Method and system for vehicle navigation using information from smart node

1. A node for capturing information about moving objects at an intersection, the node comprising:

a plurality of first cameras that are positioned to capture first digital images of an intersection from different fields of view within a first vision range, each first camera capturing a unique field of view having a field of view apex and a depth of field in a corresponding intersection direction;

a second camera positioned to capture second digital images in a field of view that is wider than that of each first camera;

a processor and a computer-readable storage medium comprising programming instructions that are configured to, when executed, cause the processor to:

detect in the first digital images and the second digital images a set of objects of interest of the intersection,

determine motion of each detected object of interest in the set of objects of interest from consecutive images of the first digital images or the second digital images separated in time, and

generate, for each object of interest of the set of objects of interest, augmented perception data that includes location data in the global coordinate system and the determined motion of each object of interest in the set of objects of interest; and

a communication system configured to transmit, via a wireless communication system, the augmented perception data to a remote server system, wherein the augmented perception data is accessible by at least one vehicle to control navigation of the at least one vehicle through the intersection.

2. The node of claim 1 , wherein:

the plurality of first cameras comprise a plurality of narrow field of view cameras, each of which is positioned to capture digital images of a unique segment of the intersection; and

the second camera comprises a wide field of view camera that is positioned to capture digital images that include substantially all of the unique segments of the intersection.

3. The node of claim 2 , wherein the wide field of view camera comprises a convex lens.

4. The node of claim 3 , wherein the second digital images comprise a hemispherical image.

5. The node of claim 3 , wherein the second digital images comprise a panoramic image.

6. The node of claim 3 , wherein the computer-readable medium further comprises additional programming instructions that are configured to, when executed, further cause the processor to:

segment each of the second digital images into a plurality of image segments; and

detect, in at least one of the plurality of image segments, an object of interest in the second vision range, wherein the set of objects of interest of the intersection includes the object of interest in the second vision range.

7. The node of claim 1 , wherein the second camera is further positioned to capture the second digital images in a volume of space at the intersection vertically below the field of view apex of each first camera, the second camera comprising a second vision range of 360°.

8. The node of claim 1 , wherein the computer-readable medium further comprises additional programming instructions that are configured to, when executed, cause the processor to:

process the first digital images of the first vision range to extract at least one object of interest in the first vision range; and

process the second digital images of the second vision range to extract at least one object of interest in the second vision range,

wherein:

the second vision range is different from the first vision range, and

the set of objects of interest of the intersection comprises the at least one object of interest extracted from the first vision range and the at least one object of interest extracted from the second vision range.

9. The node of claim 1 , further comprising:

a node housing configured to house the plurality of first cameras and the second camera;

a node controller housing configured to house the processor, the computer-readable storage medium and at least a portion of the communication system; and

a node mount assembly configured to attach the node housing and the node controller housing to a gantry of a traffic light pole.

10. The node of claim 1 , wherein:

the communication system transmits the generated augmented perception data to a first server of the remote server system; and

the communication system is further configured to transmit at least one of the first digital images and the second digital images to a second server of the remote server system.

11. The node of claim 1 , wherein the communication system is further configured to communicate with one or more nodes of a network of nodes.

12. The node of claim 11 , wherein the computer-readable medium further comprises additional programming instructions that are configured to, when executed, cause the processor to:

detect a situational flag condition associated with at least one object of interest in the set of objects of interest proximate the intersection; and

flag the generated augmented perception data associated with the at least one object having the detected situational flag condition,

wherein the communication system is further configured to transmit the flagged augmented perception data to at least one of an adjacent node of the network of nodes, a local law enforcement computing system, a local healthcare services computing system or a first responder computing system.

13. The node of claim 1 , wherein:

the computer-readable medium further comprises additional programming instructions that are configured to, when executed, further cause the processor to:

classify each object of interest of the detected set of objects of interest as one of a moving actor, a moving object or a moving vehicle;

the instructions to cause the processor to determine the motion of each object of interest in the set of objects of interest, includes instructions that are configured to, when executed, further cause the processor to:

forecast a direction and a speed of motion of each object of interest of the set of objects of interest; and

the flagged augmented perception data includes the forecasted direction and speed of motion associated with the at least one object having the detected situational flag condition.

14. The node of claim 1 , wherein:

the computer-readable medium further comprises additional programming instructions that are configured to, when executed, further cause the processor to:

classify each object of interest of the detected set of objects of interest as one of a moving actor, a moving object or a moving vehicle;

the instructions to cause the processor to determine the motion of each object of interest in the set of objects of interest, includes instruction that are configured to, when executed, further cause the processor to:

forecast a direction and a speed of motion of each object of interest of the set of objects of interest; and

the augmented perception data, for each object of interest of the detected set of objects of interest, includes the forecasted direction and speed of motion.

