Real time driving difficulty categorization
First Claim
1. A method comprising:
- receiving real time image data describing a real time image depicting a road scene;
determining region of interest data describing a version of the real time image configured to remove redundant features from the real time image;
determining dense grid sampling data describing one or more features included in the version of the real time image described by the region of interest data;
determining image feature vector data based on the one or more features included in the dense grid sampling data, the image feature vector data describing an image feature vector for the version of the real time image described by the region of interest data;
receiving client location data describing a geographic location of a client device associated with the road scene included in the real time image;
determining offline road map data for the road scene based on the geographic location of the client device, the offline road map data describing (1) a static label for a road included in the road scene and (2) offline road information describing a regulatory speed limit for the road included in the road scene;
selecting, based on the static label, a classifier for analyzing the image feature vector; and
executing, by a processor, the selected classifier to determine a real time driving difficulty category describing the difficulty for a user of the client device to drive in the road scene depicted in the real time image, wherein the real time driving difficulty category is determined based on the image feature vector, offline road information and runtime road description data describing the real time driving conditions of the road scene depicted in the real time image.
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Abstract
The disclosure includes a system and method for determine a real time driving difficulty category. The method may include determining image feature vector data based on one or more features depicted in a real time image of a road scene. The image feature vector data may describe an image feature vector for an edited version of the real time image. The method may include determining offline road map data for the road scene, which includes a static label for a road included in the road scene and offline road information describing a regulatory speed limit for the road. The method may include selecting, based on the static label, a classifier for analyzing the image feature vector. The method may include executing the selected classifier to determine a real time driving difficulty category describing the difficulty for a user of the client device to drive in the road scene.
13 Citations
18 Claims
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1. A method comprising:
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receiving real time image data describing a real time image depicting a road scene; determining region of interest data describing a version of the real time image configured to remove redundant features from the real time image; determining dense grid sampling data describing one or more features included in the version of the real time image described by the region of interest data; determining image feature vector data based on the one or more features included in the dense grid sampling data, the image feature vector data describing an image feature vector for the version of the real time image described by the region of interest data; receiving client location data describing a geographic location of a client device associated with the road scene included in the real time image; determining offline road map data for the road scene based on the geographic location of the client device, the offline road map data describing (1) a static label for a road included in the road scene and (2) offline road information describing a regulatory speed limit for the road included in the road scene; selecting, based on the static label, a classifier for analyzing the image feature vector; and executing, by a processor, the selected classifier to determine a real time driving difficulty category describing the difficulty for a user of the client device to drive in the road scene depicted in the real time image, wherein the real time driving difficulty category is determined based on the image feature vector, offline road information and runtime road description data describing the real time driving conditions of the road scene depicted in the real time image.
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2. A method comprising:
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determining dense grid sampling data describing one or more features included in a version of a real time image of a road scene which is configured to remove one or more redundant features from the real time image; determining image feature vector data based on one or more features described by the dense grid sampling data, the image feature vector data describing an image feature vector for the version of the real time image which is configured to remove the one or more redundant features from the real time image; determining offline road map data for the road scene based on a geographic location of a client device associated with the road scene included in the real time image, the offline road map data describing (1) a static label for a road included in the road scene and (2) offline road information describing a regulatory speed limit for the road included in the road scene; selecting, based on the static label, a classifier for analyzing the image feature vector; and executing, by a processor, the selected classifier to determine a real time driving difficulty category describing the difficulty for a user of the client device to drive in the road scene depicted in the real time image, wherein the real time driving difficulty category is determined based on the image feature vector and the offline road information. - View Dependent Claims (3, 4, 5, 6, 7, 8, 9, 10)
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11. A non-transitory computer-readable medium having computer instructions stored thereon that are executable by a processing device to perform or control performance of operations comprising:
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determining dense grid sampling data describing one or more features included in a version of a real time image of a road scene which is configured to remove one or more redundant features from the real time image; determining image feature vector data based on one or more features described by the dense grid sampling data, the image feature vector data describing an image feature vector for flail the version of the real time image which is configured to remove the one or more redundant features from the real time image; determining offline road map data for the road scene based on a geographic location of a client device associated with the road scene included in the real time image, the offline road map data describing (1) a static label for a road included in the road scene and (2) offline road information describing a regulatory speed limit for the road included in the road scene; selecting, based on the static label, a classifier analyzing the image feature vector; and executing, by a processor, the selected classifier to determine a real time driving difficulty category describing the difficulty for a user of the client device to drive in the road scene depicted in the real time image, wherein the real time driving difficulty category is determined based on the image feature vector and the offline road information. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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Specification