AUTOMATICALLY PERCEIVING TRAVEL SIGNALS

• US 20180299893A1
• Filed: 04/18/2017
• Published: 10/18/2018
• Est. Priority Date: 04/18/2017
• Status: Abandoned Application

• Current Assignee: Motional AD, LLC (Aptiv PLC)
• Original Assignee: nuTonomy, Inc. (Aptiv PLC)
• Inventors: Qin, Baoxing, Vijayalingam, Aravindkumar
• Application Number:
  US15/490,599
• Publication Number:
  US 20180299893A1
• Time in Patent Office: Days
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means

Abstract

Among other things, one or more travel signals are identified by analyzing one or more images and data from sensors, classifying candidate travel signals into zero, one or more true and relevant travel signals, and estimating a signal state of the classified travel signals.

Claims

1. A method comprising:

(a) causing a vehicle to drive autonomously on a road,

(b) automatically detecting a travel signal and estimating a signal state of the travel signal, and

(c) automatically controlling a maneuver of the vehicle based on the signal state.

2. The method of claim 1, in which the detecting the travel signal comprises identifying, in an image derived from signals of a sensor, a representation of the travel signal.

3. The method of claim 1, in which the identifying the representation of the travel signal comprises analyzing pixels of the image based on saturation or lightness or both.

4. The method of claim 1, in which the identifying the representation of the travel signal comprises determining edges based on pixels and generating a shape based on the edges.

5. The method of claim 4, in which the identifying the representation of the travel signal is based on one or more of the following criteria:

edges, shapes, convexity, sizes, and solidness.

6. The method of claim 1, in which the identifying the representation of the travel signal is based on matching characteristics of the representation of the travel signal to predefined criteria.

7. The method of claim 6, in which the identifying the representation of the travel signal is based on modeling the predefined criteria by probabilistic distributions and inferring probabilistic scores.

8. The method of claim 1, in which the detecting the travel signal comprises determining a correspondence between the representation of the travel signal and a true travel signal.

9. The method of claim 1, in which the determining the correspondence is based on one or more of the following:

a previously identified travel signal, travel signal shapes, travel signal colors, travel signal positions, travel signal configurations, road networks, a location of the vehicle, and a route of the vehicle.

10. The method of claim 1, in which the determining the correspondence comprises using prior information associated with the travel signal.

11. The method of claim 10, in which the prior information comprises one or more of the following:

shapes, sizes, colors, locations, positions, and configurations.

12. The method of claim 1, in which the determining the correspondence comprises using prior information to generate a prior image of a travel signal.

13. The method of claim 12, in which the prior image comprises a bird'"'"'s-eye view or a field of view of a vision sensor or both.

14. The method of claim 1, in which the determining the correspondence comprises computing a classification score.

15. The method of claim 14, in which the classification score comprises a weighted sum of differences between measured data associated with the travel signal and prior information associated with the travel signal.

16. The method of claim 1, in which the determining the correspondence comprises computing a classification score using an algorithmic analysis on measured data associated with the travel signal and prior information.

17. The method of claim 16, in which the algorithmic analysis comprising (1) creating correspondences between the travel signal and known true travel signals;

(2) computing a likelihood score associated with the correspondences; and

(3) iterating (1) and (2) using a different set of correspondences until an optimal likelihood score associated with an optimal set of correspondences is identified.

18. The method of claim 17, in which the iterating comprises one or more of the following:

a randomized search, an exhaustive search, a linear programming, and a dynamic programming.

19. The method of claim 1, in which the estimating the signal state comprises using state transition information.

20. The method of claim 19, in which the transition information comprises colors, shapes, flashing patterns, or combinations of them.

21. The method of claim 1, in which the estimating the signal state is based on consistency of two or more travel signals.

22. The method of claim 1, in which the estimating the signal state is based on a position of a travel signal within a travel signal configuration.

23. The method of claim 1, in which the estimating the signal state comprises temporal filtering based on a previously estimated signal state.

24. The method of claim 1, comprising generating an alert based on an estimated signal state.