EP1185883A1 - Model-supported allocation of vehicles to traffic lanes - Google Patents

Model-supported allocation of vehicles to traffic lanes

Info

Publication number
EP1185883A1
EP1185883A1 EP01929273A EP01929273A EP1185883A1 EP 1185883 A1 EP1185883 A1 EP 1185883A1 EP 01929273 A EP01929273 A EP 01929273A EP 01929273 A EP01929273 A EP 01929273A EP 1185883 A1 EP1185883 A1 EP 1185883A1
Authority
EP
European Patent Office
Prior art keywords
lane
block
vehicle
lanes
determined
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.)
Withdrawn
Application number
EP01929273A
Other languages
German (de)
French (fr)
Inventor
Klaus Winter
Jens Lueder
Werner Kederer
Jürgen DETLEFSEN
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 EP1185883A1 publication Critical patent/EP1185883A1/en
Withdrawn legal-status Critical Current

Links

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
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4004—Means for monitoring or calibrating of parts of a radar system
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • 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
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4052—Means for monitoring or calibrating by simulation of echoes
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G01S2013/932—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles using own vehicle data, e.g. ground speed, steering wheel direction
    • 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
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4004—Means for monitoring or calibrating of parts of a radar system
    • G01S7/4026—Antenna boresight
    • 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
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4004—Means for monitoring or calibrating of parts of a radar system
    • G01S7/4026—Antenna boresight
    • G01S7/403—Antenna boresight in azimuth, i.e. in the horizontal plane
    • 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
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4052—Means for monitoring or calibrating by simulation of echoes
    • G01S7/4082—Means for monitoring or calibrating by simulation of echoes using externally generated reference signals, e.g. via remote reflector or transponder
    • G01S7/4091—Means for monitoring or calibrating by simulation of echoes using externally generated reference signals, e.g. via remote reflector or transponder during normal radar operation

Definitions

  • the invention is based on a method for lane assignment of successive vehicles.
  • the majority of the known systems use a microwave radar beam or an infrared lidar beam to detect preceding vehicles and stationary as well as moving objects. This beam is reflected on the objects and received by the sensor, whereby the relative position and the relative speed of the objects can be determined. From this information, the future course range of the vehicle can be predicted, which is described in detail in patent specification DE 197 22 947 Cl.
  • the object of the invention is to enable lane detection and the detection of the lane being traveled and, if appropriate, horizontal misalignment detection from reflected signals.
  • the adaptive vehicle speed controller can expediently be used on multi-lane motorways, since there is a follow-up drive in most cases.
  • Sensor reference models for streets with different numbers of lanes and for driving in different lanes are stored.
  • the reference model which is most similar to the current measurement diagram, provides information on how many lanes the road has and which lane the vehicle is currently on. This result is output as a so-called lane hypothesis.
  • a misalignment can be determined by evaluating the transverse offsets of the reflection objects as a function of their long distance, that is to say the distance between the sensor and the reflection object, which is parallel to the center of the vehicle.
  • the advantage of this invention is to output a lane hypothesis using this simple analysis method of sensor data and to recognize any sensor misalignment that may be present.
  • Fig. 1 is a block diagram for model-based lane and misalignment detection
  • a radar object is an object confirmed in each case from one measurement to the next measurement by comparing predicted distance, transverse offset and relative speed data and determined measurement data.
  • the following treatments of the radar object data have proven to be expedient, (a) pre-filtering, i.e. each radar object is only taken into account once for the transverse offset histogram, or (b) weighted consideration of the individual objects in the histogram depending on the number of individual measurements of the individual radar objects.
  • the offset transverse to the center of the vehicle can be used as the input transverse offset, or the transverse offset (dyc) related to the course of the ACC vehicle can also be used to compensate for changes in transverse offset due to cornering.
  • the determined frequency distribution is correlated with a model for frequency distributions with respect to Lane allocation for multi-lane roads (e.g. 3 lanes) with a defined width or alternatively with characteristic transverse offset histograms for the different lanes used by the ACC vehicle.
  • the submodel with the highest correlation to the determined frequency distribution is given as a lane hypothesis (number of lanes and lane used by your own vehicle).
  • a model-based lane and misalignment detection is shown in FIG.
  • the radar object data such as distance, relative speed and lateral offset are obtained from the measurement data of the radar sensor.
  • these are filtered in an object filter, which is shown as block 2.
  • This filtering can be done in different ways. This advantageously takes place either by considering each object only once for the transverse offset histogram or by considering each object with a weighting, the weighting depending on how often an object was recognized in individual measurements.
  • These filtered data are then transferred to a transverse offset histogram, which is shown in block 3.
  • the frequency of the filtered object data as a function of the measured transverse offset to the longitudinal axis of the vehicle is stored in this transverse offset histogram.
  • lane models are stored which serve as reference histograms. These reference histograms are either model lane models or lane models that were obtained empirically.
  • a separate, characteristic reference histogram is stored for each type of road, whether with or without oncoming traffic, whether one or more lanes in one direction and for the use of each lane.
