. Rather, it is also conceivable that the first method is independent of the method on which the parameter estimation is based.
  • step b second integrity information is determined
  • the parameter estimation in particular via the parameter estimation or to a (the) estimated parameter, based on at least data from at least one second sensor that differs from the first sensor or a second method that differs from the first method for determining the
  • Integrity information in particular that in step b) a second piece of integrity information about an estimation result of the
  • Determination of the integrity information is determined.
  • the second sensor is preferably a sensor of a motor vehicle.
  • the second sensor can be a GNSS sensor, an (optical and / or acoustic) environment sensor (such as a radar sensor, lidar sensor, ultrasound sensor and / or camera sensor), an inertial sensor and / or a wheel sensor (such as such as wheel speed sensor and / or wheel circumference speed sensor).
  • an (optical and / or acoustic) environment sensor such as a radar sensor, lidar sensor, ultrasound sensor and / or camera sensor
  • an inertial sensor and / or a wheel sensor such as such as wheel speed sensor and / or wheel circumference speed sensor.
  • the second method can basically be the same method (-nart) or one of the methods (-art) on which the parameter estimation is based. However, this is not absolutely necessary. Rather, it is also conceivable that the second method is independent of the method on which the parameter estimation is based.
  • the data used in steps a) and b) generally have the same time stamp or time stamps located close to one another in time.
  • the first method and the second method for determining the integrity information can also be the same or of the same type. For example, to determine the first
  • Integrity information and the second integrity information data from different sensors or sensor types are processed using methods of the same type, for example, each is filtered.
  • step c) the integrity area is determined by fusing at least the first integrity information and the second integrity information (with one another). In this case, for example, an optionally weighted overlay of the first integrity information with at least the second integrity information (and / or possibly further (for example third, fourth, etc.) integrity information) takes place.
  • the range of integrity is preferably a confidence interval.
  • a confidence interval also known as the confidence interval or confidence interval and expectation range
  • the confidence interval specifies the range that at infinite
  • Repetition of a random experiment with a certain probability includes the true location of the parameter.
  • the integrity information can be, for example, a variance and / or a residual of a respective parameter estimate.
  • Integrity information is also information that is determined as a function of a variance and / or a residual and / or an (other) indicator of the trustworthiness of the estimate.
  • the first integrity information can be a first confidence area and the second integrity information can be a second confidence area.
  • a driving operation parameter is used here in particular
  • the driving operation parameter helps at least to describe an own movement and / or own position of a motor vehicle.
  • the driving operation parameter can be, for example, an (own) position, a (own) speed, (own) acceleration or a position (or orientation) of the motor vehicle.
  • the driving operation parameter is preferably a self-position of the motor vehicle.
  • At least the first sensor or the second sensor be in or on one
  • the first sensor can be a GNSS sensor that receives navigation satellite data.
  • the second sensor can be a (further)
  • Act vehicle sensor (which is not a GNSS sensors).
  • vehicle sensors can, for example, be an inertial sensor (IMU’s, i.e. inertial measuring units), wheel speed sensor, steering angle sensor,
  • the first sensor or the second sensor can also be an optical sensor, such as a (stereo) camera sensor, a RADAR sensor or LIDAR sensor, or an acoustic sensor, such as
  • a first integrity area is determined as the first integrity information.
  • step a) a determination of a first integrity range for an estimated parameter based on at least data from at least one first sensor or a first method for determining the Integrity area.
  • a first protection level is preferably determined as first integrity information.
  • a second integrity area is determined as second integrity information.
  • a second one can be determined in step b)
  • a second protection level is preferably determined as second integrity information.
  • a further protection level (third protection level, fourth protection level, etc.) can be determined as further (third, fourth, etc.) integrity information.
  • a protection level usually describes the (spatial,
  • the estimated parameter (value) basically describes a (single, in particular momentary) estimation result of the parameter estimation.
  • the protection level describes the area in which there is a real or actual value of an estimated parameter with a minimum probability.
  • a protection level describes in particular a confidence interval or a (spatial) confidence range, in which the true value of an estimated parameter can be compared with a
  • the estimated value of the parameter is usually in the middle or the center of the confidence interval or confidence range.
  • an overall integrity area is determined as the integrity area, which is determined by merging at least the first integrity area and the second integrity area.
  • an overall protection level is determined as the integrity area, which is determined by merging at least the first protection level and the second protection level. Even if none before First or second protection levels have been determined, it is preferred that the integrity area is a protection level or that it is output as a protection level.
  • step c) a (cross) plausibility check of the first protection level with the second protection level and / or vice versa can also take place. Furthermore, a (cross) plausibility check of the first protection level and / or the second protection level can take place with a further (third) protection level and / or vice versa.
  • the methods for determining the integrity information can in particular be two or more of the following methods: least squares method, in particular “sequential least squares”, filter methods, in particular those which can be implemented using a Kalman filter, for example "Extended Kalman Filters”, “Unscented Kalman Filters”, and / or particle filters, hatch filters are also basic
  • Integrity information uses the least squares method.
