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EP4562369A1 - Création d'un réseau de voies de passage logique dans des espaces de stationnement - Google Patents

Création d'un réseau de voies de passage logique dans des espaces de stationnement

Info

Publication number
EP4562369A1
EP4562369A1 EP23738463.1A EP23738463A EP4562369A1 EP 4562369 A1 EP4562369 A1 EP 4562369A1 EP 23738463 A EP23738463 A EP 23738463A EP 4562369 A1 EP4562369 A1 EP 4562369A1
Authority
EP
European Patent Office
Prior art keywords
nodes
node
determining
centroids
waypoint
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
EP23738463.1A
Other languages
German (de)
English (en)
Inventor
Sebastian BUSSE
Daniel LAUBRICH
Jonas Jung
Stefan Wappler
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.)
Volkswagen AG
Original Assignee
Volkswagen AG
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 Volkswagen AG filed Critical Volkswagen AG
Publication of EP4562369A1 publication Critical patent/EP4562369A1/fr
Pending legal-status Critical Current

Links

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
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G01C21/3833—Creation or updating of map data characterised by the source of data
    • G01C21/3841—Data obtained from two or more sources, e.g. probe vehicles
    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/06—Automatic manoeuvring for parking
    • 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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • 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
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G01C21/3807—Creation or updating of map data characterised by the type of data
    • G01C21/3815—Road data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096791—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is another vehicle
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
    • G08G1/096805—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route
    • G08G1/096827—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route where the route is computed onboard
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Definitions