  • the currently determined, current transverse offset histogram from block 3 is correlated with each of the reference models stored in block 4. As a result, a correlation result is obtained for each correlation from the current cross offset histogram with one of the reference models, the higher the closer the current cross offset histogram and the reference histogram are.
  • the horizontal misalignment of the radar sensor can be determined from the position of the mean values for the lanes in the histogram in relation to the vehicle center axis.
  • a further histogram about the distance of the observed objects with equivalent object treatment (type (a) or (b)) must be stored, and a misalignment angle determined by determining the center of gravity of the histograms.
  • FIG. 2 shows a flow chart that is suitable for lane analysis and misalignment detection of a radar sensor.
  • a yaw rate signal can be used for this purpose, which comes, for example, from a sensor for driving dynamics control. It is also conceivable to take a steering angle into account. If this yaw rate signal is, for example, less than 0.001 rad / s, then one can conclude that a straight section of the route has been traveled. In this case, the amplitudes are filtered in block 8 in order to detect only actual radar reflections and to remove noise. In block 9, these measuring points are shown in an x, y diagram.
  • the frequencies with which the objects were recognized by the radar beam can be determined from the x, y diagram.
  • a distribution can be made from this x, y diagram in block 11 of the recognized objects on the roadway are modeled by generating a cross offset histogram.
  • the offset of the model generated in block 11 is further determined, which indicates the lateral placement of one's own vehicle in the driven lane.
  • the current transverse offset histogram is compared with the previous histogram. By observing the data record changes in block 13, a lane hypothesis can be output in block 14 which identifies the lane currently being used.
  • the angle of the dominant object which is in front of the driver's vehicle is determined in block 23.
  • the dominant object is advantageously the vehicle, which is moving in the same lane as the own vehicle and has the smallest distance from the own vehicle and is therefore decisive for the distance and speed control of the own vehicle.
  • block 24 it is checked whether the angle of the dominant object determined in block 23 is approximately 0 ° on average over time. If this condition of block 24 is met, then in block 25, together with the frequencies from the x, y diagram, which were determined in block 10, the current data is verified with old data from previous measurements.
  • a "locked” object is also determined from the x, y diagram of the filtered objects determined in block 9 in block 19.
  • This "locked” object is a vehicle immediately preceding it, its distance from its own vehicle and its relative speed in relation to own vehicle can be used for distance and speed control.
  • the position of this “locked” object is also forwarded to block 18 for determining a possible misalignment In this step 19, the center of gravity of the driving line can be determined in block 15 from the x, y diagram of block 9.
  • driving line focal points represent the lateral transverse offset of the movement trajectories of vehicles that move in the middle of a respective lane.
  • block 17 it can be seen from these driving line focal points whether the objects in the radar detection area move parallel to the driver's own vehicle, which is of particular interest in maneuvering maneuvers.
  • the dominant object can be observed separately from the driving line focal points of step 15 in block 16 and supplied to block 17 by recognizing whether the recognized objects are moving parallel to one's own vehicle.
  • the information obtained in step 17 regarding the parallelism of the detected objects is fed to the misalignment detection of the radar sensor in block 18.
  • This parallel speed is the speed of the detected objects, based on your own vehicle. From these parallel speeds, the new positions of the detected radar objects are further calculated in block 21 on the basis of their old positions and their movement trajectories. These pre-calculated targets are compared with the new measurement data for the next measurement cycle and checked for plausibility. From the data obtained in step 21, a statistical center of gravity of the transverse offsets is determined in step 22, which is fed to block 18 and is used there to determine a possible sensor de- junction. In block 26 it is also shown that a radiant angle of the vehicle is determined from the radar measurement.
  • the float angle of the driving Stuff determined by means of a further device is advantageously done by using vehicle dynamics variables from a device for vehicle dynamics control, which is already standard in most vehicles.
  • the two float angles determined in steps 26 and 27 are compared with one another in block 28 and any difference between these two sizes of the sensor misalignment detection is passed on in block 18.
  • the flowchart shown in Figure 2 partially includes several procedures and approaches to determine a size.
  • the determination of a misalignment (18) was demonstrated using several options. In order to implement a lane determination or determination of a sensor misalignment, it is sufficient according to the invention to use one of the listed procedures in each case. It is also conceivable to combine two or more procedures with one another, in which case the individual results can be compared with one another and checked for plausibility.

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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)
  • Electromagnetism (AREA)
  • Radar Systems Or Details Thereof (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention relates to a method and a device for allocating successive vehicles to traffic lanes. The allocation of the vehicles to traffic lanes takes place with the aid of a model, using a frequency distribution of the lateral misalignments of detected radar objects. The inventive method can also be used to detect maladjustment of the sensor.

Description

Modellgestutzte Fahrspurzuordnung für FahrzeugeModel-based lane assignment for vehicles
Stand der TechnikState of the art
Die Erfindung geht aus von einem Verfahren zur Fahrspurzuordnung von aufeinanderfolgenden Fahrzeugen.The invention is based on a method for lane assignment of successive vehicles.