  • the least squares method can be used particularly advantageously in order to carry out a comparatively (in particular in comparison to a Kalman filter) dynamic determination, in particular (exclusively) on the basis of GNSS data or navigation satellite data.
  • the method of least squares (short: MKQ or English: “least squares”, short: LS; obsolete: method of least squares of deviations) is a standard mathematical method for the adjustment calculation.
  • a curve is sought for a data point cloud that runs as close as possible to the data points.
  • Integrity information a Kalman filter can be used.
  • the determination by means of a Kalman filter can be used particularly advantageously in order to carry out a comparatively reliable determination (in particular in comparison with the least squares method).
  • Kalman filtering techniques are often used to estimate target parameters such as position, speed, location and / or time (PVAT) in localization sensors that process the input measurements in real time.
  • Measurements in automotive applications could be, for example, the observation of GNSS (global navigation satellite systems), IMU (inertial measurement unit), wheel speed sensors and / or steering angle sensors.
  • GNSS global navigation satellite systems
  • IMU intial measurement unit
  • wheel speed sensors and / or steering angle sensors.
  • Optical sensors such as radar, lidar or cameras could also be part of the
  • the Kalman filter is set up to perform a sensor data fusion of navigation satellite data (GNSS data) and data of at least one further vehicle sensor, such as inertial data.
  • GNSS data navigation satellite data
  • inertial data data of at least one further vehicle sensor, such as inertial data.
  • the criteria for using Kalman filtering are varied.
  • the data history can be taken into account when adapting PVAT updates in order to advantageously improve the stability of the solution.
  • the least squares method is used as the first method and a Kalman filter provides the second method, it is particularly preferred that the fusion takes place in such a way that the gradients or rapid changes of the least squares method to the
  • the first integrity information and the second integrity information be weighted in step c). This allows the particular advantage that the merging can be improved depending on the situation.
  • the weighting can preferably take place as a function of an accuracy requirement for the estimate. For example, a higher positional accuracy may be required during a parking maneuver than during a driving maneuver on the highway. In addition, the accuracy requirement can also increase with the driving speed of the vehicle.
  • the weighting can take place depending on the environment in which the sensors and / or the vehicle are currently located.
  • the weight that is associated with integrity information can be significantly or even exclusively based on Navigation satellite data (GNSS data) is increased if the sensors or the vehicle is on a freeway or country road or has a clear view of navigation satellites.
  • GNSS data Navigation satellite data
  • the weights, to which integrity information is assigned which are based at least in part on vehicle sensors (which are not GNSS sensors), could be increased if the sensors or the vehicle are located in a city, in particular in a house gorge. For example, this can help reduce reception of
  • only those integrity information items that are based at least in part on satellite data or GNSS data can be taken into account at times. This can take place, for example, for the period of time in which other vehicle sensors are faulty or defective.
  • this also means in particular that (at times) either the first or the second (or possibly (only) the further)
  • Integrity information can be output as a (current) integrity area. This takes place in particular depending on the availability of the corresponding sensor information.
  • the weighting can take place, for example, in such a way that a compromise between the reliability of reliably recognizing an estimation error and the reaction time for recognizing the estimation error is realized.
  • second integrity information can be given more confidence, for example by adapting appropriate weights.
  • the weighting is preferably (also) dependent on the properties of the methods used to determine the integrity information. For example, the weight assigned to a more dynamic method can be increased if the integrity range is to be determined more dynamically. In particular, the weighting in this context is dependent on the dynamic properties and / or the filter properties of the methods used.
  • third integrity information for the estimated parameter is determined on the basis of at least data from at least one third sensor or a third method for determining the integrity information, and the third integrity information is also taken into account in the fusion in step c) .
  • a computer program is also used
  • a machine-readable storage medium is also proposed, on which the computer program presented here is stored.
  • the machine-readable storage medium is usually a computer-readable data carrier.
  • a control device for a motor vehicle is also proposed, the control device being set up to carry out a method presented here.
  • the control device is preferably a device (or a computer) for localization.
  • a motor vehicle can also be specified which has a control unit presented here.
  • the motor vehicle is preferably an automated and / or autonomously operating vehicle, in particular an autonomous automobile.
  • FIG. 1 schematically shows an exemplary sequence of a method proposed here.
  • the method is used to determine an integrity area 1 of a parameter estimate, the integrity area describing the area in which an estimated parameter with a minimum probability lies.
  • the sequence of method steps a), b) and c) shown with blocks 110, 120 and 130 is generally established in a normal operating sequence.
  • steps a) and b) can be carried out at least partially in parallel or even simultaneously.
  • first integrity information 5 is determined on the basis of at least data 2 from at least one first sensor 13 or a first method 4 for determining the integrity information.
  • second integrity information 7 is determined on the basis of at least data 3 from at least one second sensor 14 that differs from the first sensor or a second method 6 that differs from the first method 4 for determining the integrity information.
  • A occurs in block 130 Determining the integrity area 1 by fusing at least the first integrity information 5 and the second integrity information 7.
  • FIG. 2 schematically shows a motor vehicle 10 with an exemplary one
  • Embodiment of a control device 11 proposed here The control device 11 is set up to carry out a method proposed here.
