Method of Determining a Point of Interest and/or a Road Type in a Map, and Related Cloud Server and Vehicle

Patent metadata

Field	Value
Publication number	US-2023236030-A1
Application number	US-202318157446-A
Country	US
Kind code	A1
Filing date	Jan 20, 2023
Priority date	Jan 27, 2022
Publication date	Jul 27, 2023
Grant date	—

Abstract

Provided is a computer-implemented method of determining a point of interest and/or a road type in a map, comprising the steps of: acquiring processed sensor data collected from one or more vehicles; extracting from the processed sensor data a set of classification parameters; and determining based on the set of classification parameters one or more points of interest (POI) and its geographic location and/or one or more road types.

First claim

1 . A computer-implemented method comprising:
acquiring processed sensor data collected from one or more vehicles; extracting from the processed sensor data a set of classification parameters; and determining based on the set of classification parameters:
one or more points of interest (POI) and its geographic location; and/or
one or more road types.

2 . The method of claim 1 , wherein the determining is performed by using a trained neural network classifier that uses the set of classification parameters as input and outputs the at least one POI and/or road type as a classification result.

3 . The method of claim 2 , wherein the trained neural network classifier is a trained convolution neural network classifier.

4 . The method of claim 1 , further comprising:
detecting and tracking a plurality of objects based on sensor-based data and localization data to determine a plurality of individual trails for each of a plurality of object classes.

5 . The method of claim 4 , further including:
aggregating each of the individual trails to determine a plurality of object class specific aggregated trails in a grid cell map representation of a map.

6 . The method of claim 5 , wherein the determining of the one or more POI and/or at least one road type in the map is based on the object class specific aggregated trails.

7 . The method of claim 6 , wherein object class specific histograms are determined for each grid cell of the map using the object class specific aggregated trails.

8 . The method of claim 7 , wherein the histograms are determined with regard to at least one of:
a plurality of different driving directions; or a plurality of different walking directions.

9 . The method of claim 7 , wherein the histograms include at least one of:
an average observed speed over ground; or an average angle deviation of trails.

10 . The method of claim 7 , wherein the histograms include a creation time of each individual trail.

11 . The method of claim 5 , further comprising:
generating the map using the object class specific aggregated trails and the determined one or more POI and/or road type.

12 . The method of claim 11 , wherein the map is generated by using only aggregated trails that have been at least one of:
aggregated by using a minimum number of individual trails; or aggregated by using a minimum number of trails determined within a specific amount of time in the past.

13 . The method of claim 11 , wherein the map is generated by at least one of:
providing a reliability indication for the object class specific aggregated trails; or providing a reliability indication for the one or more POI and/or road type.

14 . The method of claim 1 , wherein the processed sensor data are radar-based sensor data and GPS-based sensor data.

15 . The method of claim 1 , wherein the processed sensor data are LiDAR-based sensor data and GPS-based sensor data.

16 . An apparatus adapted to:
acquire processed sensor data collected from one or more vehicles; extract from the processed sensor data a set of classification parameters; and determine based on the set of classification parameters:
one or more points of interest (POI) and its geographic location; and/or
one or more road types.

17 . (canceled)

18 . A system comprising:
a cloud server; and a plurality of vehicles, the cloud server adapted to:
acquire processed sensor data collected from one or more vehicles of the plurality of vehicles;
extract from the processed sensor data a set of classification parameters; and
determine based on the set of classification parameters:
one or more points of interest (POI) and its geographic location; and/or
one or more road types; and

the one or more vehicles of the plurality of vehicles comprising:
a communication interface configured to receive a map including at least one of determined POIs or determined road types; and
a control unit configured to make advanced driving and safety decisions based on the received map.

19 . The apparatus of claim 16 , wherein the apparatus comprises a cloud server.

Assignees

• Aptiv Tech Ltd

Inventors

• Nunn Christian
• Mueller Dennis
• Roese-Koerner Lutz
• Colling Pascal

Classifications

• H04L67/12 Primary

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title

• G06F16/29

  Geographical information databases · CPC title

• B60W50/0098

  Details of control systems ensuring comfort, safety or stability not otherwise provided for · CPC title

• G01C21/30

  Map- or contour-matching · CPC title

• G01C21/32

  Structuring or formatting of map data · CPC title