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EP-4735831-A1 - SYSTEMS AND METHODS FOR UPDATING ROAD GEOMETRY GRAPHS

EP4735831A1EP 4735831 A1EP4735831 A1EP 4735831A1EP-4735831-A1

Abstract

There is provided systems (400) and methods (490) for updating road geometry graphs. In particular, there is provided a method of of updating an initial road geometry graph (280) for a geographical area (100), the method comprising: obtaining an image (301) of new road geometry of the geographical area (100); converting the image (301) of new road geometry of the geographical area (100) to an updated road geometry graph (290) of the geographical area (100), wherein said converting comprises: applying a first trained machine learning model (321) to the image (301) of new road geometry to obtain a set of nodes (325) of the updated road geometry graph (290), and applying a second trained machine learning model (331) to the image (301) of new road geometry and the obtained set of nodes (325) to obtain a set of edges (335) of the updated road geometry graph (290); merging the updated road geometry graph (290) with the initial stored geometry graph (280).

Inventors

  • RATHOD, Sagar Gangakisan

Assignees

  • TomTom Global Content B.V.

Dates

Publication Date
20260506
Application Date
20240524

Claims (16)

  1. 1. A method of updating an initial road geometry graph for a geographical area, the method comprising: obtaining an image of new road geometry of the geographical area; converting the image of new road geometry of the geographical area to an updated road geometry graph of the geographical area, wherein said converting comprises: applying a first trained machine learning model to the image of new road geometry to obtain a set of nodes of the updated road geometry graph, and applying a second trained machine learning model to the image of new road geometry and the obtained set of nodes to obtain a set of edges of the updated road geometry graph; merging the updated road geometry graph with the initial stored geometry graph.
  2. 2. The method of claim 1 wherein the image of new road geometry of the geographical area is obtained based on the initial stored road geometry graph for the geographical area and one or more satellite images of an actual road geometry for the geographic area.
  3. 3. The method of claim 1 or 2 wherein applying a second trained machine learning model to the image of new road geometry and the obtained set of nodes comprises iteratively applying the second trained machine learning model to each node in the set of nodes to obtain a respective sub-set of nodes which are directly connected to said node.
  4. 4. The method of any proceeding wherein said merging comprises applying a third machine learning algorithm to identify correspondences between one or more roads in the updated road geometry graph and one or more respective roads in the initial stored geometry graph.
  5. 5. The method of claim 4 wherein said merging comprises updating the attributes of one or more roads in the updated road geometry based on the attributes of the one or more identified corresponding roads in the initial stored geometry.
  6. 6. The method of any preceding claim wherein one or more nodes of the set of nodes for the updated road geometry graph correspond to an intersection between a new road in the image and an existing road in the initial stored road geometry graph and/or to an intersection between at least two roads in the image.
  7. 7. The method of any preceding claim wherein the step of obtaining comprises: applying an image of road geometry corrections to an image of the initial stored road geometry generated from the initial road geometry graph, wherein the image of road geometry corrections identifies one or more differences between the actual road geometry of the geographical area and the initial stored road geometry.
  8. 8. The method of claim 7 wherein applying comprises deleting one or more roads from the initial stored road geometry based on an overlap with one or more road deletions in the image of road geometry corrections.
  9. 9. A method of training a first and second machine learning model for converting an image of predicted road geometry to a road geometry graph, the method comprising: obtaining a set of existing road geometry graphs, wherein each road geometry graph comprises a set of initial nodes and a set of edges; for each existing road geometry graph in the set of existing road geometry graphs: generating an image of the existing road geometry from the existing road geometry graph; determining, based on the initial set of nodes, a respective set of training nodes, training a first machine learning model according to a first training set comprising the set of images of existing road geometry graphs labelled with the respective sets of training nodes such that the first machine learning model is arranged to take as input an image of predicted road geometry and generate as output a corresponding set of nodes; training a second machine learning model according to a second training set comprising a set of query nodes and corresponding images of existing road geometry graphs from the set of road geometry graphs, wherein each query node is labelled with the road geometry graph edges connected to the query node such that the second machine learning model is arranged to take as input a composite image comprising an image of predicted road geometry, a corresponding set of nodes and a query node, and generate as output an indication of the edges connected to the query node according to the predicted road geometry.
  10. 10. The method of claim 10 wherein the respective set of training nodes are an enlarged set of nodes for the existing road geometry graph generated by interpolating additional nodes on edges in the set of edges of the existing road geometry graph.
  11. 11. The method of claim 9 or 10 wherein the first machine learning model and the second machine learning model form a convolutional encoder-decoder neural network.
  12. 12. The method of any of claims 9-11 wherein the set of nodes comprise road intersections and road shape points.
  13. 13. The method of claim 12 wherein the additional nodes are additional road shape points.
  14. 14. The method of any of claims 9-13 wherein the first machine learning model and/or the second machine learning model comprise any of: a Linet segmentation model; a UnetPlusPlus segmentation model, a DeepLab model, a Segformer model, a SwinTransformer model; and/or any of: a VGG19 encoder; a Resnet50 encoder; an Efficient B1-B5 encoder, a DenseNet encoder.
  15. 15. An apparatus arranged to carry out a method according to any one of claims 1 to 14.
  16. 16. A computer-readable medium storing a computer program which, when executed by a processor, causes the processor to carry out a method according to any one of claims 1 to 14.

