Top-down refinement in lane marking navigation
First Claim
1. A computer system comprising:
- one or more memories storing instructions; and
one or more processors configured to execute the instructions to cause the system to;
receive, from one or more cameras, one or more images of a roadway in a vicinity of a vehicle, the roadway comprising a lane marking comprising a dashed line;
obtain a multi-frame model of the lane marking, wherein the multi-frame model of the lane marking includes a location for the lane marking at a first time;
update the multi-frame model of the lane marking based on odometry of the one or more cameras relative to the roadway to obtain an updated multi-frame model of the lane marking, wherein the updated multi-frame model of the lane marking includes an estimated updated location for the lane marking at a second time;
identify a plurality of candidate dashes from the received one or more images;
calculate spacing information relating to spaces between the plurality of candidate dashes;
perform a top-down modeling process to refine the updated multi-frame model of the lane marking at the updated location at the second time based on an appearance of dashes derived from the received one or more images and based on the calculated spacing information; and
cause one or more navigational responses in the vehicle based on the refinement of the updated multi-frame model.
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Accused Products
Abstract
Systems and methods use cameras to provide autonomous navigation features. In one implementation, top-down refinement in lane marking navigation is provided. The system may include one or more memories storing instructions and one or more processors configured to execute the instructions to cause the system to receive from one or more cameras one or more images of a roadway in a vicinity of a vehicle, the roadway comprising a lane marking comprising a dashed line, update a model of the lane marking based on odometry of the one or more cameras relative to the roadway, refine the updated model of the lane marking based on an appearance of dashes derived from the received one or more images and a spacing between dashes derived from the received one or more images, and cause one or more navigational responses in the vehicle based on the refinement of the updated model.
32 Citations
22 Claims
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1. A computer system comprising:
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one or more memories storing instructions; and one or more processors configured to execute the instructions to cause the system to; receive, from one or more cameras, one or more images of a roadway in a vicinity of a vehicle, the roadway comprising a lane marking comprising a dashed line; obtain a multi-frame model of the lane marking, wherein the multi-frame model of the lane marking includes a location for the lane marking at a first time; update the multi-frame model of the lane marking based on odometry of the one or more cameras relative to the roadway to obtain an updated multi-frame model of the lane marking, wherein the updated multi-frame model of the lane marking includes an estimated updated location for the lane marking at a second time; identify a plurality of candidate dashes from the received one or more images; calculate spacing information relating to spaces between the plurality of candidate dashes; perform a top-down modeling process to refine the updated multi-frame model of the lane marking at the updated location at the second time based on an appearance of dashes derived from the received one or more images and based on the calculated spacing information; and cause one or more navigational responses in the vehicle based on the refinement of the updated multi-frame model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A vehicle, comprising:
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a body; one or more cameras configured to acquire one or more images of a roadway in a vicinity of a vehicle, the roadway comprising a lane marking comprising a dashed line; one or more memories storing instructions; and one or more processors configured to execute the instructions to cause the vehicle to; receive, from one or more cameras, the one or more acquired images of the roadway; obtain a multi-frame model of the lane marking, wherein the multi-frame model of the lane marking includes a location for the lane marking at a first time; update the multi-frame model of the lane marking based on odometry of the one or more cameras relative to the roadway to obtain an updated multi-frame model of the lane marking, wherein the updated multi-frame model of the lane marking includes an estimated updated location for the lane marking at a second time; identify a plurality of candidate dashes from the received one or more images; calculate spacing information relating to spaces between the plurality of candidate dashes; perform a top-down modeling process to refine the updated multi-frame model of the lane marking at the updated location at the second time based on an appearance of dashes derived from the received one or more images and based on the calculated spacing information; and cause one or more navigational responses in the vehicle based on the refinement of the multi-frame updated model. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A method comprising:
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receiving, by one or more processors, from one or more cameras, one or more images of a roadway in a vicinity of a vehicle, the roadway comprising a lane marking comprising a dashed line; obtaining a multi-frame model of the lane marking, wherein the model of the lane marking includes a location for the lane marking at a first time; updating, by the one or more processors, the multi-frame model of the lane marking based on odometry of the one or more cameras relative to the roadway to obtain an updated multi-frame model of the lane marking, wherein the updated multi-frame model of the lane marking includes an estimated updated location for the lane marking at a second time; identifying a plurality of candidate dashes from the received one or more images; calculating spacing information relating to spaces between the plurality of candidate dashes; performing a top-down modeling process to refine, by the one or more processors, the updated multi-frame model of the lane marking at the updated location at the second time based on an appearance of dashes derived from the received one or more images and based on the calculated spacing information; and causing, by the one or more processors, one or more navigational responses in the vehicle based on the refinement of the updated multi-frame model. - View Dependent Claims (22)
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Specification