AUTOMATED ROUTING GRAPH MODIFICATION MANAGEMENT

1 . A computer-implemented method of creating routing graph modifications for a navigation constraints system that controls navigation of an autonomous vehicle, comprising:

receiving, by one or more processors, routing graph modification data from at least one data source, the routing graph modification data including geographic location data identifying a location to which a routing graph modification applies, the at least one data source including at least one of a municipal data source, a traffic data source, routing/dispatch data from a routing/dispatch system, or perception data collected by an autonomous vehicle;

associating, by the one or more processors, the routing graph modification data with one or more roadway elements in a routing graph of the navigation constraints system;

evaluating the routing graph modification data associated with the one or more roadway elements to classify the routing graph modification data as a routing graph modification associated with the one or more roadway elements; and

providing, by the one or more processors, the routing graph modification data of the routing graph modification to the navigation constraints system.

2 . The method of claim 1 , wherein the routing graph modification data from the at least one data source includes timing data indicating a duration of a routing graph modification, further comprising associating, by the one or more processors, a routing graph modification expiration time with the routing graph modification.

3 . The method of claim 2 , further comprising instructing, by the one or more processors, the navigation constraints system to remove an expired routing graph modification.

4 . The method of claim 2 , further comprising triggering, by the one or more processors, a refresh of routing graph modification data of the navigation constraints system when the routing graph modification expiration time has been reached.

5 . The method of claim 1 , further comprising standardizing, by the one or more processors, representations of the routing graph modification data from the at least one data source and storing standardized representations of the routing graph modification data in an evidence storage.

6 . The method of claim 1 , wherein the associating comprises providing, by the one or more processors, the routing graph modification data to a clustering algorithm that clusters the routing graph modification data by geographic location and routing graphs clustered routing graph modification data to the one or more roadway elements in the routing graph of the navigation constraints system.

7 . The method of claim 6 , further comprising associating, by the one or more processors, the clustered routing graph modification data mapped to the one or more roadway elements in the routing graph of the navigation constraints system with supporting context data including at least one of metadata or video.

8 . The method of claim 7 , wherein evaluating the routing graph modification data comprises providing, by the one or more processors, the clustered routing graph modification data mapped to the one or more roadway elements in the routing graph of the navigation constraints system and the supporting context data to a display interface and enabling a human to provide inputs via the display interface to classify the routing graph modification data as the routing graph modification associated with the one or more roadway elements.

9 . The method of claim 6 , wherein evaluating the routing graph modification data comprises providing, by the one or more processors, the clustered routing graph modification data mapped to the one or more roadway elements in the routing graph of the navigation constraints system to a machine learning classification model to classify the routing graph modification data as the routing graph modification associated with the one or more roadway elements.

10 . The method of claim 9 , further comprising weighting, by the one or more processors, the routing graph modification data received from respective data sources of the at least one data source by feeding back weighting data from the machine learning classification model to provide supervised learning.

11 . A navigation constraints system that controls navigation of an autonomous vehicle, comprising:

a memory that stores instructions; and

one or more processors that execute the instructions from the memory to perform operations comprising:

receiving routing graph modification data from at least one data source, the routing graph modification data including geographic location data identifying a location to which a routing graph modification applies, the at least one data source including at least one of a municipal data source, a traffic data source, routing/dispatch data from a routing/dispatch system, or perception data collected by an autonomous vehicle;

associating the routing graph modification data with one or more roadway elements in a routing graph;

enabling evaluation of the routing graph modification data associated with the one or more roadway elements to classify the routing graph modification data as a routing graph modification associated with the one or more roadway elements;

determining a travel route for navigating the autonomous vehicle based at least in part on navigational routing graph data evaluated relative to the routing graph modification associated with the one or more roadway elements; and

controlling motion of the autonomous vehicle based at least in part on the determined travel route.

12 . The system of claim 11 , wherein the routing graph modification data from the at least one data source includes timing data indicating a duration of a routing graph modification, the one or more processors further executing the instructions to perform operations including associating a routing graph modification expiration time with the routing graph modification.

13 . The system of claim 12 , the one or more processors further executing the instructions to perform operations including removing an expired routing graph modification.

14 . The system of claim 12 , the one or more processors further executing the instructions to perform operations including triggering a refresh of routing graph modification data of the navigation constraints system when the routing graph modification expiration time has been reached.

15 . The system of claim 11 , further comprising an evidence storage, the one or more processors further executing the instructions to perform operations including standardizing representations of the routing graph modification data from the at least one data source and storing standardized representations of the routing graph modification data in the evidence storage.

16 . The system of claim 11 , the one or more processors further executing the instructions to execute a clustering algorithm to cluster the routing graph modification data by geographic location and to map clustered routing graph modification data to the one or more roadway elements in the routing graph.

17 . The system of claim 16 , the one or more processors further executing the instructions to associate the clustered routing graph modification data mapped to the one or more roadway elements in the routing graph with supporting context data including at least one of metadata or video.

18 . The system of claim 17 , further comprising a display interface, the one or more processors further executing the instructions to provide the clustered routing graph modification data mapped to the one or more roadway elements in the routing graph and the supporting context data to the display interface and to enable a human to provide inputs via the display interface to classify the routing graph modification data as the routing graph modification associated with the one or more roadway elements.

19 . The system of claim 16 , wherein the clustering algorithm comprises one of a k-means clustering algorithm or a density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm.

20 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to create routing graph modifications for a navigation constraints system that controls navigation of an autonomous vehicle, by:

receiving routing graph modification data from at least one data source, the routing graph modification data including geographic location data identifying a location to which a routing graph modification applies, the at least one data source including at least one of a municipal data source, a traffic data source, routing/dispatch data from a routing/dispatch system, or perception data collected by an autonomous vehicle;

associating the routing graph modification data with one or more roadway elements in a routing graph of the navigation constraints system;

enabling evaluation of the routing graph modification data associated with the one or more roadway elements to classify the routing graph modification data as a routing graph modification associated with the one or more roadway elements; and

providing the routing graph modification data of the routing graph modification to the navigation constraints system.