WO2019053013A1 - Procédé de détermination de la position d'un véhicule automobile dans une environnement et dispositif de commande d'un véhicule automobile et moyen informatique pour fonctionner sur un réseau de données - Google Patents

Procédé de détermination de la position d'un véhicule automobile dans une environnement et dispositif de commande d'un véhicule automobile et moyen informatique pour fonctionner sur un réseau de données Download PDF

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Publication number
WO2019053013A1
WO2019053013A1 PCT/EP2018/074490 EP2018074490W WO2019053013A1 WO 2019053013 A1 WO2019053013 A1 WO 2019053013A1 EP 2018074490 W EP2018074490 W EP 2018074490W WO 2019053013 A1 WO2019053013 A1 WO 2019053013A1
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WO
WIPO (PCT)
Prior art keywords
landmark
motor vehicle
characteristic
features
maneuver
Prior art date
Application number
PCT/EP2018/074490
Other languages
German (de)
English (en)
Inventor
Friedrich Schweizer
Original Assignee
Bayerische Motoren Werke Aktiengesellschaft
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 Bayerische Motoren Werke Aktiengesellschaft filed Critical Bayerische Motoren Werke Aktiengesellschaft
Priority to CN201880051101.8A priority Critical patent/CN111051817B/zh
Publication of WO2019053013A1 publication Critical patent/WO2019053013A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30—Map- or contour-matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

Definitions

  • the invention relates to a method for determining a position of a motor vehicle in an environment. For this purpose, it is recognized that the motor vehicle passes a predetermined landmark whose position is known. Thus, it can be deduced from the position of the landmark on the position of the motor vehicle.
  • the invention also includes a control device for a motor vehicle in order to carry out the method according to the invention. Furthermore, the invention provides a stationary computing device.
  • a method of the type mentioned is known for example from DE 10 201 1 1 12 404 A1.
  • an object in the surroundings is detected as a landmark by means of a camera and then the relative position of the motor vehicle with respect to the object is determined in order to determine the intrinsic position of the motor vehicle starting from the known position of the object and the relative position.
  • such a method requires complex object recognition in image data of the camera.
  • the relative position of the motor vehicle with respect to the object must be able to be measured.
  • a method is known, by means of which thresholds are recognized on a road surface by a motor vehicle. Since the position of the bumps is known, the position of the motor vehicle can thus be equated with the position of the bumps when driving over the bumps.
  • the method detects the bumps on the basis of a vibration pattern, for example, a roar, which is caused when crossing the thresholds in the motor vehicle.
  • a vibration pattern for example, a roar
  • the invention provides a method for determining a position of a motor vehicle in an environment.
  • the position may be an absolute geoposition or a relative position, for example with respect to a predetermined reference object.
  • the method uses the approach known from the prior art to provide a respective position indication of the landmark for at least one predetermined landmark of the environment. So it is at least one landmark and their position in the area known.
  • the position of the landmark is described by a corresponding position indication. According to the method, it is recognized that the motor vehicle currently passes the at least one landmark in each case. It can therefore be recognized during a trip each time when the motor vehicle passes or passes a known landmark.
  • the position information of each currently passed landmark is then output as the current position of the motor vehicle.
  • a driving maneuver is in particular a time sequence of steering settings and / or acceleration settings and / or braking settings to understand.
  • a landmark may be a characteristic double curve of a road, which can be recognized by the corresponding steering behavior of the motor vehicle.
  • the respective maneuvering characteristic of the landmark indicates predetermined typical or characteristic features of the respective driving maneuver, eg first a right turn, followed by a left turn. In other words, the maneuver characteristic describes how the motor vehicle has to be driven or guided while passing the landmark.
  • the landmark is detected by means of a longitudinal guide (acceleration and / or braking) and / or transverse guidance (steering) of the motor vehicle.
  • a longitudinal guide acceleration and / or braking
  • / or transverse guidance steering
  • the invention by means of at least one detection device of the motor vehicle description data of each current from generated the motor vehicle driven maneuver.
  • predetermined characteristics of the driving maneuver are extracted from the description data.
  • the currently passed landmark is identified or recognized.
  • a predetermined matching criterion for the features to be compared can be provided.
  • a predetermined tolerance interval can be provided for a difference between the features, within which a match of the respective features is still signaled. If all the extracted features or at least a predetermined minimum proportion of the extracted features then agree with the respectively corresponding characteristic features of a maneuver characteristic according to the matching criterion, the landmark described by this maneuver characteristic is recognized as the one that is currently being passed.
