Joint probabilistic modeling and inference of intersection structure

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.