In den vergangenen Jahren sind zahlreiche Veröffentlichungen bekannt geworden, die sich mit einer automatischen Regelung der Geschwindigkeit eines Fahrzeugs unter Berücksichtigung des Ab- Standes zu vorausfahrenden Fahrzeugen beschäftigen. Solche Systeme werden häufig als Adaptive Cruise Control (ACC) bzw. im Deutschen als adaptive Fahrgeschwindigkeitsregler bezeichnet.In recent years, numerous publications have become known which deal with an automatic regulation of the speed of a vehicle, taking into account the distance to preceding vehicles. Such systems are often referred to as adaptive cruise control (ACC) or in German as adaptive cruise control.
Eine grundsatzliche Beschreibung einer solchen Vorrichtung ist beispielsweise in dem Aufsatz „Adaptive Cruise Controls - System Aspects and Development Trends" von Winner, Witte et al . , veröffentlicht auf der SAE 96 vom 26. bis 29. Februar 1996 in Detroit (SAE-Paper No . 961010), enthalten.A basic description of such a device is, for example, in the article "Adaptive Cruise Controls - System Aspects and Development Trends" by Winner, Witte et al., Published on SAE 96 from February 26-29, 1996 in Detroit (SAE paper no 961010).
Zur Detektion vorherfahrender Fahrzeuge und stehender sowie be- wegter Objekte verwendet die Mehrzahl der bekannten Systeme einen Mikrowellen-Radarstrahl oder einen Infrarot-Lidarstrahl . Dieser Strahl wird an den Objekten reflektiert und vom Sensor empfangen, wodurch die Relativposition und die Relativgeschwindigkeit der Objekte ermittelt werden kann. Aus diesen Informa- tionen kann man den zukunftigen Kursbereich des Fahrzeugs vorhersagen, was in der Patentschrift DE 197 22 947 Cl ausfuhrlich beschrieben ist. Aufgabe der Erfindung ist es, aus reflektierten Signalen eine Fahrspurerkennung sowie die Erkennung der selbst befahrenen Spur und gegebenenfalls eine horizontale Dejustageerkennung zu ermöglichen. Der adaptive Fahrgeschwindigkeitsregler laßt sich zweckmaßigerweise auf mehrspurigen Kraftfahrstraßen einsetzen, da dort in den meisten Fällen eine Folgefahrt vorliegt.The majority of the known systems use a microwave radar beam or an infrared lidar beam to detect preceding vehicles and stationary as well as moving objects. This beam is reflected on the objects and received by the sensor, whereby the relative position and the relative speed of the objects can be determined. From this information, the future course range of the vehicle can be predicted, which is described in detail in patent specification DE 197 22 947 Cl. The object of the invention is to enable lane detection and the detection of the lane being traveled and, if appropriate, horizontal misalignment detection from reflected signals. The adaptive vehicle speed controller can expediently be used on multi-lane motorways, since there is a follow-up drive in most cases.
Durch die Spurerkennung sowie durch die Erkennung der Fahrspur, die durch das eigene Fahrzeug befahren wird, kann man die bewegten Objekte, die sich vor dem eigenen Fahrzeug befinden, den entsprechenden Fahrspuren zuordnen. Durch diese Objektzuordnung zu den Fahrspuren laßt sich zuverlässig das direkt vorherfahrende Zielobjekt bestimmen, dessen Geschwindigkeit und Beschleunigung das Fahrverhalten des eigenen, sensorgesteuerten Fahrzeugs bestimmt. Diese Fahrspurzuordnung erfolgt, indem in einem Speicher desThrough lane detection and through the detection of the lane that the vehicle is traveling through, one can assign the moving objects that are in front of the vehicle to the corresponding lanes. Through this object assignment to the lanes, the directly preceding target object can be reliably determined, the speed and acceleration of which determine the driving behavior of the own, sensor-controlled vehicle. This lane assignment is done by in a memory of the
Sensors Referenzmodelle für Straßen mit unterschiedlich vielen Fahrspuren sowie für das Befahren der unterschiedlichen Fahrspuren hinterlegt sind. Durch Einlesen der gewonnenen Meßdaten in ein Querversatzhistogramm, in dem die Häufigkeitsverteilung der Querversatze der einzelnen Objekte aufgetragen sind, kann man dieses aktuelle Meßdiagramm mit den hinterlegten Referenzmodellen korrelieren. Das Referenzmodell, das die größte Ähnlichkeit mit dem aktuellen Meßdiagramm besitzt, gibt Auskunft, wieviel Fahrspuren die Straße besitzt und auf welcher Fahrspur sich das Fahrzeug momentan befindet. Dieses Ergebnis wir als sogenannte Fahrspurhypothese ausgegeben.Sensor reference models for streets with different numbers of lanes and for driving in different lanes are stored. By reading the measurement data obtained into a cross offset histogram in which the frequency distribution of the cross misalignments of the individual objects is plotted, one can correlate this current measurement diagram with the stored reference models. The reference model, which is most similar to the current measurement diagram, provides information on how many lanes the road has and which lane the vehicle is currently on. This result is output as a so-called lane hypothesis.