  • the invention provides the advantage that only the driving behavior of the motor vehicle itself, in particular with respect to the longitudinal guide and the transverse guide, must be detected in order to derive or determine the position of the motor vehicle from this. There is no elaborate detection of vehicle-external objects, be it visible objects and / or bumps, necessary.
  • the invention includes other embodiments that provide additional benefits.
  • the description data is generated on the basis of a dead-reckoning method based on a vehicle movement of the motor vehicle (dead-reckoning) by means of the at least one detection device.
  • an odometry of the motor vehicle is used.
  • a position signal of a GNSS Global Navigation Satellite System
  • GPS Global Positioning System
  • Such a position signal of a GNSS has a greater variance or scattering than a position detection by means of odometry.
  • the vehicle movement can be determined more accurately than by means of a position signal of a GNSS.
  • description data for at least one of the following driving dynamics variables are determined by means of the at least one detection device: a wheel speed of at least one wheel, an acceleration in at least one spatial direction, a yaw rate, a vehicle speed, a steering angle.
  • the at least one detection device can for this purpose each provide a sensor and / or signal processing.
  • a sensor for example, a wheel speed sensor, an acceleration sensor, a steering angle sensor and / or a yaw rate sensor may be provided.
  • the driving speed can be determined by means of a speed detection, which can be based for example on a measured wheel speed of the wheels of the motor vehicle.
  • the description data may therefore contain sensor data and / or processed and / or combined sensor data.
  • the described dynamic driving variables can be detected reliably in a motor vehicle.
  • a detection device can also be formed by said receiver of the position signal of a GNSS, which would be used here only in combination with at least one further detection device.
  • An embodiment provides, in particular, that the detection of the passage of the landmark takes place independently of an image recognition of objects in the environment. As a result, one is not dependent on, for example, the visibility in the environment in an advantageous manner.
  • the description data is detected by means of a plurality of detection devices.
  • a movement model of the motor vehicle In order to combine the description data from different detection devices, it is provided that they are combined by means of a movement model of the motor vehicle. This ensures that a plausible description of the driving maneuver results.
  • An example of such a movement model is the single-track model known per se.
  • the characteristic features of the predetermined maneuvering characteristic and the features extracted from the descriptive data each include at least one of a landmark entry point, a marker exit point of the landmark, a radius of a driven curve, a curvature of the driven curve Yaw rate, an entrance direction (for example, a direction) toward the landmark, an exit direction (for example, a direction) away from the landmark, a corner corner point.
  • a curve corner point can be determined, for example, by extending the entry direction and the exit direction, the intersection of which then yields the curve corner point.
  • the entry direction here is based on the curve entry point, the exit direction on the basis of the curve exit point extended.
  • the at least one landmark each comprise at least one intersection and / or at least one curve.
  • the driving maneuver that results when driving through at least one intersection and / or curve has proved to be reliably recognizable in an advantageous manner.
  • a turning maneuver and / or cornering may be provided corresponding to the respective landmark as a characteristic driving maneuver.
  • the resulting sequence of steering settings and / or acceleration settings and / or brake settings reliably identifies recognizable features.
  • a separate maneuver characteristic is provided for different lanes or lanes of a road of the environment (in particular for adjacent lanes).
  • the position determination can be carried out with exact lane or lane accuracy.
  • a provisional geoposition of the motor vehicle is determined by means of a receiver for a position signal of a GNSS.
  • this provisional geoposition thus determined is subject to scattering or variance and thus correspondingly inaccurate.
  • From a plurality of stored maneuvering characteristics at least one maneuvering characteristic is now selected whose assigned position lies in a predetermined surrounding area around the provisional geoposition.
  • the comparison of the extracted features of the current driving maneuver is then limited to the at least one selected maneuver characteristic.
  • the calculation effort in carrying out the method can be limited by specifying the size of the surrounding area.
  • the surrounding area has a diameter of more than 10 m, in particular more than 20 m.
  • the diameter is preferably larger than one for the the receiver of the position signal of the GNSS characteristic scattering. This advantageously ensures that the correct landmark is in the set of selected landmarks.
  • the surrounding area has a diameter of less than 500 m, in particular less than 300 m.
  • the driving maneuver is preferably determined on the basis of an odometry of the motor vehicle.
  • this can have a drift and / or an offset. Therefore, it is preferably provided that an offset of the odometriebas elected position detection is determined or corrected by means of the at least one landmark determined respective position of the motor vehicle, as may be caused by the drift and / or offset.
