Method for obtaining precision road maps

• US 6,510,382 B2
• Filed: 01/26/2001
• Issued: 01/21/2003
• Est. Priority Date: 01/26/2000
• Status: Expired due to Term

• Current Assignee: NorthStar Systems LLC (Alpha Alpha Intellectual Partners LLC)
• Original Assignee: Daimler Chrysler Company LLC (Stellantis NV)
• Inventors: Wilson, Christopher K. H.
• Primary Examiner(s): Cuchlinski, Jr.; William A.
• Assistant Examiner(s): MARC-COLEMAN, MARTHE Y
• Application Number:
  US09/769,762
• Publication Number:
  US 20020014979A1
• Time in Patent Office: 725 Days
• Field of Search: 701/200,201,202,208,214,210 340/988
• US Class Current: 701/208
• CPC Class Codes: G01C 21/3815 Road data
• CPC Class Codes: G01C 21/3867 Geometry of map features, e.g. shape points, polygons or for simplified maps
• CPC Class Codes: G09B 29/003 Maps
• CPC Class Codes: G09B 29/10 Map spot or coordinate position indicators; Map reading aids optical projection apparatus per se G03B

Abstract

A highly precise digital road map and method of producing same are provided for vehicle applications. The method obtains a conventional digital map having traditional segments and nodes, and represents a road network of the precision digital road map as a set of geometric shapes indexed to the traditional segments and nodes of the conventional digital map. Branch and merge points are identified on the road network by divergences and convergences from ideal paths of the vehicles traveling thereon. These ideal paths are obtained by statistically combining the actual paths of the vehicles traveling between the same points.

Claims

1. A method for producing a precision digital road map for vehicle applications, the method comprising the acts of:

obtaining a conventional digital map having traditional segments and nodes;

representing a road network of the precision digital road map as a set of geometric shapes indexed to the traditional segments and nodes;

wherein the act of representing comprises the act of identifying branch and merge points of the road network by divergences and convergences, respectively, from ideal paths of vehicles; and

wherein the ideals paths are obtained by statistically combining the actual paths of vehicles traveling between the same points.

2. The method according to claim 1, wherein the act of statistically combining uses a geometry refinement algorithm.

3. The method according to claim 1, wherein each identified branch and merge point is associated with a nearby one of the traditional nodes of the conventional digital map containing attribute information.

4. A method for producing a precision digital road map for vehicle applications, the method comprising the acts of:

obtaining a conventional digital map having traditional segments and nodes;

representing a road network of the precision digital road map as a set of geometric shapes indexed to the traditional segments and nodes;

wherein the act of representing comprises the act of identifying branch and merge points of the road network by divergences and convergences, respectively, from ideal paths of vehicles; and

wherein an ideal path is defined as a root of a group of drivers, all traveling between point one and point two, where point two represents a driver'"'"'s perceived destination while driving.

5. A precision digital road map, comprising:

a road network represented as a set of geometric shapes indexed to traditional segments and nodes of a conventional digital map;

wherein branch and merge points of the road network are identified by divergences and convergences, respectively, from ideal paths of vehicles, said ideal paths being statistical combinations of actual paths of vehicles traveling between the same points.

6. The precision digital road map according to claim 5, wherein each identified branch and merge point is associated with a nearby one of the traditional nodes of the conventional digital map containing attribute information.

7. The precision digital road map according to claim 5, wherein an ideal path is defined as a root of a group of drivers, all traveling between point one and point two, where point two represents a driver'"'"'s perceived destination while driving.

8. The precision digital road map according to claim 5, wherein the geometric shapes comprise road lanes traveled by the vehicles.

9. A method for producing a precision digital road map for vehicle applications, the method comprising the acts of:

obtaining a conventional digital map having traditional segments and nodes; and

representing a road network of the precision digital road map as a set of geometric shapes indexed to the traditional segments and nodes, which traditional segments and nodes have substantially no physical meaning in the precision digital road map.