  • the present invention relates to the creation of digital maps for parking spaces from sensor data from motor vehicles.
  • US 10 012 991 B1 discloses a method and a device for controlling the operation of an autonomous vehicle.
  • the autonomous vehicle can track the trajectories of other vehicles on a road. Based on the trajectories of the other vehicles, the autonomous vehicle can generate a pool of combined trajectories. The autonomous vehicle can then select one of the combined trajectories as a representative trajectory. The representative trajectory can be used to change the speed or direction of the autonomous vehicle.
  • US 2020 0217458 A1 discloses a device and a method that provide trajectory data for a geographical area.
  • Multiple probe data sets are received or identified by one or more mobile devices.
  • the probe data sets include time values in a first sequence that are associated with location values in the first sequence.
  • At least one of the probing data sets is modified to reverse the location values so that the modified probing data set includes time values in a first sequence that are associated with location values in a second sequence.
  • a location clustering algorithm is performed based on the plurality of test data sets and the modified test data set based on the location values.
  • the method for determining a route network for a motor vehicle has the provision of at least two trajectories.
  • Each trajectory is indicative of a path traveled by a motor vehicle within the surroundings of the motor vehicle.
  • the trajectory has waypoints.
  • Each waypoint of the trajectory has a dedicated point of the motor vehicle along the route in the area at a corresponding point in time.
  • the surroundings point preferably a park environment.
  • the method further comprises: determining corresponding trajectory sections from the trajectories. Determining waypoint centroids of corresponding waypoints of corresponding trajectory sections.
  • Each waypoint centroid has a position and orientation within the environment. Determine centroid clusters based on the identified waypoint centroids. Determining nodes from the focal clusters.
  • the method comprises determining a route network in the environment based on the remaining nodes, the node centroids and the node connections.
  • a trajectory according to the present invention is a path, a path or a route that a motor vehicle travels.
  • a trajectory can be a movement path of an object, represented by the temporal sequence of coordinates during runtime.
  • control module is set up to: determine corresponding trajectory sections from the trajectories; to determine waypoint centroids of corresponding waypoints of corresponding trajectory sections, each waypoint centroid having a position and orientation within the environment; Determine centroid clusters based on the identified waypoint centroids; to identify nodes from the focus clusters; identify additional waypoint centroids based on essentially parallel oriented centroid clusters; to determine node connections based on the other waypoint centroids and the nodes, such that a node is linearly connected to its nearest node neighbors; to check whether nodes exist where it is possible to reach neighboring nodes via more than one node connection; and if such nodes exist: to determine the node centroids of those nodes that can reach each other via more than one node connection; Replace all nodal connections between each affected node and the respective connected unaffected nodes with nodal connections between the corresponding nodal centroid and the unaffected connected nodes; and delete the affected nodes. Furthermore, the control module is set up to determine a network
  • the determination of further waypoint centroids is additionally based on at least one node point of at least one of the essentially parallel oriented centroid clusters.
  • determining node connections based on the further waypoint centroids and the node points involves replacing the node points that were used to determine the further waypoint centroids with the corresponding further waypoint centroids.
  • the focus clusters are indicative of sections of a path network in the area.
  • the nodes are indicative of starting points, end points and crossing points of the network of paths in the area.
  • a focal cluster only has two nodes. This has the advantage that the path network can be optimized or created more precisely.
  • the method further comprises dividing the determined road network in the area into a first level, which is indicative of a road network in the area, and into a second level, which is indicative of lanes in the area.
  • the motor vehicle to which the road network is provided can also be another motor vehicle.
  • Various aspects of the present disclosure relate to an apparatus comprising one or more processors and one or more storage devices configured to perform the previously presented method.
  • Various aspects of the present disclosure relate to a program having program code for performing the previously presented method when the program code a computer, a processor, a control module or a programmable hardware component.
  • Various aspects of the present disclosure relate to a computer-implemented method for controlling one or more settings of a vehicle, as well as a corresponding device and a corresponding computer program.
  • the method, the device and the computer program can be used to adjust (that is, change) the one or more settings based on the following criteria.
  • Not all vehicle settings are equally suitable for this concept.
  • the present concept is suitable for one or more settings that can be changed by the one or more occupants of the vehicle via a user interface (of the vehicle), that is, settings that can also be changed by the users themselves without this Changes to the vehicle's hardware or software would be necessary.
  • a possible limitation to such settings also has the advantage that the actions of the occupants, i.e.
  • the method may include recording occupants changing settings, along with corresponding environmental indicators.
  • the one or more settings of the vehicle may relate to functions of an interior of the vehicle, such as comfort functions of the vehicle. These can be, for example, one or more settings of the vehicle, at least one of an air conditioning setting of the vehicle, a seat setting of the vehicle, a lighting setting of the vehicle, a windshield wiper setting of the vehicle. But the settings can also include a vehicle suspension setting, such as switching between a comfort and a sport suspension setting. Other settings are also conceivable.
  • FIG. 1 shows a flowchart of an example of a method according to the invention for determining a route network for a motor vehicle