Durch Auswertung der Querversatze der Reflexionsobjekte in Abhängigkeit ihrer Langsentfernung, also der Entfernung zwischen Sensor und Reflexionsobjekt, die parallel zur Fahrzeug ittelach- se ist, ist eine Dejustage feststellbar.A misalignment can be determined by evaluating the transverse offsets of the reflection objects as a function of their long distance, that is to say the distance between the sensor and the reflection object, which is parallel to the center of the vehicle.
Der Vorteil dieser Erfindung ist es, mittels dieser einfachen Analysemethode von Sensordaten eine Fahrspurhypothese auszugeben und eine eventuell vorliegende Sensordejustage zu erkennen. ZeichnungThe advantage of this invention is to output a lane hypothesis using this simple analysis method of sensor data and to recognize any sensor misalignment that may be present. drawing
Ein Beispiel der Erfindung ist in der Zeichnung dargestellt und wird im folgenden naher beschrieben und erläutert. Es zeigenAn example of the invention is shown in the drawing and is described and explained in more detail below. Show it
Fig. 1 ein Blockschaltbild zur modellgestutzten Fahrspur- und Dejustageerkennung undFig. 1 is a block diagram for model-based lane and misalignment detection and
Fig. 2 Losungsansatze zur Fahrspurbestimmung und Dejustageerkennung des Sensors.Fig. 2 Approaches to lane determination and misalignment detection of the sensor.
Beschreibung des AusfuhrungsbeispielsDescription of the exemplary embodiment
Wesentlich ist, daß eine Häufigkeitsverteilung der Querversätze von erfaßten Radarobjekten ermittelt wird. Ein Radarobjekt ist ein von einer Messung zur nächsten Messung durch Vergleich von prädizierten Abstands-, Querversatz und Relativgeschwindigkeits- daten und ermittelten Meßdaten jeweils bestätigtes Objekt. Folgende Behandlungen der Radarobjektdaten haben sich als zweckmäßig erwiesen, (a) ein vorabfiltern, d.h. jedes Radarobjekt wird nur einmal für das Querversatzhistogramm berücksichtigt oder (b) ein in Abhängigkeit von der Anzahl der Einzelmessung der einzelnen Radarobjekte gewichtetes Berücksichtigen der Einzelobjekte in dem Histogramm. Als Eingangsgroße Querversatz kann einerseits der auf die Fahrzeugmitte bezogene Versatz (dyv) verwendet wer- den, oder andererseits zur Kompensation von Querversatzanderun- gen aufgrund von Kurvenfahrten auch der auf den Kurs des ACC- Fahrzeugs bezogene Querversatz (dyc) . Die ermittelte Häufigkeitsverteilung wird korreliert mit einem Modell für Häufigkeitsverteilungen bzgl . Spurzuordnung bei mehrspurigen Straßen (z.B. 3 Fahrspuren) mit definierter Breite oder alternativ mit charakteristischen Querversatzhistogrammen für die unterschiedlichen, vom ACC-Fahrzeug benutzten Fahrspuren. Das Teilmodell it der höchsten Korrelation zur ermittelten Haufigkeitsvertei- lung wird als Fahrspurhypothese ausgegeben (Anzahl Fahrspuren und vom eigenen Fahrzeug benutzte Fahrspur) .It is essential that a frequency distribution of the transverse offsets of detected radar objects is determined. A radar object is an object confirmed in each case from one measurement to the next measurement by comparing predicted distance, transverse offset and relative speed data and determined measurement data. The following treatments of the radar object data have proven to be expedient, (a) pre-filtering, i.e. each radar object is only taken into account once for the transverse offset histogram, or (b) weighted consideration of the individual objects in the histogram depending on the number of individual measurements of the individual radar objects. The offset transverse to the center of the vehicle (dyv) can be used as the input transverse offset, or the transverse offset (dyc) related to the course of the ACC vehicle can also be used to compensate for changes in transverse offset due to cornering. The determined frequency distribution is correlated with a model for frequency distributions with respect to Lane allocation for multi-lane roads (e.g. 3 lanes) with a defined width or alternatively with characteristic transverse offset histograms for the different lanes used by the ACC vehicle. The submodel with the highest correlation to the determined frequency distribution is given as a lane hypothesis (number of lanes and lane used by your own vehicle).