  • the offset of a trajectory detection based on a relative position detection is corrected or at least reduced.
  • the odometriebasiere position detection can also be used for detecting a driving trajectory for long distances, especially longer than 500 m, in an advantageous manner.
  • Some embodiments relate to the question of how the respective maneuver characteristic of the respective landmark can be formed.
  • the method steps associated therewith constitute a separate aspect of the invention, which can also be carried out without the method steps described above.
  • description data which describe a respective driving maneuver of the other vehicle when passing the respective landmark are preferably received from a plurality of foreign vehicles.
  • a foreign vehicle may be a motor vehicle that has passed the landmark even before the motor vehicle, which currently passes the landmark.
  • respective predetermined features are extracted. From the extracted features a respective frequency distribution is formed and from the respective frequency distribution one of the characteristic features is then determined.
  • a characteristic feature can therefore be, for example, an average of the extracted features from the foreign vehicles. This results in the advantage that the influence of the individual driving trajectory of each individual other vehicle is relativized. Examples of such characteristic features are: an average curve radius, an averaged entry point into the landmark, a averaged exit point from the landmark, an averaged absolute direction indication (heading, eg as an indication of a compass direction).
  • One embodiment provides that in at least one frequency distribution two lane-accurate landmarks are identified on the basis of mathematical local extreme positions (ie maxima and / or minima) of the frequency distribution.
  • a predetermined lane width can be used in addition to the plausibility check.
  • the invention also provides a control device for a motor vehicle.
  • the control device may be mounted in the motor vehicle e.g. be provided as at least one control unit or be coupled as a server of the Internet via a radio link with the motor vehicle.
  • the control device can be provided by a stationary computing device, ie as a computer or computer network. It may also be provided a hybrid form with at least one control device and at least one server.
  • the control device is set up to carry out an embodiment of the method according to the invention.
  • the invention also relates to the described forming of the respective maneuver characteristic of the at least one landmark.
  • a stationary computing device for operating on a data network is provided, wherein the computing device is adapted to perform a method with the method steps that have been described in connection with the formation of maneuver characteristics.
  • FIG. 1 is a schematic representation of a motor vehicle whose position is detected by an embodiment of the method according to the invention
  • FIG. 2 is a diagram for illustrating a method step of an embodiment of the method according to the invention, which can be carried out by a control device;
  • FIG. 1 is a schematic representation of a motor vehicle whose position is detected by an embodiment of the method according to the invention
  • FIG. 2 is a diagram for illustrating a method step of an embodiment of the method according to the invention, which can be carried out by a control device
  • FIG. 1 is a schematic representation of a motor vehicle whose position is detected by an embodiment of the method according to the invention
  • FIG. 2 is a diagram for illustrating a method step of an embodiment of the method according to the invention, which can be carried out by a control device
  • FIG. 1 is a schematic representation of a motor vehicle whose position is detected by an embodiment of the method according to the invention
  • FIG. 2 is a diagram for illustrating a method step of an embodiment of the method according to
  • Fig. 3 is a diagram illustrating a statistical distribution of
  • Fig. 5 is a diagram for illustrating a statistical distribution of a characteristic feature of the maneuver characteristics of Fig. 4, on the basis of which a distinction can be made between the two lanes.
  • Fig. 1 shows a motor vehicle 10.
  • the motor vehicle 10 may be, for example, a passenger car or truck.
  • the motor vehicle 10 can, starting from a past or historical position 1 1 traveled by driving along a road 12 a termerajektorie 13 and this have also determined by means of a dead-reckoning method.
  • the driving trajectory 13 can use the dead reckoning method to ascertain the relative changes in position that result over time that arise as a result of driving along the trajectory 13.
  • the dead reckoning method used may have an offset or drift 14 to be compensated for.
  • predetermined landmarks 15 are detected while driving along the road 12, whose positions 16 can be known in a geo-coordinate system 17 and described by a respective position indication.
  • the respective position 16 is synonymous here for the associated position information.
  • a landmark 15 may each, for example, a curve or a turn-off of the road 12 act.
  • the motor vehicle 10 when passing or passing through the respective landmark 15 a predetermined, characteristic of the landmark 15 sequence of maneuvering, ie steering operations and / or acceleration operations and / or braking. These can be determined by means of an odometry of the motor vehicle 10.
  • a current position of the motor vehicle 10 can also be detected in the motor vehicle 10 - but only with an accuracy that is less than the accuracy of the dead reckoning method.
