Autonomous vehicle driving path label generation for machine learning models

Abstract

A system identifies a set of input data including a roadgraph identifying an intermediate autonomous vehicle (AV) driving path related to a scene representing an environment proximate an AV. The intermediate AV driving path reflects a modification to an initial (AV) driving path to avoid one or more obstacles obstructing the initial AV driving path. The system performs, using the set of input data, a path adjustment operation that identifies one or more candidate AV driving paths based on the intermediate AV driving path and determines a cost value for each of the candidate AV driving paths. The system identifies, among the candidate AV driving paths, a final AV driving path having a cost value that satisfies an evaluation criterion. The final AV driving path is to be included in the set of training data as a target output paired with training input including scene data identifying the scene.