Detecting foliage using range data

• US 10,163,015 B2
• Filed: 11/16/2016
• Issued: 12/25/2018
• Est. Priority Date: 11/16/2016
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Castro, Marcos Paul Gerardo, Kadetotad, Sneha, Liu, Dongran, Jain, Jinesh J.
• Primary Examiner(s): BEKELE, MEKONEN T
• Application Number:
  US15/353,640
• Publication Number:
  US 20180137374A1
• Time in Patent Office: 769 Days
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G06T 2207/30252 Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06T 7/97 Determining parameters from multiple pictures depth or shape recovery from multiple images G06T7/55; stereo camera calibration G06T7/85
• CPC Class Codes: G06V 10/40 Extraction of image or video features
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads

Abstract

A system for detecting and identifying foliage includes a tracking component, a tracking parameters component, and a classification component. The tracking component is configured to detect and track one or more features within range data from one or more sensors. The tracking parameters component is configured to determine tracking parameters for each of the one or more features. The tracking parameters include a tracking age and one or more of a detection consistency and a position variability. The classification component is configured to classify a feature of the one or more features as corresponding to foliage based on the tracking parameters.

Claims

1. A method for detecting foliage, the method comprising:

detecting and tracking one or more features within range data from one or more sensors;

calculating tracking parameters for each of the one or more features, the tracking parameters comprising a tracking age and one or more of a detection consistency and a position variability; and

classifying at least one feature of the one or more features as corresponding to foliage based on one or more of the tracking age falling below an age threshold, the detection consistency falling below a consistency threshold, or the position variability exceeding a variability threshold; and

determining a driving maneuver for a vehicle based on the classification of the at least one feature and providing the driving maneuver to an automated driving/assistance system to be executed by the vehicle.

2. The method of claim 1, wherein classifying the feature of the one or more features comprises classifying a first feature, the method further comprising classifying a second feature as corresponding to a solid object based on the tracking parameters.

3. The method of claim 2, wherein classifying the second feature as corresponding to a solid object comprises classifying as corresponding to a solid object based on one or more of the tracking age exceeding an age threshold, the detection consistency exceeding a consistency threshold, or the position variability falling below a variability threshold.

4. The method of claim 1, wherein determining tracking parameters comprises determining the tracking age, wherein determining the tracking age for a specific feature comprises determining how long the specific feature has been tracked or detected.

5. The method of claim 1, wherein determining tracking parameters comprises determining the detection consistency, wherein determining the detection consistency for a specific feature comprises determining a percent or ratio of frames in which the feature has been detected within a time period.

6. The method of claim 1, wherein determining tracking parameters comprises determining the position variability, wherein determining the position variability for a specific feature comprises determining a standard deviation for one or more of position and orientation of the specific feature.

7. The method of claim 1, further comprising determining whether to avoid impacting an obstacle corresponding to the feature of the one or more features based on the classification of the feature.

8. The method of claim 1, wherein determining the driving maneuver for the vehicle based on the classification of the at least one feature comprises determining that a location corresponding to foliage is to be avoided with a lower priority than a solid object.

9. A system comprising:

a tracking component configured to detect and track one or more features within range data from one or more sensors;

a tracking parameters component configured to calculate tracking parameters for each of the one or more features, the tracking parameters comprising a tracking age and one or more of a detection consistency and a position variability; and

a classification component configured to classify at least one feature of the one or more features as corresponding to foliage based on one or more of the tracking age falling below an age threshold, the detection consistency falling below a consistency threshold, or the position variability exceeding a variability threshold; and

an obstacle avoidance component configured to determine a driving maneuver for a vehicle based on the classification of the at least one feature and provide the driving maneuver to an automated driving/assistance system to be executed by the vehicle.

10. The system of claim 9, wherein classifying the feature of the one or more features comprises classifying a first feature, wherein the classification component is further configured to classify a second feature as corresponding to a solid object based on the tracking parameters.

11. The system of claim 10, wherein the classification component is configured to classify the second feature as corresponding to a solid object based on one or more of:

the tracking age exceeding an age threshold;

the detection consistency exceeding a consistency threshold;

or

the position variability falling below a variability threshold.

12. The system of claim 9, wherein the tracking parameters component is configured to determine tracking parameters comprising one or more of:

the tracking age, wherein the tracking parameters component is configured to determine the tracking age for a specific feature by determining how long the specific feature has been tracked or detected;

the detection consistency, wherein the tracking parameters component is configured to determine the detection consistency for a specific feature by determining a percent or ratio of frames in which the feature has been detected within a time period;

or

the position variability, wherein the tracking parameters component is configured to determine the position variability for a specific feature by determining a standard deviation for one or more of position and orientation of the specific feature.

13. The method of claim 9, wherein the obstacle avoidance component is further configured to determine whether to avoid impacting an obstacle corresponding to the feature of the one or more features based on the classification of the feature.

14. Non-transitory computer readable storage media storing instructions that, when executed by one or more processors, cause the one or more processors to:

detect and track one or more features within range data from one or more sensors;

calculate tracking parameters for each of the one or more features, the tracking parameters comprising a tracking age and one or more of a detection consistency and a position variability; and

classify at least one feature of the one or more features as corresponding to foliage based on one or more of the tracking age falling below an age threshold, the detection consistency falling below a consistency threshold, or the position variability exceeding a variability threshold; and

determine a driving maneuver for a vehicle based on the classification of the at least one feature and providing the driving maneuver to an automated driving/assistance system to be executed by the vehicle.

15. The non-transitory computer readable storage media of claim 14, wherein classifying the feature of the one or more features comprises classifying a first feature, wherein the instructions further cause the one or more processors to classify a second feature as corresponding to a solid object based on the tracking parameters.

16. The non-transitory computer readable storage media of claim 15, wherein the instructions cause the one or more processors to classify the second feature as corresponding to a solid object based on one or more of:

the tracking age exceeding an age threshold;

the detection consistency exceeding a consistency threshold;

or

the position variability falling below a variability threshold.

17. The non-transitory computer readable storage media of claim 14, wherein the instructions cause the one or more processors to determine tracking parameters by determining one or more of:

the tracking age, wherein the instructions cause the one or more processors to determine the tracking age for a specific feature by determining how long the specific feature has been tracked or detected;

the detection consistency, wherein the instructions cause the one or more processors to determine the detection consistency for a specific feature by determining a percent or ratio of frames in which the feature has been detected within a time period;

or

the position variability, wherein the instructions cause the one or more processors to determine the position variability for a specific feature by determining a standard deviation for one or more of position and orientation of the specific feature.