  • the determined travel trajectory 13 is more accurate in terms of relative position changes than the receiver for the GNSS, but an absolute location of the determined travel trajectory 13 with respect to the geo-coordinate system 17 is subject to e.g. the described drift 14.
  • the respective driving through the landmarks 15 is recognized in the motor vehicle 10 and then, based on a position indication of the respective position 16 of the detected landmark 15 by means of a displacement 18, the determined driving trajectory 13 lane in the coordinate system 17 or arranged anchored.
  • the method steps carried out for this purpose can be performed by a control device ECU of the motor vehicle 10, e.g. be performed by a controller.
  • a region or a surrounding area 19 can be determined in which the motor vehicle 10 is currently located in each case.
  • the GNSS position determined for this purpose represents an approximate localization or preliminary geoposition, ie a location with the accuracy of the receiver for the position signal.
  • the preliminary geoposition may be the center of the environmental region 19. Then, for example, it can then be determined in a database which possible landmarks 15 can actually be located in the vicinity of the motor vehicle 10 at all. All landmarks 15 whose position 16 lies within the current region 19 are selected.
  • the motor vehicle 10 during its travel to individual driving maneuvers of the motor vehicle 10, for example, a cornering or a turning maneuver, extract from each description data of a detection device predetermined features 20.
  • a curved entry point 21, a curve entry direction 22, a curve exit point 23, a curve exit direction 24, a curve corner point 25 and a curve radius 26 are illustrated by way of example as curved features.
  • drift drift
  • the respective landmark 15 is not significant due to the relatively small spatial extent of the respective landmark 15 (e.g., less than 1 km, in particular less than 500 m), and therefore the respective landmark 15 can be recognized from the extracted features 20.
  • an average value for the respective feature 20 can now be stored for each of the determined or extracted features 20.
  • Such an average value is an example of a typical or characteristic feature.
  • the totality or amount of the characteristic features of a landmark 15 represents a maneuvering characteristic for driving through the respective landmark 15.
  • a comparison of the extracted features 20 with the characteristic features of the respective landmark 15, ie the maneuver characteristic of the landmark 15, are performed.
  • a tolerance interval may be provided for determining a match of an extracted feature 20 with a corresponding characteristic feature (e.g., for the feature "curve radius").
  • this landmark 15 is signaled as a currently traversed landmark.
  • the position 16 of this currently traveled or passed landmark 15 is then used as the current position of the motor vehicle 10 in order to anchor the determined travel trajectories 13 in the manner described by means of the displacement 18 at the detected positions 16.
  • Fig. 2 illustrates how the characteristic features for a single landmark
  • the method steps described below may be performed by a stationary computing device SRV, e.g. can be realized as a server of the Internet.
  • the computing device SRV can communicate with motor vehicles via a respective communication link for data exchange.
  • the respective communication link can e.g. a mobile connection and / or an Internet connection.
  • the characteristic features result in this example as average values of predetermined features which are extracted for several motor vehicles when passing or passing through the respective landmark 15 from their driving maneuvers.
  • the landmark 15 of FIG Turn off 27 act, for example, an intersection.
  • the motor vehicles have passed the landmark 15 in time from the motor vehicle 10. They are referred to below as better vehicles for better distinction. In particular, these may be selected vehicles or measuring vehicles whose position is known. From the foreign vehicles, a driving trajectory 28 can be received individually. In general, it can be provided that each other vehicle, when passing the landmark 15, determines extracted features 29 relative to the respective driving maneuvers performed when passing the landmark 15.
  • a curve radius K as an example of such an extracted feature 29 will be discussed further as an example.
  • FIG. 2 illustrates how one of the foreign vehicles can determine a radius value K1 as an extracted feature 29 of the curve radius K.
  • Fig. 3 illustrates how the turning radius K (as an example of an extracted feature 29 in general) is plotted by plotting all radius values of the other vehicles for the landmark 15, i. here the turn-off option 27, as a histogram gives a frequency distribution H, in which the radius value K1 represents a possible value.
  • a characteristic feature 30 can then be determined or determined, for example, as the most frequent value of the feature 29, in this case as an example of the curve radius K.
  • the set of characteristic features 30 thus determined then gives the maneuver characteristic of the landmark 15.
  • Fig. 4 illustrates how lanes can be distinguished by characteristic features lane.
  • Fig. 4 illustrates similar to Fig. 2 a turning possibility 31, but in which two lanes 32 can be traveled.