  • FIG. 2 shows a flowchart of a further example according to the invention of a method for determining a route network for a motor vehicle
  • 3 shows a flowchart of a further example of a method according to the invention for determining a route network for a motor vehicle
  • FIG. 4 shows a block diagram of a further example according to the invention of a device for determining a route network for a motor vehicle
  • FIG. 5 shows a graph of an example according to the invention for determining a route network for a motor vehicle
  • 6 to 6c show further graphs of an example according to the invention for determining a route network for a motor vehicle
  • FIG. 7 to 7a show further graphs of an example according to the invention for determining a route network for a motor vehicle
  • 9 to 9a show further graphs of an example according to the invention for determining a route network for a motor vehicle.
  • Fig. 1 shows a flowchart of an example of a method according to the invention for determining a route network for a motor vehicle.
  • the method for determining a route network for a motor vehicle 100 preferably an autonomously driving motor vehicle, has the provision 10 of at least two trajectories 110.
  • Each trajectory 110 is indicative of a path traveled by a motor vehicle 100 within an environment 200 of the motor vehicle 100.
  • the trajectory 110 has waypoints 111 - also called waypoints.
  • Each waypoint 111 of the trajectory 110 has a dedicated point of the motor vehicle 100 along the route in the environment 200 at a corresponding point in time. And the environment 200 preferably has a parking environment.
  • the method further comprises: determining 20 corresponding trajectory sections 112 from the trajectories 110; Determining 30 waypoint centroids 113 - also called WaypointCentroids - corresponding waypoints 111 of corresponding trajectory sections 112, each waypoint centroid 113 having a position and orientation within the environment 200; Determine 40 centroid clusters 114 - also called Centroid Cluster - based on the determined waypoint centroids 113; Determine 50 nodes 115 - also called intersection clusters - from the focus clusters 114; Determine 60 further waypoint centroids 116, based on essentially parallel oriented centroid clusters 114; Determining 70 node connections 117, based on the further waypoint centroids 116 and the nodes 115, such that a node 115 is linearly connected to its nearest node neighbors; Check 80 whether no
  • the method comprises determining 90 a path network 210 of the environment 200, based on the remaining nodes 115, the node centroids 118 and the node connections 117.
  • Fig. 2 shows a flowchart of a further example according to the invention of a method for determining a route network for a motor vehicle.
  • 1 furthermore has that the determination 70 of node connections 117, based on the further waypoint centroids 116 and the nodes 115, involves replacing 72 of the nodes 115, which were used to determine 60 the further waypoint centroids 116, by the corresponding further waypoint focal points 116.
  • FIG 3 shows a flowchart of a further example according to the invention of a method for determining a route network for a motor vehicle.
  • the method shown in FIG is indicative, for lanes around 200.
  • Fig. 4 shows a block diagram of a further example according to the invention of a device for determining a route network for a motor vehicle.
  • the device 300 for determining a road network 210 for a motor vehicle 200 preferably an autonomously driving motor vehicle, has: an interface 310, which is set up to communicate control information with at least one specific vehicle component 110 of a motor vehicle 100; and a control module 320 configured to: provide at least two trajectories 110, each trajectory 110 being indicative of a path traveled by a motor vehicle 100 within an environment 200 of the motor vehicle 100; the trajectory 110 has waypoints 111; each waypoint 111 of the trajectory 110 has a dedicated point of the motor vehicle 100 along the route in the environment 200 at a corresponding time; and the environment 200 preferably includes a parking environment.
  • control module 320 is set up to determine corresponding trajectory sections 112 from the trajectories 110; to determine waypoint centroids 113 of corresponding waypoints of corresponding trajectory sections 112, each waypoint centroid 113 having a position and orientation within the environment 200; to determine centroid clusters 114 based on the determined waypoint centroids 113; to determine nodes 115 from the focus clusters 114; to determine further waypoint centroids 116 based on essentially parallel oriented centroid clusters 114;
  • the control module 320 is there for this set up to determine a path network 210 of the environment 200, based on the remaining nodes 150, the node centers 118 and the node connections 117.
  • Fig. 5 shows a graph of an example according to the invention for determining a route network for a motor vehicle.
  • the road network consists of several objects that are related to one another, and through these relationships a vehicle 100 can plan a route through the map.
  • the road network consists of three layers, two logical graphs and the geometric information.
  • the two graphs are the links/linkjnintersections and the lanes/lane_intersections, where the connections represent the higher logical level.
  • the geometric information is represented by the shaping lines (shapepointjines).
  • the relationships between these layers are as follows (according to MTC Json format):
  • each connection is connected to 1-N lanes - each lane is connected to exactly one link and one shapepoint jine
  • each shapepoint jine is linked to exactly one lane
  • connection/lane has exactly two intersections, one at the beginning and one at the end
  • each intersection is connected to 1-N connections/tracks and can be the start or end of several connections/tracks.
  • FIGS. 6 to 6c show further graphs of an example according to the invention for determining a path network 210 for a motor vehicle 100.
  • the graphs are created by merging and clustering poses of one or more tracks.
  • the original trajectories 110 and the result of the clustering are shown in Figures 6 to 7a.
  • Each centroid cluster 114 - also called CentroidCluster - consists of a path of waypoint centroids 113 - also called WaypointCentroids.
  • each CentroidCluster is represented by a different line style. They show the geometric information of the merged trajectories 110 and function as shapepoint lines “shapepointjines” of the road networks. The lanes represent the shapepointjines in a logical graph and only contain the start and end points of each Centroid Cluster.
  • FIG. 6 A node structure consisting of nodes 115 - also called intersection cluster - is formed.