  • the control unit 11 is used here, for example, to determine a current own position of the motor vehicle 10.
  • the control unit 11 can receive data 2, 3, which enable a conclusion to be drawn about the vehicle 10's own position.
  • GNSS data 2 can be received by navigation satellites 12 via an antenna 13 of the vehicle 10.
  • GNSS stands for Global Navigation Satellite System.
  • the GNSS data 2 comprise, for example, signals which allow a conclusion to be drawn about the position of the respective satellite 12 and the duration of which can be evaluated in order to determine the intrinsic position of the vehicle 10 by means of a triangulation.
  • vehicle-internal data 3 from sensors of the vehicle such as an inertial sensor 14 (IMU), can be a sensor of the vehicle.
  • IMU inertial sensor
  • the own position of the motor vehicle 10 is estimated using two methods 4, 6, which run here as examples, and integrity information 5, 7 is determined for the estimated own position.
  • the methods 4, 6 are those for estimating the self-position of the motor vehicle 10 and for determining the integrity information 5, 7 for the estimated self-position.
  • the own position is therefore an example of the estimated parameter.
  • the first method 4 uses the least squares method as an example.
  • the least squares method is used to estimate the (current) own position of the vehicle 10 on the basis of (only) the acquired GNSS data 2 and to provide first integrity information 5 about the integrity of the self position thus estimated.
  • the least squares method which generally only takes into account one type of data, here GNSS data 2 for example, and works regularly without considering a processing history and / or data history, usually reacts very well dynamic on measured value changes. A disadvantage of this method, however, can be seen in a reduced accuracy (compared to the Kalman filter).
  • a first estimation result usually includes the (estimated) own position of the vehicle 10 determined by means of the least squares method.
  • the first integrity information 5 usually comprises an (information) integrity information determined by means of the least squares method.
  • the first integrity information item 5 can be, for example, a deviation from the expected value when using the least squares method
  • Act actual position This can include a variance and / or a residual, for example.
  • the first integrity information 5 can already include a first protection level.
  • the second estimate is carried out here using a Kalman filter as an example.
  • the second method 5 is accordingly based on a (sensor) fusion of the GNSS data 2 with vehicle-internal data 3 and usually also takes into account the processing history and / or data history.
  • Own position and the second integrity information 7 about the integrity of the second estimation result will be rather smooth or smoothed and can be interpreted as a model-driven low-pass filter of the input measurements.
  • the second estimation result generally includes the (estimated) own position of the vehicle 10 determined using the Kalman filter and, as second integrity information 7, includes (estimated) integrity information determined using the Kalman filter about the integrity of the (estimated) determined using the Kalman filter ) Own position of the vehicle 10.
  • the second integrity information item 7 can be, for example, a deviation from the actual own position to be expected when the Kalman filter is used. This can include a variance and / or a residual, for example. Alternatively or cumulatively, the second
  • Integrity information 7 also already include a second protection level.
  • the integrity area 1 is here by fusing at least the first integrity information 5 and the second integrity information 7 or as Result of a fusion 17 of first integrity information 5 and second integrity information 7 determined.
  • the first integrity information 5 and the second integrity information 7 can also be weighted.
  • the fusion can take place in such a way that the gradients or rapid changes of the least squares method, which here represents the first method 4 and has a low accuracy (compared to the second method 6), to the precise and smoothed background solution of the Kalman Filter, which provides the second method 6 here, is added.
  • the gradients or rapid changes of the least squares method which here represents the first method 4 and has a low accuracy (compared to the second method 6)
  • the precise and smoothed background solution of the Kalman Filter which provides the second method 6 here, is added. This is illustrated graphically and by way of example in FIG. 3.
  • the weighting can be carried out, for example, in such a way that a compromise between the reliability of reliably recognizing an estimation error and the reaction time for recognizing the estimation error is realized.
  • second integrity information 7 can be given more confidence, for example by adapting appropriate weights.
  • the first integrity information 5, which is based on the least squares method can be compensated for by a so-called “hatch” filter applied to the difference between the two solutions.
  • Integrity area 1 can thereby advantageously the dynamics of
  • This integrity area 1 can be, for example, a (total) protection level of the (currently) determined own position of the vehicle.
  • FIG. 2 illustrates by way of example that at least one third piece of integrity information 9 for the estimated parameter can also be determined on the basis of at least data 3 from at least one third sensor 15 or a third method 8 for determining the integrity information that the further integrity information 9 can be taken into account in the merger in step d).
  • Deviation information 18 As shown in FIG. 3, the deviation information 18 is plotted over time 19.
  • the upper course with a solid line shows the time course of the first integrity information 5. This is determined here, for example, on the basis of the least squares method.
  • the first integrity information 5 describes here, for example, the deviation from the actual position of the vehicle that is to be expected when (only) using the least squares method.
  • the lower course with a solid line shows the time course of the second integrity information 7. This is determined here, for example, on the basis of a Kalman filter solution or is output by a Kalman filter.
  • the second integrity information 7 describes here, for example, the deviation from the actual position of the vehicle to be expected when using (only) the Kalman filter.
  • the course with the dashed line exemplifies the course of the integrity region 1 determined as a result of the merger 17. This combines the dynamic properties of the first integrity information 5 with the reliability of the second integrity information 7.

