Vision-based detection and classification of traffic lights
First Claim
1. A method comprising:
- receiving, from an image-capture device coupled to an autonomous vehicle, an image of a field of view of the autonomous vehicle, wherein the image comprises a plurality of image portions;
calculating, for each given image portion, a respective score indicating a level of confidence that the given image portion represents an illuminated component of a traffic light, wherein calculating, for each given image portion, a respective score indicating a level of confidence that the given image portion represents an illuminated component of a traffic light comprises;
calculating a first sub-score for a center area of a particular image portion based on at least one characteristic of the center area of the particular image portion;
calculating a second sub-score for an outer area of the particular image portion surrounding the center area of the particular image portion based on at least one characteristic of the outer area of the particular image portion; and
calculating the score based on a difference between the second sub-score and the first sub-score;
identifying, from among the plurality of image portions, one or more candidate portions as image portions having a score exceeding a threshold level of confidence;
determining, using a classifier, that a particular candidate portion represents an illuminated component of a traffic light, wherein the classifier has been trained using training data indicative of a plurality of example images that include traffic lights and a plurality of example images that do not include traffic lights; and
based on the particular candidate portion representing an illuminated component of a traffic light, providing instructions executable by a computing device to control the autonomous vehicle.
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Abstract
The present disclosure is directed to an autonomous vehicle having a vehicle control system. The vehicle control system includes an image processing system. The image processing system receives an image that includes a plurality of image portions. The image processing system also calculates a score for each image portion. The score indicates a level of confidence that a given image portion represents an illuminated component of a traffic light. The image processing system further identifies one or more candidate portions from among the plurality of image portions. Additionally, the image processing system determines that a particular candidate portion represents an illuminated component of a traffic light using a classifier. Further, the image processing system provides instructions to control the autonomous vehicle based on the particular candidate portion representing an illuminated component of a traffic light.
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Citations
20 Claims
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1. A method comprising:
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receiving, from an image-capture device coupled to an autonomous vehicle, an image of a field of view of the autonomous vehicle, wherein the image comprises a plurality of image portions; calculating, for each given image portion, a respective score indicating a level of confidence that the given image portion represents an illuminated component of a traffic light, wherein calculating, for each given image portion, a respective score indicating a level of confidence that the given image portion represents an illuminated component of a traffic light comprises; calculating a first sub-score for a center area of a particular image portion based on at least one characteristic of the center area of the particular image portion; calculating a second sub-score for an outer area of the particular image portion surrounding the center area of the particular image portion based on at least one characteristic of the outer area of the particular image portion; and calculating the score based on a difference between the second sub-score and the first sub-score; identifying, from among the plurality of image portions, one or more candidate portions as image portions having a score exceeding a threshold level of confidence; determining, using a classifier, that a particular candidate portion represents an illuminated component of a traffic light, wherein the classifier has been trained using training data indicative of a plurality of example images that include traffic lights and a plurality of example images that do not include traffic lights; and based on the particular candidate portion representing an illuminated component of a traffic light, providing instructions executable by a computing device to control the autonomous vehicle. - View Dependent Claims (2, 3, 4, 5)
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6. An image processing system comprising:
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an image-capture device; at least one processor; and a memory having stored thereon instructions that, upon execution by the at least one processor, cause the image processing system to perform functions comprising; receiving, from the image-capture device coupled to an autonomous vehicle, an image of a field of view of the autonomous vehicle, wherein the image comprises a plurality of image portions; calculating, for each given image portion, a respective score indicating a level of confidence that the given image portion represents an illuminated component of a traffic light, wherein calculating, for each given image portion, a respective score indicating a level of confidence that the given image portion represents an illuminated component of a traffic light comprises; calculating a first sub-score for a center area of a particular image portion based on at least one characteristic of the center area of the particular image portion, wherein the center area is surrounded by a middle area of the particular image portion; calculating a second sub-score for an outer area of the particular image portion surrounding the middle area of the particular image portion based on at least one characteristic of the outer area of the particular image portion, wherein the middle area lies between the outer area and the center area; and calculating the score based on a difference between the second sub-score and the first sub-score; identifying, from among the plurality of image portions, one or more candidate portions as image portions having a score exceeding a threshold level of confidence; determining that a particular candidate portion represents an illuminated component of a traffic light based on training data indicative of a plurality of example images that include traffic lights and a plurality of example images that do not include traffic lights; and based on the particular candidate portion representing an illuminated component of a traffic light, providing executable instructions to control the autonomous vehicle. - View Dependent Claims (7, 8, 9, 10, 11, 12, 18, 19, 20)
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13. A non-transitory computer-readable medium having stored thereon instructions that, upon execution by at least one processor of a computing device, cause the computing device to perform functions comprising:
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receiving, from an image-capture device coupled to an autonomous vehicle, an image of a field of view of the autonomous vehicle, wherein the image comprises a plurality of image portions; calculating, for each given image portion, a respective score indicating a level of confidence that the given image portion represents an illuminated component of a traffic light, wherein calculating, for each given image portion, a respective score indicating a level of confidence that the given image portion represents an illuminated component of a traffic light comprises; calculating a first sub-score for a center area of a particular image portion based on at least one characteristic of the center area of the particular image portion; calculating a second sub-score for an outer area of the particular image portion surrounding the center area of the particular image portion based on at least one characteristic of the outer area of the particular image portion; and calculating the score based on a difference between the second sub-score and the first sub-score; identifying, from among the plurality of image portions, one or more candidate portions as image portions having a score exceeding a threshold level of confidence; determining, using a classifier, that a particular candidate portion represents an illuminated component of a traffic light, wherein the classifier has been trained using training data indicative of a plurality of example images that include traffic lights and a plurality of example images that do not include traffic lights; and based on the particular candidate portion representing an illuminated component of a traffic light, providing instructions executable by the computing device to control the autonomous vehicle. - View Dependent Claims (14, 15, 16, 17)
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Specification