Machine-learned architecture for efficient object attribute and/or intention classification
AssigneeZoox, Inc.
InventorsOytun Ulutan, Subhasis Das, Yi-Ting Lin, Derek Xiang Ma
Life of this patent
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Abstract
A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.
Claims20
A method comprising:
- receiving a first image associated with a first time;
- receiving a second image associated with a second time prior to the first time, wherein the first image and the second image are associated with a same object;
- determining, by a first set of machine-learned layers and based at least in part on the first image, a first output;
- storing the first output in a memory;
- retrieving a second output from the memory, the second output being associated with the second image and having been previously processed by the first set of machine-learned layers and stored in the memory;
- determining, by one or more machine-learned layers and based at least in part on the first output and the second output, a first confidence score associated with a first pedestrian attribute and a second confidence score associated with a second pedestrian attribute;
- and
- controlling a vehicle based at least in part on at least one of the first confidence score or the second confidence score.
A system comprising:
- one or more processors;
- and
- a memory storing processor-executable instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
- receiving a first image associated with a first time;
- receiving a second image associated with a second time prior to the first time;
- determining, by a first set of machine-learned layers and based at least in part on the first image, a first output;
- storing the first output in a memory;
- retrieving a second output from the memory, the second output being associated with the second image;
- determining, by one or more machine-learned layers and based at least in part on the first output and the second output, an attribute associated with an object in an environment;
- and
- controlling a vehicle based at least in part on the attribute.
One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
- receiving a first image associated with a first time;
- receiving a second image associated with a second time prior to the first time;
- determining, by a first set of machine-learned layers and based at least in part on the first image, a first output;
- storing the first output in a memory;
- retrieving a second output from the memory, the second output being associated with the second image;
- determining, by one or more machine-learned layers and based at least in part on the first output and the second output, an attribute associated with an object in an environment;
- and
- controlling a vehicle based at least in part on the attribute.
Dependencies are inferred from the claim text.