  • the node structure - also called the IntersectionCluster structure - can be added, which also contains WaypointCentroids 113.
  • the initial intersection clusters 115 are the start and end points of the Centroid Cluster 114.
  • intersection clusters 115 are now expanded to contain further WaypointCentroids 113, which are close enough to the original center of gravity - also called centroid - and have a parallel orientation, thus in the same or the opposite direction. If different intersection clusters 115 overlap, they can be merged into a single cluster. All WaypointCentroids 113 can also know whether they are part of an intersection cluster 115.
  • intersection connections 117 - also called IntersectionClusterConnection - can be added.
  • These objects may contain references to the CentroidClusters 114 involved. They may also contain information about which WaypointCentroid 113 from which cluster is in the connection, references to two intersection clusters 115 and a flag indicating whether the connection is bidirectional.
  • each CentroidCluster 114 begins and ends with an Intersection Cluster 115.
  • an Intersection Cluster 115 When iterating over the centroids of a cluster, one “walks” from one Intersection Cluster 115 to another and the Intersection Cluster Connection objects 117 are filled iteratively. Only the waypoint centroids 113 that are part of an intersection cluster 115 are processed. This is shown by “circles” in Fig. 6b.
  • Fig 6c In the last step, the road network for the intersections can be simplified. 6 shows that the node centroids 118 - also called T-junctions - have three Intersection Clusters 115, all of which are connected to one another. This can be simplified so that there is only one intersection cluster 115 in the middle of the intersection that has connections to the different directions a vehicle can take. Since the T-junction 118 in FIG. 6c is a very simple one, another example is shown in FIG. 7 as an intersection.
  • FIG. 7 to 7a show further graphs of an example according to the invention for determining a route network for a motor vehicle.
  • intersection clusters 115 An intersection with four intersection clusters 115 is shown, all of which are connected to one another. As in the example of FIG. 6c, there should only be one intersection cluster 115 with four connections to the different directions.
  • the nodes and edges of the intersection form a maximal cluster structure that should be shrunk to a node.
  • finding maximal cluster structures in a graph is an NP-complete problem, so it is not possible to shrink the entire cluster structure at once in polynomial time.
  • IntersectionClusterConnection 117 to be collapsed is part of one or more triangles, there are also connections that should be merged.
  • the newly created Intersectioncluster 115 representing the shrunken IntersectionClusterConnection 117 should receive references to the CentroidClusters 114 previously stored by the connection. Even if this intersection cluster 115 is later merged with another when another connection is shrunk, care should be taken to ensure that this information is still available.
  • shrinking each connection that belongs to a triangle care should be taken to adjust each reference in the entire ClusterGraph accordingly to obtain the desired result.
  • the last thing that should now be done is to convert the Intersectionclusters 115 and IntersectionClusterConnections 117 with the included WaypointCentroids 113 and CentroidClusters 114 into links, lanes and shapepoint lines, thus into a path network.
  • the first and easiest part is creating the connections and their intersections.
  • the intersection clusters 115 can be translated directly into link intersections.
  • a link can be created between these link intersections if there is an IntersectionClusterConnection 117 between two of these intersection clusters 115.
  • the CentroidClusters 114 stored within the IntersectionClusterConnections 117 and in the Intersectionclusters 115 should be taken into account (since some of the connections have been reduced to Intersectionclusters 115 and the corresponding CentroidClusters 114 have been referenced therein). It should be noted that an IntersectionCluster(Connection) 114 or 117 can only store part of the WaypointCentroids 113 of a CentroidCluster 114.
  • a CentroidCluster 114 can be divided into different Shapepoint Lines/Lanes.
  • WaypointCentroids 113 of a CentroidCluster 114 to which an intersection cluster 115 or a connection refers, a lane intersection can be created from its first and last WaypointCentroids 113 and the respective lane or connection in between.
  • a shapepoint line can also be created from these WaypointCentroids 113 and relationships can be added between these newly created lanes and shapepoint lines.
  • the relationships between the links and the lanes are created.
  • a created lane was constructed from the WaypointCentroids 113 referenced by an IntersectionClusterConnection 117, a relationship can easily be added to the connection created from the same object. If the lane was created from the WaypointCentroids 113 referenced by an intersection cluster 115, a lane is created within an intersection that describes the transition from one link to another.
  • the first WaypointCentroid, 113 which belongs to the CentroidCluster 114 and is referenced by the Intersectioncluster 115, can be examined. If this first WaypointCentroid 113 also points to an IntersectionClusterConnection 117, a relationship can be added between the created lane and the connection created by this IntersectionClusterConnection 117. If the first WaypointCentroid 113 does not reference an IntersectionClusterConnection 117, it can be checked to see if it has a predecessor that can be checked for a reference to an IntersectionClusterConnections 117. This continues until a (perhaps previous) predecessor is found that points to an IntersectionClusterConnection 117 to establish the relationship as described previously.
  • a “neighborhood diamond” means that two centroids have both a common predecessor and a common successor.
  • the diamond can be removed by merging the two centers of gravity - also called centroids - into one.
  • a centroid z has a common successor m with one of its successors n.
  • An edge should be removed.
  • l.d.R. has n other predecessors or successors besides z and m. If there are other predecessors, remove the edge between z and n. If there are other successors, remove the edge between n and m.
  • centroid has another centroid as both a predecessor and a successor.
  • 9 to 9a show further graphs of an example according to the invention for determining a route network for a motor vehicle. They show further configurations that can further optimize the process.
