OBSTACLE DETECTION ARRANGEMENTS IN AND FOR AUTONOMOUS VEHICLES

• US 20100026555A1
• Filed: 06/11/2007
• Published: 02/04/2010
• Est. Priority Date: 06/09/2006
• Status: Abandoned Application

• Current Assignee: Carnegie Mellon University
• Original Assignee: Carnegie Mellon University
• Inventors: Johnston, Joshua, Ziglar, Jason, WHITTAKER, William L.
• Application Number:
  US11/761,347
• Publication Number:
  US 20100026555A1
• Time in Patent Office: Days
• US Class Current: 342/70
• CPC Class Codes: G05D 1/024 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0257 using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• CPC Class Codes: G05D 1/027 comprising intertial navigation means, e.g. azimuth detector inertial navigation G01C21/16; inertial navigation combined with non-inertial navigation instruments G01C21/165
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS
• CPC Class Codes: G08G 1/161 Decentralised systems, e.g. inter-vehicle communication
• CPC Class Codes: G08G 1/22 Platooning, i.e. convoy of communicating vehicles

Abstract

An arrangement for obstacle detection in autonomous vehicles wherein two significant data manipulations are employed in order to provide a more accurate read of potential obstacles and thus contribute to more efficient and effective operation of an autonomous vehicle. A first data manipulation involves distinguishing between those potential obstacles that are surrounded by significant background scatter in a radar diagram and those that are not, wherein the latter are more likely to represent binary obstacles that are to be avoided. A second data manipulation involves updating a radar image to the extent possible as an object comes into closer range. Preferably, the first aforementioned data manipulation may be performed via context filtering, while the second aforementioned data manipulation may be performed via blob-based hysteresis.

Claims

1. A method of providing obstacle detection in an autonomous vehicle, said method comprising the steps of:

obtaining a radar diagram;

discerning at least one prospective obstacle in the radar diagram;

ascertaining background scatter about the at least one prospective obstacle;

classifying the at least one prospective obstacle in relation to the ascertained background scatter; and

refining the radar diagram and reevaluating the at least one prospective obstacle;

said reevaluating comprising repeating said steps of ascertaining and classifying.

2. The method according to claim 1, wherein said classifying step comprises applying a context-based filter to data corresponding to the at least one prospective obstacle.

3. The method according to claim 2, wherein said step of applying a context-based filter comprises applying a kernel filter.

4. The method according to claim 3, wherein said step of applying a kernel filter comprises:

choosing at least one pixel from the radar diagram corresponding to a discerned prospective obstacle;

applying a first mathematical function to the at least one chosen pixel; and

applying a second mathematical function to at least one pixel disposed adjacent to the at least one chosen pixel; and

relating the first mathematical function and the second mathematical function towards classifying the at least one prospective obstacle.

5. The method according to claim 4, wherein said step of applying a second mathematical function comprises applying a second mathematical function to a plurality of pixels disposed about a periphery of the at least one chosen pixel.

6. The method according to claim 4, wherein:

said step of applying a first mathematical function comprises deriving a first aggregate intensity, corresponding to the at least one chosen pixel;

said step of applying a second mathematical function comprises deriving a second aggregate intensity, corresponding to the at least one pixel disposed adjacent to the at least one chosen pixel;

said relating step comprising subtracting the second aggregate intensity from the first aggregate intensity.

7. The method according to claim 6, wherein said relating step comprises normalizing, relative to a number of pixels in the at least one chosen pixel, the first aggregate intensity subtracted by the second aggregate intensity, to yield a normalized net intensity.

8. The method according to claim 7, wherein said classifying step further comprises classifying a discerned prospective obstacle as a binary obstacle if the normalized net intensity is greater than a predetermined threshold value.

9. The method according to claim 4, wherein the at least one chosen pixel corresponds to a maximum size for a prospective obstacle to be classified as a binary obstacle.

10. The method according to claim 9, wherein the at least one pixel disposed adjacent to the at least one chosen pixel corresponds to a desired extent of clear space adjacent a binary obstacle.

11. The method according to claim 1, wherein said discerning step comprises labeling at least one discerned obstacle with polar radar coordinates.

12. The method according to claim 11, wherein said refining comprises transforming at least a portion of the radar diagram from polar coordinates to rectangular coordinates.

13. The method according to claim 12, wherein said transforming step comprises accessing a vehicle pose history

14. The method according to claim 1, wherein said discerning step comprises time-stamping at least one discerned obstacle.

15. The method according to claim 1, wherein said reevaluating step further comprises applying hysteresis to data corresponding to the at least one prospective obstacle.