In Figur 1 wird eine modellgestutzte Fahrspur- und Dejustageer- kennung dargestellt. In Block 1 des Flußdiagramms werden die Radarobjektdaten wie Abstand, Relativgeschwindigkeit und Querversatz aus den Meßdaten des Radarsensors gewonnen. Diese werden in einem nächsten Schritt in einem Objektfilter, der als Block 2 dargestellt ist, gefiltert. Dieses Filtern kann auf unterschied- liehe Arten geschehen. Vorteilhafterweise geschieht dieses entweder indem jedes Objekt nur einmal für das Querversatzhistogramm berücksichtigt wird oder indem jedes Objekt mit einer Ge- wichtung berücksichtigt wird, wobei die Gewichtung davon abhangig ist, wie oft ein Objekt in Einzelmessungen erkannt wurde. Diese gefilterten Daten werden weiterführend in ein Querversatzhistogramm, das in Block 3 dargestellt ist, übernommen. In diesem Querversatzhistogramm wird die Häufigkeit der gefilterten Objektdaten in Abhängigkeit des gemessenen Querversatzes zur Fahrzeuglangsachse gespeichert. In Block 4 sind Fahrspurmodelle gespeichert, die als Referenzhistogramme dienen. Diese Referenzhistogramme sind entweder modellhafte Fahrspurmodelle oder Fahrspurmodelle, die empirisch gewonnen wurden. Für jeden Straßentyp, ob mit oder ohne Gegenverkehr, ob ein oder mehrere Fahrspuren in einer Richtung und für die Benutzung jeweils jeder Fahr- spur ist ein eigenes, charakteristisches Referenzhistogramm gespeichert. In Block 5 wird das momentan ermittelte, aktuelle Querversatzhistogramm aus dem Block 3 mit jedem der in Block 4 hinterlegten Referenzmodelle korreliert. Als Ergebnis erhalt man für jede Korrelation aus dem aktuellen Querversatzhistogramm mit einem der Referenzmodelle ein Korrelationsergebnis, das umso hoher ist, je ahnlicher sich das aktuelle Querversatzhistogramm und das Referenzhistogramm sind. Durch Auswahl des Referenzhi- stogrammes, das in Block 5 das höchste Korrelationsergebnis aufweist, kann man auf die Anzahl der Fahrspuren, die benutzte Fahrspur sowie eine mögliche Dejustage des Radarsensors schließen. In Block 6 werden diese gewonnenen Informationen ausgegeben und für eine weitergehende Verarbeitung bereitgestellt. Das in Figur 1 dargestellte Ablaufdiagramm wird beliebig oft durchlau- fen, das bedeutet, wenn in Block 6 eine Fahrspurhypothese und gegebenenfalls eine Sensordejustage ermittelt wurden, beginnt der Ablauf von neuem, indem in Block 1 neue Radardaten in gleicher Weise wie beschrieben, verarbeitet werden. Je nach Anzahl der detektierten Fahrspuren und deren relativer Position zum ei- genen Fahrzeug erhalt man in Block 3 ein Histogramm mit mehreren Maxi a. Aus der Position der Mittelwerte für die Fahrspuren im Histogramm bezogen auf die Fahrzeugmittelachse kann die horizontale Dejustage des Radarsensors bestimmt werden. Hierzu muß neben dem Querversatz dyv oder alternativ dyc ein weiteres Histo- gramm über den Abstand der beobachteten Objekte mit äquivalenter Objektbehandlung (Art (a) oder (b) ) abgelegt werden, und über Schwerpunktbestimmung der Histogramme ein Dejustagewinkel bestimmt werden.A model-based lane and misalignment detection is shown in FIG. In block 1 of the flow chart, the radar object data such as distance, relative speed and lateral offset are obtained from the measurement data of the radar sensor. In a next step, these are filtered in an object filter, which is shown as block 2. This filtering can be done in different ways. This advantageously takes place either by considering each object only once for the transverse offset histogram or by considering each object with a weighting, the weighting depending on how often an object was recognized in individual measurements. These filtered data are then transferred to a transverse offset histogram, which is shown in block 3. The frequency of the filtered object data as a function of the measured transverse offset to the longitudinal axis of the vehicle is stored in this transverse offset histogram. In block 4, lane models are stored which serve as reference histograms. These reference histograms are either model lane models or lane models that were obtained empirically. A separate, characteristic reference histogram is stored for each type of road, whether with or without oncoming traffic, whether one or more lanes in one direction and for the use of each lane. In block 5, the currently determined, current transverse offset histogram from block 3 is correlated with each of the reference models stored in block 4. As a result, a correlation result is obtained for each correlation from the current cross offset histogram with one of the reference models, the higher the closer the current cross offset histogram and the reference histogram are. By selecting the reference histogram, which has the highest correlation result in block 5, one can determine the number of lanes that are used Close lane and possible misalignment of the radar sensor. In block 6, the information obtained is output and made available for further processing. The flow diagram shown in FIG. 1 is run through as often as desired, which means that if a lane hypothesis and possibly a sensor misalignment have been determined in block 6, the process begins anew by processing new radar data in block 1 in the same way as described. Depending on the number of lanes detected and their relative position to one's own vehicle, a histogram with several maxi a is obtained in block 3. The horizontal misalignment of the radar sensor can be determined from the position of the mean values for the lanes in the histogram in relation to the vehicle center axis. For this purpose, in addition to the transverse offset dyv or alternatively dyc, a further histogram about the distance of the observed objects with equivalent object treatment (type (a) or (b)) must be stored, and a misalignment angle determined by determining the center of gravity of the histograms.
