Extending object detection and identification capability for an object sensor device

1. A method for extending an object identification range in a vehicle environment, the method comprising:

solving to a remaining one of a set of pre-defined shape models for an object by successive iterations of point cloud data as a distance from a vehicle to the object along a travel lane decreases due to a velocity of the vehicle exceeding a velocity of the object by:

receiving, via an object sensor device, the point cloud data;

detecting at least a portion of the object from the point cloud data;

comparing each of a set of pre-defined shape models with the at least the portion of the object; and

updating the set of pre-defined shape models to include ones comparing favorably with the point cloud data; and

when the successive iterations solve to a singular remaining one of the set of pre-defined shape models, identifying an iterative match to the object based on the singular remaining one of the set of pre-defined shape models.

2. The method of claim 1 , further comprising:

tracking the object based on the singular remaining one of the set of pre-defined shape models.

3. The method of claim 1 , wherein the point cloud data is generated at a range gate distance characteristic of the object sensor device, the range gate distance characteristic producing a point cloud density relatable to the at least the portion of the object.

4. The method of claim 3 , wherein the object sensor device comprises at least one of:

a LIDAR object sensor device;

a radar object sensor device; and

a milliwave object sensor device.

5. The method of claim 1 , wherein the point cloud data is generated at a range gate distance characteristic of the object sensor device.

6. The method of claim 1 , wherein the set of pre-defined shape models comprises:

a vehicular model profile relatable to each of a plurality of point cloud data samples of a pre-defined three-dimensional vehicle based on a corresponding plurality of closing distances relative to a range gate distance characteristic of the object sensor device.

7. The method of claim 6 , wherein the object sensor device has a known origin location with respect to the point cloud data of the object.

8. The method of claim 6 , wherein the set of pre-defined shape models comprises at least one of:

a passenger vehicle model;

a sport utility vehicle model;

a sport vehicle model; and

a cargo vehicle model.

9. A method for object identification in a vehicle environment, the method comprising:

receiving, via an object sensor device, first point cloud data relating to at least a spatial portion of the vehicle environment, the first point cloud data being relatable to at least a portion of a vehicular object and a set of pre-defined shape models associated with the at least a spatial portion of the environment;

comparing each of the set of pre-defined shape models with the first point cloud data;

forming a first subset of pre-defined shape models based on remaining ones of the set of pre-defined shape models comparing favorably with the first point cloud; and

determining most-likely match of the set of pre-defined shape models by iteratively, as a distance between a vehicle and the vehicular object along a travel lane decreases due to a velocity of the vehicle exceeding a velocity of the vehicular object:

receiving subsequent point cloud data relating to at least a further portion of the vehicular object;

comparing each of a remaining ones of the first subset of pre-defined shape models with the subsequent point cloud data; and

updating the remaining ones of the first subset of pre-defined shape models; and

when the first subset of pre-defined shape models corresponds to a remaining one of the first subset of pre-defined shape models, identifying a most-likely match of the vehicular object with the remaining one of the first subset of pre-defined shape models.

10. The method of claim 9 , wherein the first point cloud data is generated at a range gate distance characteristic of the object sensor device, the range gate distance characteristic producing a point cloud density relatable to the at least the portion of the vehicular object.

11. The method of claim 10 , wherein the object sensor device comprises at least one of:

a LIDAR object sensor device;

a radar object sensor device; and

a milliwave object sensor device.

12. The method of claim 9 , wherein the first point cloud data is generated at a range gate distance characteristic of the object sensor device.

13. The method of claim 9 , wherein the set of pre-defined shape models comprises:

a vehicular model profile relatable to each of a plurality of point cloud data samples of a known three-dimensional vehicle based on a corresponding plurality of sample distances up to a range gate distance characteristic of the object sensor device.

14. The method of claim 13 , wherein the object sensor device has a known origin location with respect to the each of the plurality of point cloud data samples of the known three-dimensional vehicle.

15. The method of claim 13 , wherein the set of pre-defined shape models comprise at least one of:

a passenger vehicle model;

a sport utility vehicle model;

a sport vehicle model; and

a cargo vehicle model.

16. A vehicle control unit for vehicular object identification in a vehicle environment, the vehicle control unit comprising:

a processor;

memory communicably coupled to the processor and to an object sensor device, and the memory storing:

an object detection module including instructions that, when executed by the processor, cause the processor to:

receive, via an object sensor device, point cloud data;

detect at least a portion of a vehicular object from the point cloud data; and

produce detected vehicular object data from the at least the portion of the vehicular object from the point cloud data; and

an object decision module including instructions that, when executed by the processor, cause the processor to:

compare each of a set of pre-defined shape models with the detected vehicular object data; and

update the set of pre-defined shape models to ones comparing favorably with the detected vehicular object data; and

wherein, when successive iterations of the object detection module and the object decision module on subsequent point cloud data, as a distance from a vehicle to the vehicular object along a travel lane decreases due to a velocity of the vehicle exceeding a velocity of the vehicular object, solve to a remaining one of the set of pre-defined shape models, the object decision module produces vehicular object classification data based on the remaining one of the set of pre-defined shape models.

17. The vehicle control unit of claim 16 , wherein the point cloud data is generated at a range gate distance characteristic of the object sensor device, the range gate distance characteristic producing a point cloud density relatable to the at least the portion of the object.

18. The vehicle control unit of claim 16 , wherein the point cloud data is generated at a range gate distance characteristic of the object sensor device.

19. The vehicle control unit of claim 18 , wherein the object sensor device has a predetermined origin location with respect to the each of the plurality of point cloud data samples of each of the set of pre-defined shape models.

20. The vehicle control unit of claim 16 , wherein the set of pre-defined shape models comprises at least one of:

a passenger vehicle model;

a sport utility vehicle model;

a sport vehicle model; and

a cargo vehicle model.