15. The node of claim 1 , wherein:

the computer-readable medium further comprises additional programming instructions that are configured to, when executed, further cause the processor to:

determine a location of each object of interest in a corresponding image of the first digital images and the second digital images; and

translate the determined location into location data in a global coordinate system.

16. A method for capturing information about moving objects at an intersection, the method comprising:

capturing, by a plurality of first cameras that are positioned at an intersection, first digital images of the intersection in each corresponding intersection direction within a first vision range;

capturing, by a second camera that is positioned at the intersection and that has a second vision range that is different from any of the first vision ranges of the first cameras, second digital images;

detecting, by a processor, in the first digital images and the second digital images, a set of objects of interest surrounding the intersection;

determining, by the processor, motion of each object of interest in the set of objects of interest from consecutive images of the first digital images or the second digital images separated in time;

generating, by the processor, augmented perception data for each object of interest of the set of objects of interest; and

transmitting, by a communication system, the generated augmented perception data to a remote server system, the augmented perception data is accessible by at least one vehicle to control navigation of the at least one vehicle through the intersection.

17. The method of claim 16 , wherein:

the capturing, by the plurality of first cameras, comprises capturing by each first camera of the plurality of first cameras, a respective one digital image of the first digital images from a different field of view apex to a depth of field in a different intersection direction as compared to each other first camera; and

the capturing, by the second camera, includes capturing in a field of view that is in a volume of space at the intersection vertically below the field of view apex of each first camera.

18. The method of claim 17 , wherein:

the plurality of first cameras comprise a plurality of narrow field of view cameras, each of which is positioned to capture digital images of a unique segment of the intersection; and

the second camera comprises a wide field of view camera that is positioned to capture the second digital images that include substantially all of the unique segments of the intersection.

19. The method of claim 17 , wherein the wide field of view camera comprises a convex lens.

20. The method of claim 17 , wherein the capturing, by the second camera, includes capturing a hemispherical image.

21. The method of claim 16 , further comprising:

segmenting, by the processor, each second digital image of the second digital images into a plurality of image segments;

wherein:

the detecting further comprises detecting, in at least one image segment of the plurality of image segments, an object of interest in the second vision range, and

the set of objects of interest proximate the intersection include the object of interest in the second vision range.

22. The method of claim 16 , further comprising:

processing, by the processor, the first digital images of the first vision range to extract at least one object of interest in the first vision range; and

processing, by the processor, the second digital images of the second vision range to extract at least one object of interest in the second vision range,

wherein:

the second vision range is different from the first vision range, and

the set of objects of interest proximate the intersection comprises the at least one object of interest in the first vision range and the at least one object of interest in the second vision range.

23. The method of claim 16 , wherein:

the transmitting of the generated augmented perception data, by the communication system, includes transmitting the generated augmented perception data to a first server of the remote server system; and

further comprising:

transmitting, by the communication system, at least one of the first digital images and the second digital images to a second server of the remote server system.

24. The method of claim 16 , further comprising communicating, by the communication system, information associated with a traffic condition of the node to one or more adjacent nodes of a network of nodes.

25. The method of claim 24 , wherein:

the detecting further comprises detecting a situational flag condition associated with at least one object of interest in the set of objects of interest proximate the intersection; and

further comprising:

flagging, by the processor, the generated augmented perception data associated with the at least one object having the detected situational flag condition,

wherein the transmitting further includes transmitting, by the communication system, the flagged augmented perception data to at least one of an adjacent node of the network of nodes, a local law enforcement computing system, a local healthcare services computing system or a first responder computing system.

26. The method of claim 25 , further comprising:

classifying, by the processor, each object of interest of the detected set of objects of interest as one of a moving actor, a moving object or a moving vehicle,

wherein the determining of the motion of each object of interest in the set of objects of interest, includes:

forecasting, by the processor, a direction and a speed of motion of each object of interest of the set of objects of interest; and

wherein the flagged augmented perception data includes the forecasted direction and speed of motion associated with the at least one object having the detected situational flag condition.

27. The method of claim 16 , further comprising:

classifying, by the processor, each object of interest of the detected set of objects of interest as one of a moving actor, a moving object or a moving vehicle;

wherein the determining of the motion of each object of interest in the set of objects of interest, includes:

forecasting, by the processor, a direction and a speed of motion of each object of interest of the set of objects of interest; and

wherein the augmented perception data, for each object of interest of the detected set of objects of interest, includes the forecasted direction and speed of motion.

28. The method of claim 16 , further comprising, by the processor:

determining a location of each object of interest in a corresponding image of the first digital images and the second digital images; and

translating the determined location into location data in a global coordinate system.