In Figur 2 ist ein Ablaufdiagramm dargestellt, das zur Fahrspuranalyse und Dejustageerkennung eines Radarsensors geeignet ist. In Block 7 wird erkannt, ob sich das Fahrzeug auf einem graden Straßenabschnitt befindet. Hierzu kann man ein Gierratensignal heranziehen, das beispielsweise aus einem Sensor zur Fahrdynamikregelung stammt. Weiterhin ist auch denkbar, einen Lenkwinkel mit zu berücksichtigen. Ist dieses Gierratensignal beispielsweise kleiner als 0.001 rad/s, so kann man auf das Befahren eines geraden Streckenabschnittes schließen. In diesem Fall werden in Block 8 die Amplituden gefiltert, um nur tatsach- liehe Radarreflexionen zu erfassen und Rauschen zu entfernen. In Block 9 werden diese Meßpunkte in einem x,y-Diagramm dargestellt. In Block 10 kann man aus dem x,y-Diagramm die Häufigkeiten bestimmen, mit der die Objekte vom Radarstrahl erkannt wurden. Aus diesem x,y-Diagramm kann in Block 11 eine Verteilung der erkannten Objekte auf der Fahrbahn modelliert werden indem ein Querversatzhistogramm erzeugt wird. Weiterführend wird in Block 12 der Versatz des in Block 11 erzeugten Modells bestimmt, der auf die laterale Ablage des eigenen Fahrzeugs in der befah- renen Spur schließen laßt. In Block 13 wird das momentane Querversatzhistogramm mit dem vorherigen Histogramm verglichen. Durch die Beobachtung der Datensatzanderungen in Block 13 läßt sich in Block 14 eine Fahrspurhypothese ausgeben, die die momentan benutzte Fahrspur identifiziert. Wird in Block 7 erkannt, dass sich das Fahrzeug auf einem geraden Streckenabschnitt befindet, so wird in Block 23 der Winkel des dominanten Objektes bestimmt, das sich vor dem eigenen Fahrzeug befindet. Das dominante Objekt ist vorteilhafterweise das Fahrzeug, das sich in der gleichen Fahrspur bewegt wie das eigene Fahrzeug und den ge- ringsten Abstand zum eigenen Fahrzeug aufweist und damit für die Abstands- und Geschwindigkeitsregelung des eigenen Fahrzeugs ausschlaggebend ist. In Block 24 wird geprüft, ob der in Block 23 ermittelte Winkel des dominanten Objektes im zeitlichen Mittel etwa 0° ist. Ist diese Bedingung des Blockes 24 gegeben, so wird m Block 25, gemeinsam mit den Häufigkeiten aus dem x,y- Diagramm, die in Block 10 ermittelt wurden, eine Verifikation der aktuellen Daten mit alten Daten aus vorhergehenden Messungen durchgeführt. Sind die aktuellen Daten aufgrund der in Block 25 durchgeführten Verifikation plausibel, so werden diese Daten im weiteren Verlauf für eine Bestimmung einer möglichen Dejustage des Radarsensors benutzt, indem diese an Block 18 weitergegeben werden. Aus dem in Block 9 bestimmten x,y-Diagramm der gefilterten Objekte wird weiterhin in Block 19 ein „gelocktes" Objektes bestimmt. Dieses „gelockte" Objekt ist ein unmittelbar vorher- fahrendes Fahrzeug, dessen Abstand zum eigenen Fahrzeug und dessen Relativgeschwindigkeit in Bezug zum eigenen Fahrzeug für die Abstands- und Geschwindigkeitsregelung verwendet werden. Auch die Position dieses „gelockten" Objektes wird an Block 18 zur Bestimmung einer möglichen Dejustage weitergegeben. Parallel zu diesem Schritt 19 können in Block 15 aus dem x,y-Diagramm des Blockes 9 die Fahrlinienschwerpunkte bestimmt werden. Diese Fahrlinienschwerpunkte repräsentieren den lateralen Querversatz der Bewegungstrajektorien von Fahrzeugen, die sich mittig auf einer jeweiligen Fahrspur bewegen. Aus diesen Fahrlinienschwerpunkten kann in Block 17 erkannt werden, ob sich die Objekte im Radarerfassungsbereich parallel zum eigenen Fahrzeug bewegen, was insbesondere bei Fahrspurwechselmanövern von besonderem Interesse ist. Parallel zu diesem Schritt kann aus den Fahrlinien- Schwerpunkten des Schrittes 15 in Block 16 das dominante Objekt separat beobachtet und Block 17 zugeführt werden, indem erkannt wird, ob sich die erkannten Objekte parallel zum eigenen Fahrzeug bewegen. Die in Schritt 17 gewonnene Information bezuglich der Parallelität der erkannten Objekte wird der Dejustageerken- nung des Radarsensors in Block 18 zugeführt. Weiterhin ist es vorteilhaft, bei einem in Block 7 erkannten, geraden Streckenabschnitt aus den vorliegenden Radardaten wie Winkelgeschwindigkeit und Relativgeschwindigkeit die Parallelgeschwindigkeiten zu bestimmen, wie es in Block 20 dargestellt ist. Diese Parallelge- schwindigkeit sind die Geschwindigkeiten der erkannten Objekte, bezogen auf das eigene Fahrzeug. Aus diesen Parallelgeschwindig- keiten werden weiterführend in Block 21 die neuen Positionen der erkannten Radarobjekte auf Grundlage ihrer alten Positionen und ihrer Bewegungstrajektorien vorausberechnet. Diese vorausberech- neten Ziele werden mit den neuen Meßdaten des nächsten Meßzy- klusses verglichen und auf Plausibilitat überprüft. Aus den in Schritt 21 gewonnenen Daten wird in Schritt 22 ein statistischer Schwerpunkt der Querversatze ermittelt, der dem Block 18 zugeführt wird und dort zur Bestimmung einer möglichen Sensordeju- stage verwendet wird. In Block 26 ist weiterhin dargestellt, dass aus der Radarmessung ein Schwimmwinkel des Fahrzeugs bestimmt wird. Dies geschieht mittels einer Beobachtung der Abstände und Relativgeschwindigkeiten der Radarobjekte. In einem weiteren Schritt in Block 27 wird der Schwimmwinkel des Fahr- zeugs mittels einer weiteren Vorrichtung bestimmt, dies geschieht vorteilhafterweise durch Heranziehen fahrdynamischer Großen aus einer Vorrichtung zur Fahrdynamikregelung, die m den meisten Fahrzeugen