Description

SYSTEMS AND METHODS FOR UPDATING ROAD GEOMETRY GRAPHS Field of the invention The present invention relates to systems and methods for correcting map data, in particular for updating stored road geometries to correct identified discrepancies. Background of the invention The adoption of electronic navigational aids has seen a large increase in recent years. Many road vehicles now integrate navigation and location services from the factory. Such services can offer accurate turn-by-turn navigation assistance, and increasingly specific guidance regarding lane changes and specific road layouts. The use of such navigational guidance systems has expanded beyond personal motor vehicles and such systems are now routinely used by users of many other modes of transport including commercial vehicles (such as large goods vehicles, commercial passenger transport vehicles). With the advances being made in the field of self-driving vehicles such navigation systems are a key technology in enabling autonomous point to point driving. Additionally, with the large number of personal smart devices integrating navigational systems and location technology, users are increasingly reliant on such services for navigation when using other forms of personal transport, such as pedal cycles and even pedestrian travel. In all of these cases accurate, detailed, digital mapping information is essential, in particular as the level of guidance offered by these systems becomes more granular. The availability of accurate road geometries and road layouts over large geographical areas is therefore desired. The consequences of an incorrect road geometry can lead to significant issues. For example, missing roads lead to individual journeys being sub- optimal and the most efficient route may not be used. On an aggregate level, as the incorrect road geometry will affect a large number of people in the same geographical area, this can lead to more widespread issues, such as unnecessary traffic or bottle necks. Similarly, incorrect road layouts can lead to confusion for vehicle users as they attempt to follow navigational guidance. As roadways and roads are often changed and revised even initially accurate mapping data may become inaccurate in short periods of time. Even, once discrepancies in existing mapping data have been identified applying corrections can be a timeconsuming process. Often significant manual processing is required in order to maintain the extensive metadata (such as road attributes) in the mapping data itself when road layouts are updated. Summary of the invention It is an object of the invention to provide systems and methods for updating electronic maps based on corrections to road geometries. In particular, it has been noticed, as discussed in more detail below, that improved methods are required for the conversion of raster type road geometry corrections to map updates suitable for applying directly to vector based maps, which typical store the road geometries (or layouts) in the form of connected graphs. To that end systems and methods are provided for updating an initial road geometry graph for a geographical area, by applying trained node identification and edge identification models to raster road correction data, to thereby update said graph. According to a first aspect of the invention, there is provided a method of updating an initial (or stored or otherwise existing) road geometry graph for a geographical area, the method comprising: obtaining (or receiving or otherwise accessing) an image of new (or predicted or otherwise updated) road geometry of the geographical area; converting the image of new road geometry of the geographical area to an updated road geometry graph of the geographical area; and merging the updated road geometry graph with the initial stored geometry graph, such as to create a further road geometry graph. Said converting comprises: applying a first trained machine learning model to the image of new road geometry to obtain a set of nodes of the updated road geometry graph, and applying a second trained machine learning model to the image of new road geometry and the obtained set of nodes to obtain a set of edges (or road segments) of the updated road geometry graph. Typically, the first machine learning model and the second machine learning model form a convolutional encoder-decoder neural network. Alternatively, the first and/or the second machine learning network may be a transformer type network. The image of new road geometry of the geographical area may be obtained based on the initial stored road geometry graph for the geographical area and one or more satellite images of an actual road geometry for the geographic area (such as by the method provided in the third aspect described shortly below). Applying the second trained machine learning model to the image of new road geometry and the obtained set of nodes may be iterative. In particular said applying may comprise iteratively applying th