  • Fig. 9 If there are IntersectionClusterConnections 117 that are very short (e.g. ⁇ 10m) and lead to (or come from) a "dead end", it is very likely that you have driven into a parking lot. These connections should not be present in the ClusterGraph and the Roadnet objects obtained from it. To do this, the length of these connections can be calculated, and if this length is below a certain threshold, this connection is deleted. This can leave redundant intersection clusters 115 that do not provide any additional information for the graph, as shown in the middle representations of FIG. 9. Therefore, they can be removed and their neighboring connections merged. This merging of the neighboring connections also includes merging the CentroidClusters 114.
  • intersection cluster 115 can be considered redundant if the following conditions are met:
  • a connection describes a section of road between two connecting intersections. Every connection has a starting point and an ending point. The start and end points are not presented as independent features. They always belong to the connection intersection at the beginning or end of the connection. The direction from the start point to the end point is the forward direction.
  • a connection can be unidirectional or bidirectional and describes the permitted traffic flow on the road section.
  • a connector intersection represents a street intersection where two or more streets meet or intersect. An intersection is also used to model the end point of a dead end. Two streets that intersect but are not topologically connected do not form an intersection. An intersection is represented by exactly one point in the road network.
  • a lane describes a section of lane between two lane intersections. Each lane has a start and an end point. The start and end points are not presented as independent features. They always belong to the lane intersection at the beginning or end of the lane. Lanes are always unidirectional and describe a logical path from the start to the end point. Lane intersections
  • a lane intersection represents a lane intersection where two or more lanes meet or separate.
  • a lane intersection is also used to model the end point of a dead end. Two lanes that intersect but are not topologically connected do not form an intersection. An intersection is represented by exactly one point in the lane network.
  • a shape dot line is the geometric representation of a lane that describes the commonly used center line of the lane. It is represented by a polyline.
  • a waypoint 111 represents a single position of a single trajectory 110 and is the smallest increment of information. The trajectory 110, to which the waypoint 111 belongs, is determined via the track ID. This information can be used optionally.
  • each waypoint 111 contains the information as to which other waypoint 111 is its predecessor and which is its successor in the trajectory 110. After the WaypointCentroids 113 have been calculated, each waypoint 111 also knows which centroid, i.e. center of gravity, it is assigned to.
  • the WaypointCentroid 113 is the result of grouping waypoints 111 of different trajectories 110 and has a pose (position and orientation) that depends on the waypoints 111 assigned to it. It contains references to these waypoints 111 and the IDs of their trajectories 110.
  • a centroid can be part of several Centroid Clusters 114 and thus have several predecessor or successor centroids. Each centroid may also contain information about which Intersection Cluster 115 or Intersection Cluster Connections 117 it belongs to.
  • a CentroidCluster 114 is a polyline of WaypointCentroids 113.
  • intersection clusters 115 are the start, intersection and end points of the road network (link intersections). Each intersection cluster 115 consists of at least one waypoint centroid 113, but can also have several centroids, especially in the case of intersections. Their position, and thus the position of their waypoints 113, determines the position of the intersection cluster 115. However, since the intersection cluster 115 is part of a logical graph, its position is only important for visualization. An intersection cluster 115 has at least one neighboring IntersectionClusterConnection 117 to connect it to another intersection cluster 115.
  • an intersection cluster 115 may have information about CentroidClusters 114 and their centroids if this intersection cluster 115 was created during the reduction of an IntersectionClusterConnection 117.
  • the connection information for its Centroid Clusters 114 and centroids are written to the new Intersection Cluster 115.
  • An IntersectonClusterConnection 117 is a connection between two IntersectionClusters. 115. It also represents a connection between intersections in the road network (link). Such a connection can be unidirectional or bidirectional, which is determined by the bidirectional member flag. If the connection is unidirectional, it points from the first to the second intersection cluster 115.
  • the clusters member contains references to all CentroidClusters 114 that are at least partially represented by the connection, and the centra ids_of_cl usters member contains references to the specific WaypointCentroids 113 of these clusters, that are represented by the connection.
  • An IntersectionClusterConnection 117 often only references a portion of the WaypointCentroids 113 of a CentroidCluster 114.
  • the ClusterGraph is the main object that contains all relevant objects for creating road networks.
  • the idea of the invention is to use the movement data from several trips to identify points at which “certain poses accumulate”. First, it ensures that all clusters are unidirectional. Furthermore, intersections and parallel trajectory sections are selected in the resulting trajectory clusters. On this basis, a graph structure with its nodes and edges is generated, which represents the logical path network. This has two levels: the street and its lanes.
  • Examples may further include or relate to a (computer) program with program code for executing one or more of the above methods when the program is executed on a computer, a processor or other programmable hardware component. Steps, operations or processes of various of the methods described above can also be carried out by programmed computers, processors or other programmable hardware components. Examples may also include program storage devices, e.g. B. digital data storage media, which are machine-, processor- or computer-readable and encode or contain machine-executable, processor-executable or computer-executable programs and instructions.
  • program storage devices e.g. B. digital data storage media, which are machine-, processor- or computer-readable and encode or contain machine-executable, processor-executable or computer-executable programs and instructions.
  • FPLAs (Field) Programmable Logic Arrays)
  • GPU Graphics Processor Unit