bereits serienmäßig vorhanden ist. Die beiden in den Schritten 26 und 27 ermittelte Schwimmwinkel werden in Block 28 miteinander verglichen und eine eventuell vorhanden Differenz dieser beiden Großen der Sensordejustageerkennung in Block 18 weitergegeben.FIG. 2 shows a flow chart that is suitable for lane analysis and misalignment detection of a radar sensor. In block 7 it is recognized whether the vehicle is on a straight road section. A yaw rate signal can be used for this purpose, which comes, for example, from a sensor for driving dynamics control. It is also conceivable to take a steering angle into account. If this yaw rate signal is, for example, less than 0.001 rad / s, then one can conclude that a straight section of the route has been traveled. In this case, the amplitudes are filtered in block 8 in order to detect only actual radar reflections and to remove noise. In block 9, these measuring points are shown in an x, y diagram. In block 10, the frequencies with which the objects were recognized by the radar beam can be determined from the x, y diagram. A distribution can be made from this x, y diagram in block 11 of the recognized objects on the roadway are modeled by generating a cross offset histogram. In block 12, the offset of the model generated in block 11 is further determined, which indicates the lateral placement of one's own vehicle in the driven lane. In block 13, the current transverse offset histogram is compared with the previous histogram. By observing the data record changes in block 13, a lane hypothesis can be output in block 14 which identifies the lane currently being used. If it is recognized in block 7 that the vehicle is on a straight section of the route, the angle of the dominant object which is in front of the driver's vehicle is determined in block 23. The dominant object is advantageously the vehicle, which is moving in the same lane as the own vehicle and has the smallest distance from the own vehicle and is therefore decisive for the distance and speed control of the own vehicle. In block 24 it is checked whether the angle of the dominant object determined in block 23 is approximately 0 ° on average over time. If this condition of block 24 is met, then in block 25, together with the frequencies from the x, y diagram, which were determined in block 10, the current data is verified with old data from previous measurements. If the current data is plausible on the basis of the verification carried out in block 25, then this data will be used in the further course to determine a possible misalignment of the radar sensor by passing it on to block 18. A "locked" object is also determined from the x, y diagram of the filtered objects determined in block 9 in block 19. This "locked" object is a vehicle immediately preceding it, its distance from its own vehicle and its relative speed in relation to own vehicle can be used for distance and speed control. The position of this “locked” object is also forwarded to block 18 for determining a possible misalignment In this step 19, the center of gravity of the driving line can be determined in block 15 from the x, y diagram of block 9. These driving line focal points represent the lateral transverse offset of the movement trajectories of vehicles that move in the middle of a respective lane. In block 17, it can be seen from these driving line focal points whether the objects in the radar detection area move parallel to the driver's own vehicle, which is of particular interest in maneuvering maneuvers. In parallel to this step, the dominant object can be observed separately from the driving line focal points of step 15 in block 16 and supplied to block 17 by recognizing whether the recognized objects are moving parallel to one's own vehicle. The information obtained in step 17 regarding the parallelism of the detected objects is fed to the misalignment detection of the radar sensor in block 18. It is also advantageous to determine the parallel speeds for a straight section of the route identified in block 7 from the available radar data such as angular speed and relative speed, as shown in block 20. This parallel speed is the speed of the detected objects, based on your own vehicle. From these parallel speeds, the new positions of the detected radar objects are further calculated in block 21 on the basis of their old positions and their movement trajectories. These pre-calculated targets are compared with the new measurement data for the next measurement cycle and checked for plausibility. From the data obtained in step 21, a statistical center of gravity of the transverse offsets is determined in step 22, which is fed to block 18 and is used there to determine a possible sensor de- junction. In block 26 it is also shown that a radiant angle of the vehicle is determined from the radar measurement. This is done by observing the distances and relative speeds of the radar objects. In a further step in block 27, the float angle of the driving Stuff determined by means of a further device, this is advantageously done by using vehicle dynamics variables from a device for vehicle dynamics control, which is already standard in most vehicles. The two float angles determined in steps 26 and 27 are compared with one another in block 28 and any difference between these two sizes of the sensor misalignment detection is passed on in block 18.