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • Computer Security & Cryptography (AREA)
  • Automation & Control Theory (AREA)
  • Human Computer Interaction (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Navigation (AREA)
  • Traffic Control Systems (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

L'invention concerne un procédé pour déterminer un domaine d'intégrité (1) d'une estimation de paramètre, le domaine d'intégrité (1) décrivant le domaine dans lequel se situe un paramètre estimé avec une probabilité minimale, ledit procédé comprenant au moins les étapes suivantes : a) détermination d'une première information d'intégrité (5) sur la base au moins de données (2) d'au moins un premier capteur (13) ou d'une première méthode (4) servant à la détermination de l'information d'intégrité, b) détermination d'une deuxième information d'intégrité (7) sur la base au moins de données (3) d'au moins un deuxième capteur (14) différent du premier capteur ou d'une deuxième méthode (6) différente de la première méthode (4), servant à la détermination de l'information d'intégrité, c) détermination du domaine d'intégrité (1) par fusion d'au moins la première information d'intégrité (5) et de la deuxième information d'intégrité (7).
EP19817669.5A 2018-12-18 2019-12-09 Procédé pour déterminer un domaine d'intégrité Pending EP3899588A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102018222166.9A DE102018222166A1 (de) 2018-12-18 2018-12-18 Verfahren zum Ermitteln eines Integritätsbereichs
PCT/EP2019/084171 WO2020126596A1 (fr) 2018-12-18 2019-12-09 Procédé pour déterminer un domaine d'intégrité

