Lyft, Inc.
DYNAMIC OBJECT DETECTION MODEL BASED ON STATIC MAP COLLECTION DATA

Abstract:

Systems, methods, and non-transitory computer-readable media can obtain information describing a static map of a geographic location, wherein the static map is determined based at least in part on a plurality of three-dimensional representations of the geographic location captured by one or more sensors of one or more vehicles. At least one training example that includes visual features and a corresponding label can be generated based on an unsupervised process for generating training examples, wherein the visual features are extracted based on the static map and at least one three-dimensional representation of the geographic location. At least one machine learning model can be trained to distinguish between static objects and non-static objects in visual data based on the at least one training example, wherein the at least one machine learning model is trained based on an unsupervised learning process.