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Abstract

L'invention concerne un procédé et dispositif de détermination d'un réseau de voies de passage pour un véhicule automobile, comprenant : - fournir au moins deux trajets indiquant respectivement un chemin parcouru par un véhicule dans son environnement; et présentant des points de passage, chaque point de passage présentant un point décidé du véhicule le long du trajet dans l'environnement à un instant correspondant, et : déterminer des segments de trajet correspondants à partir des trajets; déterminer des points de passage correspondant à des centres géométriques de segments de trajets correspondants, chaque centre géométrique présentant une position et une orientation à l'intérieur de l'environnement; déterminer des groupes de centres géométriques sur la base des centres géométriques déterminés; déterminer des points nodaux à partir des groupes de centres géométriques; déterminer d'autres centres géométriques sur la base des groupes de centres géométriques d'orientation sensiblement parallèle; déterminer des liaisons par points nodaux sur la base des autres centres géométriques et des noeuds de manière à ce qu'un point nodal soit relié de manière linéaire à ses noeuds de proximité immédiate; vérifier s'il existe des points nodaux là où des points nodaux voisins sont joignables sur plus d'une liaison de points nodaux; puis déterminer des centres géométriques nodaux de ces points nodaux concernés; remplacer toutes les liaisons par points nodaux entre chaque point nodal concerné et les points nodaux non concernés liés; effacer les points nodaux concernés; et déterminer un réseau de voies de passage de l'environnement basé sur les points nodaux restants, les centres géométriques nodaux et les liaisons par points nodaux.
EP23738463.1A 2022-07-26 2023-06-30 Création d'un réseau de voies de passage logique dans des espaces de stationnement Pending EP4562369A1 (fr)

Applications Claiming Priority (2)

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DE102022207651.6A DE102022207651B4 (de) 2022-07-26 2022-07-26 Erstellen eines logischen Wegenetzes in Parkräumen
PCT/EP2023/068100 WO2024022740A1 (fr) 2022-07-26 2023-06-30 Création d'un réseau de voies de passage logique dans des espaces de stationnement

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EP4562369A1 true EP4562369A1 (fr) 2025-06-04

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EP (1) EP4562369A1 (fr)
DE (1) DE102022207651B4 (fr)
WO (1) WO2024022740A1 (fr)

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DE102024113097B4 (de) * 2024-05-10 2026-01-29 Cariad Se Verfahren zum Ermitteln einer Navigationskarte

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JP4609501B2 (ja) 2008-02-25 2011-01-12 ソニー株式会社 光源装置ならびに表示装置
US8825265B1 (en) 2012-03-16 2014-09-02 Google Inc. Approach for consolidating observed vehicle trajectories into a single representative trajectory
US10013508B2 (en) * 2014-10-07 2018-07-03 Toyota Motor Engineering & Manufacturing North America, Inc. Joint probabilistic modeling and inference of intersection structure
DE102017209346A1 (de) * 2017-06-01 2019-01-10 Robert Bosch Gmbh Verfahren und Vorrichtung zur Erstellung einer fahrspurgenauen Straßenkarte
US11745727B2 (en) * 2018-01-08 2023-09-05 STEER-Tech, LLC Methods and systems for mapping a parking area for autonomous parking
DK201970148A1 (en) * 2018-12-10 2020-07-06 Aptiv Tech Ltd Motion graph construction and lane level route planning
US11187542B2 (en) 2019-02-26 2021-11-30 Here Global B.V. Trajectory time reversal
US12179795B2 (en) * 2021-05-24 2024-12-31 Nvidia Corporation Using arrival times and safety procedures in motion planning trajectories for autonomous vehicles
DE102021116510B4 (de) * 2021-06-25 2024-05-08 Cariad Se Verfahren und Rechenvorrichtung zum Bereitstellen einer Wegnetzkarte eines Parkhauses

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DE102022207651A1 (de) 2024-02-01
DE102022207651B4 (de) 2024-10-17

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