Publications (1)

Publication Number Publication Date
EP3899588A1 true EP3899588A1 (fr) 2021-10-27

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
EP19817669.5A Pending EP3899588A1 (fr) 2018-12-18 2019-12-09 Procédé pour déterminer un domaine d'intégrité

Country Status (7)

Country Link
US (1) US20220063642A1 (fr)
EP (1) EP3899588A1 (fr)
JP (1) JP7284268B2 (fr)
KR (1) KR20210102262A (fr)
CN (1) CN113196109B (fr)
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Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102020212042A1 (de) * 2020-09-24 2022-03-24 Robert Bosch Gesellschaft mit beschränkter Haftung Verfahren zum Bereitstellen von Informationen über die Verlässlichkeit einer Parameterschätzung eines Parameters für den Betrieb eines Fahrzeugs
CN112902966A (zh) * 2021-01-28 2021-06-04 开放智能机器（上海）有限公司 一种融合定位系统及方法
DE102022128703A1 (de) 2022-10-28 2024-05-08 Sick Ag Verfahren zum Betreiben einer Positionsmessvorrichtung
CN117451043B (zh) * 2023-12-25 2024-03-15 武汉大学 一种数-模混合估计的多源融合定位方法及系统

Family Cites Families (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000292538A (ja) * 1999-04-07 2000-10-20 Mitsubishi Electric Corp 車両用障害物検出装置
FR2830320B1 (fr) * 2001-09-28 2003-11-28 Thales Sa Centrale de navigation inertielle hybryde a integrite amelioree
US10007675B2 (en) * 2007-07-31 2018-06-26 Robert Bosch Gmbh Method of improving database integrity for driver assistance applications
JP5251090B2 (ja) * 2007-11-30 2013-07-31 セイコーエプソン株式会社 測位方法、プログラム及び測位装置
US20090182493A1 (en) * 2008-01-15 2009-07-16 Honeywell International, Inc. Navigation system with apparatus for detecting accuracy failures
FR2928741B1 (fr) * 2008-03-11 2020-06-26 Thales Dispositif et procede de surveillance d'integrite en temps reel d'un systeme de navigation par satellite
US8243136B2 (en) * 2008-03-11 2012-08-14 Panasonic Corporation Tag sensor system and sensor device, and object position estimating device and object position estimating method
US20110118979A1 (en) * 2009-11-19 2011-05-19 Robert Bosch Gmbh Automotive location data integrity
EP2402785B1 (fr) * 2010-06-23 2013-04-03 Astrium GmbH Algorithme RAIM amélioré
EP2428770B1 (fr) * 2010-09-08 2013-04-24 Harman Becker Automotive Systems GmbH Système de navigation de véhicule
US9558157B2 (en) * 2011-10-24 2017-01-31 Continental Teves Ag & Co. Ohg Sensor system for independently evaluating the accuracy of the data of the sensor system
FR3004826B1 (fr) * 2013-04-18 2015-05-08 Sagem Defense Securite Procede de controle d'integrite et dispositif de fusion-consolidation comprenant un pluralite de modules de traitement
JP2015102330A (ja) * 2013-11-21 2015-06-04 古野電気株式会社 移動情報算出装置、移動情報算出方法、移動情報算出プログラム、および移動体
EP3316001B1 (fr) * 2016-10-25 2024-06-12 Centre National d'Etudes Spatiales Amélioration collaborative du positionnement d'un véhicule
US20180186468A1 (en) * 2017-01-04 2018-07-05 Honeywell International Inc. System and methods to evaluate or improve own ship sensor data in connected vehicles
CN108761497B (zh) * 2018-03-06 2019-12-06 北京交通大学 一种列车卫星定位局域完好性监测方法及系统
CN108508461B (zh) * 2018-03-27 2022-09-23 千寻位置网络（浙江）有限公司 基于gnss载波相位高精度定位完好性监测方法

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US20220063642A1 (en) 2022-03-03
CN113196109B (zh) 2024-07-19
JP7284268B2 (ja) 2023-05-30
DE102018222166A1 (de) 2020-06-18
WO2020126596A1 (fr) 2020-06-25
JP2022513511A (ja) 2022-02-08
KR20210102262A (ko) 2021-08-19

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