16. The method according to claim 15, wherein said step of applying hysteresis comprises evaluating, at different timepoints, bunched radar data corresponding to the at least one prospective obstacle.

17. The method according to claim 16, wherein said evaluating step comprises:

evaluating, at a first timepoint, a first group of bunched radar data corresponding to the at least one prospective obstacle; and

evaluating, at a second timepoint, a second group of bunched radar data corresponding to the at least one prospective obstacle;

the second group of bunched radar data being contiguous with respect to the first group of bunched radar data relative to a predetermined reference map.

18. The method according to claim 17, wherein said evaluating step further comprises:

replacing the first group of bunched radar data with the second group of bunched radar data; and

storing the first group of bunched radar data in a history.

19. A system for providing obstacle detection in an autonomous vehicle, said system comprising:

an arrangement for discerning at least one prospective obstacle in a radar diagram;

an arrangement for ascertaining background scatter about the at least one prospective obstacle;

an arrangement for classifying the at least one prospective obstacle in relation to the ascertained background scatter; and

an arrangement for refining the radar diagram and reevaluating the at least one prospective obstacle;

said refining and reevaluating arrangement acting to prompt a repeat of ascertaining background scatter about the at least one prospective obstacle and classifying the at least one prospective obstacle in relation to the ascertained background scatter.

20. The system according to claim 19, wherein said classifying arrangement is acts to apply a context-based filter to data corresponding to the at least one prospective obstacle.

21. The system according to claim 20, wherein said classifying arrangement acts to apply a kernel filter to data corresponding to the at least one prospective obstacle.

22. The system according to claim 21, wherein said classifying arrangement acts to:

choose at least one pixel from the radar diagram corresponding to a discerned prospective obstacle;

apply a first mathematical function to the at least one chosen pixel; and

apply a second mathematical function to at least one pixel disposed adjacent to the at least one chosen pixel; and

relate the first mathematical function and the second mathematical function towards classifying the at least one prospective obstacle.

23. The system according to claim 22, wherein said classifying arrangement acts to apply a second mathematical function to a plurality of pixels disposed about a periphery of the at least one chosen pixel.

24. The system according to claim 22, wherein:

the first mathematical function yields a first aggregate intensity, corresponding to the at least one chosen pixel;

the second mathematical function yields a second aggregate intensity, corresponding to the at least one pixel disposed adjacent to the at least one chosen pixel;

said classifying arrangement acts to subtract the second aggregate intensity from the first aggregate intensity.

25. The system according to claim 24, wherein said classifying arrangement further acts to normalize, relative to a number of pixels in the at least one chosen pixel, the first aggregate intensity subtracted by the second aggregate intensity, to yield a normalized net intensity.

26. The system according to claim 25, wherein said classifying arrangement further acts to classify a discerned prospective obstacle as a binary obstacle if the normalized net intensity is greater than a predetermined threshold value.

27. The system according to claim 22, wherein the at least one chosen pixel corresponds to a maximum size for a prospective obstacle to be classified as a binary obstacle.

28. The system according to claim 27, wherein the at least one pixel disposed adjacent to the at least one chosen pixel corresponds to a desired extent of clear space adjacent a binary obstacle.

29. The system according to claim 19, wherein said discerning arrangement acts to label at least one discerned obstacle with polar radar coordinates.

30. The system according to claim 29, wherein said refining and reevaluating arrangement acts to transform at least a portion of the radar diagram from polar coordinates to rectangular coordinates.

31. The system according to claim 30, wherein said transforming said refining and reevaluating arrangement further acts to access a vehicle pose history

32. The system according to claim 19, wherein said discerning arrangement acts to time-stamp at least one discerned obstacle.

33. The system according to claim 19, wherein said refining and reevaluating arrangement further acts to apply hysteresis to data corresponding to the at least one prospective obstacle.

34. The system according to claim 33, wherein said refining and reevaluating arrangement acts to evaluate, at different timepoints, bunched radar data corresponding to the at least one prospective obstacle.

35. The system according to claim 34, wherein said refining and reevaluating arrangement acts to:

evaluate, at a first timepoint, a first group of bunched radar data corresponding to the at least one prospective obstacle; and

evaluate, at a second timepoint, a second group of bunched radar data corresponding to the at least one prospective obstacle;

the second group of bunched radar data being contiguous with respect to the first group of bunched radar data relative to a predetermined reference map.

36. The system according to claim 35, wherein said refining and reevaluating arrangement further acts to:

replace the first group of bunched radar data with the second group of bunched radar data; and

store the first group of bunched radar data in a history.