Das in Figur 2 dargestellte Ablaufdiagramm beinhaltet teilweise mehrere Vorgehensweisen und Losungsansatze zur Bestimmung einer Große. So wurde die Bestimmung einer Dejustage (18) mittels mehrerer Möglichkeiten aufgezeigt. Zur Umsetzung einer Fahrspurbestimmung oder Bestimmung einer Sensordejustage reicht es erfin- dungsgemaß aus, jeweils eine der aufgeführten Vorgehensweisen zu verwenden. Es ist weiterhin denkbar, zwei oder mehrere Vorgehensweisen miteinander zu kombinieren, wobei die jeweiligen Ein- zelergebnisse miteinander verglichen und auf Plausibilitat überpr ft werden können. The flowchart shown in Figure 2 partially includes several procedures and approaches to determine a size. The determination of a misalignment (18) was demonstrated using several options. In order to implement a lane determination or determination of a sensor misalignment, it is sufficient according to the invention to use one of the listed procedures in each case. It is also conceivable to combine two or more procedures with one another, in which case the individual results can be compared with one another and checked for plausibility.

Claims

Ansprüche Expectations
1. Verfahren zur Fahrspurzuordnung von aufeinanderfolgenden Fahrzeugen auf mehrspurigen Straßen, dadurch gekennzeichnet, dass die Fahrspurzuordnung modellgestutzt über eine Häufigkeitsverteilung der Querversätze von erfaßten Radarobjekten erfolgt.1. Method for the lane assignment of successive vehicles on multi-lane roads, characterized in that the lane assignment is model-based via a frequency distribution of the transverse offsets of detected radar objects.
2. Vorrichtung zur Durchfuhrung des Verfahrens nach Anspruch 1, dadurch gekennzeichnet, dass die ermittelte Häufigkeitsverteilung mit hinterlegten Modellen für Häufigkeitsverteilungen von Querversatzen korreliert wird, wobei in diesen Modellen Spurzu- Ordnung bei mehrspurigen Straßen (z.B. 3 Fahrspuren) mit definierter Breite oder alternativ charakteristische Querversatzhistogramme für die unterschiedlichen, vom Folge-Fahrzeug benutzten Fahrspuren, berücksichtigt werden (siehe Figur 1).2. Device for carrying out the method according to claim 1, characterized in that the determined frequency distribution is correlated with stored models for frequency distributions of transverse misalignments, in these models lane assignment in multi-lane roads (eg 3 lanes) with a defined width or alternatively characteristic transverse offset histograms for the different lanes used by the following vehicle are taken into account (see Figure 1).
3. Vorrichtung zur Durchfuhrung des Verfahrens nach wenigstens einem der Ansprüche 1 oder 2, dadurch gekennzeichnet, dass das Teilmodell mit der höchsten Korrelation zur ermittelten Häufigkeitsverteilung als Fahrspurhypothese ausgegeben wird (Anzahl Fahrspuren sowie die vom eigenen Fahrzeug benutzte Fahrspur) .3. Device for performing the method according to at least one of claims 1 or 2, characterized in that the sub-model with the highest correlation to the frequency distribution determined is output as a lane hypothesis (number of lanes and the lane used by one's own vehicle).
4. Verfahren zur Dejustageerkennung eines Sensors auf Reflexionsbasis, der insbesondere zur Durchfuhrung eines Verfahrens nach wenigstens einem der vorangehenden Ansprüche benutzbar ist, dadurch gekennzeichnet, dass aus der Position der Mittelwerte für die Fahrspuren in einem Histogramm bezogen auf die Fahrzeugachse, die horizontale Dejustage erkennbar ist.4. A method for detecting misalignment of a sensor on the basis of reflection, which can be used in particular to carry out a method according to at least one of the preceding claims, characterized in that the horizontal misalignment is recognizable from the position of the mean values for the lanes in a histogram relative to the vehicle axis ,
5. Vorrichtung zur Durchführung des Verfahrens nach Anspruch 4, dadurch gekennzeichnet, dass neben einem Histogramm für den Querversatz dyv oder alternativ dyc ein weiteres Histogramm für den Abstand der beobachteten Objekte mit äquivalenter Objektbehandlung ablegbar und über Schwerpunktbestimmung der Histogramme ein Dejustagewinkel bestimmbar ist. 5. Device for performing the method according to claim 4, characterized in that in addition to a histogram for the Cross offset dyv or alternatively dyc another histogram for the distance between the observed objects can be stored with equivalent object treatment and a misalignment angle can be determined by determining the center of gravity of the histograms.
EP01929273A 2000-03-28 2001-03-28 Model-supported allocation of vehicles to traffic lanes Withdrawn EP1185883A1 (en)

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