Joint probabilistic modeling and inference of intersection structure

Patent metadata

Field	Value
Publication number	US-10013508-B2
Application number	US-201414508653-A
Country	US
Kind code	B2
Filing date	Oct 7, 2014
Priority date	Oct 7, 2014
Publication date	Jul 3, 2018
Grant date	Jul 3, 2018

Abstract

A method of modeling an intersection structure of a roadway. The method includes receiving a first data set including road lane information, and receiving a second data set including vehicle trajectory information for an intersection structure of a roadway. The method includes determining lane node locations from at least one of the first and second data sets. A set of potential links between the lane node locations may be compiled. The method may further include assessing, for each link, a probability that the link is a valid link, and assigning each link with a probability value. The links may be filtered based on a predetermined threshold probability value and a set of valid links is generated. A model of the intersection structure is created based on the set of valid links.

First claim

What is claimed is:

1. A method of modeling an intersection structure of a roadway to provide a detailed intersection map for a fully automated driving system, the method comprising:
receiving, at a computing device, a first data set including road lane information;
receiving, at the computing device, a second data set including vehicle trajectory information for an intersection structure of a roadway;
determining, using the computing device, lane node locations from at least one of the first and second data sets;
compiling, using the computing device, a set of potential links between the lane node locations;
assessing, for each link, a probability that the link is a valid link by using a tracking algorithm to obtain a plurality of complete tracks from the second data set, wherein each complete track represents a single vehicle tracked through the intersection;
correlating each complete track with a link;
assigning each link with a probability value, with each link that is correlated with a complete track being assigned a probability value of 1;
filtering the links, using the computing device, based on a predetermined threshold probability value and generating a set of valid links;
creating, using the computing device, a model of the intersection structure based on the set of valid links;
providing a detailed intersection map with valid connections between lanes, based on the model of the intersection, for a fully automated driving system; and
utilizing the detailed intersection map by the fully automated driving system.

2. The method according to claim 1 , wherein each complete track represents a single vehicle tracked passing two node locations.

3. The method according to claim 1 , further comprising:
using the tracking algorithm to obtain a plurality of partial tracks from the second data set, each partial track representing a single vehicle tracked through at least a portion of the intersection; and
correlating each partial track with a link.

4. The method according to claim 3 , wherein the partial track passes through at least one node location.

5. The method according to claim 3 , wherein the step of assigning the probability value to a link that is correlated with a partial track comprises using a Bayesian Model Averaging technique to calculate a probability value that is less than 1.

6. The method according to claim 5 , wherein the Bayesian Model Averaging technique is governed by an equation:
p (λ q |T )=Σ m∈M p (λ q |m ) p ( m|T )

wherein p(λ q |T) is the probability value of the link (λ q ), given a set of partial tracks (T), where m is an intersection model and a set of intersection models (M) is parsimonious with data used with the equation.

7. The method according to claim 1 , wherein the road lane information is received from a first source, and the vehicle trajectory information is received from a second source.

8. The method according to claim 7 , wherein the first source comprises a preexisting map database.

9. The method according to claim 7 , wherein the first source comprises data obtained from a lane estimation algorithm using a combination of light detection and ranging (LIDAR) data and road map data.

10. The method according to claim 7 , wherein the second source comprises at least one of LIDAR data, radar data, still image data, video data, tabular data, and combinations thereof.

11. The method according to claim 1 , wherein the predetermined threshold probability value is assigned a value specific to the intersection, and filtering the links comprises excluding all links having a probability value of less than the predetermined threshold probability value from the set of valid links.

12. A method of modeling an intersection structure of a roadway to provide a detailed intersection map for a fully automated driving system, the method comprising:
identifying, using a computing device, a set of links between lane node locations for an intersection structure of a roadway;
using vehicle trajectory information from the intersection structure to generate, using the computing device, a set of vehicle tracks that passed through the intersection;
categorizing the vehicle tracks into complete tracks and partial tracks;
assessing, using the computing device, for each partial track, a probability that the partial track correlates to a link, wherein the probability is determined using a Bayesian Model Averaging technique;
generating a set of valid links by:
assigning each complete vehicle track a probability value of 1;
assigning each partial vehicle track a probability value that is less than 1; and
filtering the links based on a predetermined threshold probability value;

creating, using the computing device, a model of the intersection structure based on the set of valid links;
providing a detailed intersection map with valid connections between lanes, based on the model of the intersection, for a fully automated driving system; and
utilizing the detailed intersection map by the fully automated driving system.

13. The method according to claim 12 , wherein the set of valid links excludes all links having an assigned probability value of less than the predetermined threshold probability value.

14. The method according to claim 12 , wherein the Bayesian Model Averaging technique is governed by an equation:
p (λ q |T )=Σ m∈M p (λ q |m ) p ( m|T )

wherein p(λ q |T) is the probability value of the link (λ q ), given a set of partial tracks (T), where m is an intersection model and a set of intersection models (M) is parsimonious with data used with the equation.

Assignees

• Toyota Eng & Mfg North America

Inventors

• Joshi Avdhut S
• James Michael R

Classifications

• G06F30/13 Primary

  Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads · CPC title

• E01C1/02 Primary

  Crossings, junctions or interconnections between roads on the same level {(construction of traffic islands E01F1/00)} · CPC title

• G06F17/5004 Primary

  Physics · mapped topic

• G01C21/3867

  Geometry of map features, e.g. shape points, polygons or for simplified maps · CPC title

• G01C21/3815

  Road data · CPC title