  • Each lane 32 represents its own landmark.
  • FIG. 4 and FIG. 5 together illustrate how from extracted features 29 of the travel trajectories 28 of all other vehicles a frequency distribution H can be determined, which for an extracted feature 29, for example the curve radius K, can have two local maxima 33, which is an indication of a two-lane road is.
  • the local maxima 33 can again be used as the respective characteristic feature 30 for the two landmarks 15 of the turn-off facility 31.
  • the accuracy of navigation data or trajectory data of driving trajectories can first be broken down into global and local.
  • the goal of global navigation is a rough location to a few meters accurate. However, this accuracy is insufficient to reliably assign vehicles to lanes.
  • an assignment of a lane-precise position to a lane is possible, but reference points, so-called landmarks 15, must be present for this purpose.
  • the generation and recognition of landmarks is so far only possible optically using a camera system or LIDAR.
  • a sensor or generally detection device installed as standard in motor vehicles (i.e., a GNSS position signal receiver plus at least a wheel speed sensor, acceleration sensor, yaw rate sensor, and / or speed measurement). If the result of a fusion of these sensors is considered, such unique landmarks 15 can be recognized.
  • the aim is to identify recurring driving maneuvers in the road network and to create a maneuver characteristic over a large number of passages.
  • turning maneuvers and curves are suitable with at least one predetermined minimum curvature.
  • the merger of the detection devices results in a driving trajectory with a lower variance.
  • the fusion can e.g. based on a motion model of a motor vehicle (e.g., a one-track model).
  • the curvature of the lane can be estimated in particular from the consideration of the wheel speeds and the yaw rate. It should be noted that not every driver drives through a selected curve in the middle of the lane, but chooses his own driving line. Because of this, the curvature actually driven is subject to a frequency distribution over several passes (FIGS. 3 and 5). By this distribution, the curvature characteristic of the curve can be normalized as a characteristic feature. Thus, e.g. for every curve also an averaged suspension point at the curve entrance and corner exit.
  • the turn maneuvers are separated on the basis of various features, such as the calculated curve radius, the entry direction and the exit direction, and are individually classified into a curve characteristic.
  • a multi-lane turnabout 31 can be in this way different maneuver characteristics and thus also generate separate suspension points (landmarks) for each turn lane.
  • Fig. 2 and Fig. 4 show the generation of the landmark 15 for a right turn.
  • the relevant driving trajectories were previously determined via the entry and exit directions in and out of the intersection.
  • a mean curve radius can be determined.
  • multi-lane right turn Fig. 4
  • the heading for a lane-exact map is no longer sufficient.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Navigation (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé de détermination de la position d'un véhicule automobile (10) dans un environnement. On délivre à au moins un repère (15) de l'environnement une information de position respective (16) du repère (15) et on détecte que le véhicule automobile (10) franchit actuellement l'au moins un repère (15), et on délivre l'information de position (16) du repère (15) actuellement franchi comme position du véhicule automobile (10). Selon l'invention, on délivre une caractéristique d'une manœuvre résultant du franchissement du repère respectif (15). La caractéristique de manœuvre respective indique des caractéristiques prédéterminées (30) de la manœuvre de conduite respective, puis à l'aide d'au moins un moyen de détection du véhicule automobile (10), on génère des données de description d'une manœuvre de conduite respective effectuée actuellement par le véhicule automobile (10) et on extrait, à partir des données descriptives, des caractéristiques prédéterminées (20) de la manœuvre de conduite et on détecter le repère actuellement franchi (15) en comparant les caractéristiques extraites (20) avec les caractéristiques (30) de la caractéristique de manœuvre respective de l'au moins un repère (15).
PCT/EP2018/074490 2017-09-14 2018-09-11 Procédé de détermination de la position d'un véhicule automobile dans une environnement et dispositif de commande d'un véhicule automobile et moyen informatique pour fonctionner sur un réseau de données WO2019053013A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201880051101.8A CN111051817B (zh) 2017-09-14 2018-09-11 用于求取机动车的位置的方法、控制设备和计算装置

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102017216238.4 2017-09-14
DE102017216238.4A DE102017216238A1 (de) 2017-09-14 2017-09-14 Verfahren zum Ermitteln einer Position eines Kraftfahrzeugs in einer Umgebung sowie Steuervorrichtung für ein Kraftfahrzeug und Recheneinrichtung zum Betreiben an einem Datennetzwerk

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WO2019053013A1 true WO2019053013A1 (fr) 2019-03-21

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