Lane segment clustering using hybrid distance metrics
AssigneeArgo AI, LLC
InventorsAndrew HARTNETT, George Peter Kenneth CARR, Nikolai POPOV
Life of this patent
Estimating the term…
Abstract
Disclosed herein are system, method, and computer program product embodiments for clustering lane segments of a roadway in order to improve and simplify autonomous vehicle behavior testing. The approaches disclosed herein provide a hybrid methodology of dividing lane segments into hard features and soft features, and using a metric learning model trained in a supervised process on the entirety of lane segment features to cluster the lane segments based on the soft features. These clustered lane segments can then be assigned to what is termed as protolanes, where a single set of tests applied to a given protolane is considered valid across all of the lane segments assigned to the protolane.
Claims20
A method, comprising:
- grouping, by the one or more computing devices, a set of lane segments based on trained labels from a supervised learning process, wherein the trained labels are applicable to a plurality of features of the set of lane segments;
- calculating, by the one or more computing devices, distances corresponding to the set of lane segments in the set of lane segments based on individual features of the set of lane segments assessed by a metric learning model trained with the trained labels;
- and
- clustering, by the one or more computing devices, the set of lane segments based on the distances.
A system, comprising:
- a memory;
- and
- at least one processor coupled to the memory and configured to execute operations comprising:
- grouping a set of lane segments based on trained labels from a supervised learning process, wherein the trained labels are applicable to a plurality of features of the set of lane segments,
- calculating distances corresponding to the set of lane segments in the set of lane segments based on individual features of the set of lane segments assessed by a metric learning model trained with the trained labels, and
- clustering the set of lane segments based on the distances.
A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:
- grouping a set of lane segments based on trained labels from a supervised learning process, wherein the trained labels are applicable to a plurality of features of the set of lane segments;
- calculating distances corresponding to the set of lane segments in the set of lane segments based on individual features of the set of lane segments assessed by a metric learning model trained with the trained labels;
- and
- clustering the set of lane segments based on the distances.
Dependencies are inferred from the claim text.