System for automated lane marking

Abstract

Systems and processes can automatically identify lane markings within images through the use of a machine learning model. The machine learning model may use a reduced set of data and output an improved estimate of lane markings by applying normalized data or images to the machine learning model. Each image applied to the model can be normalized by, for example, rotating each of the images such that the depicted roads are horizontal or otherwise share the same angle. By aligning disparate images of roads, it is possible to reduce the amount of data applied to the model or to model generation, and to increase the accuracy of the machine learning model. Further, the use of normalized images by the machine learning model enables a reduction in computing resources used to apply data to the machine learning model to, for example, identify lane markings within images.

Description

[0001] Incorporation by reference into the priority application [0002] Any and all applications, if any, for which foreign or domestic priority claims are identified in the Application Data Sheet of this application are hereby incorporated by reference in their entirety pursuant to 37 CFR 1.57. Furthermore, this disclosure is related to the following disclosures, filed on December 27, 2018 (the same date as the present disclosure), which are hereby incorporated by reference in their entirety for any purpose: U.S. Application No. 16 / 234,130, entitled "Image Preprocessing in Lane Marking Determination Systems"; U.S. Application No. 16 / 233,960, entitled "Using Image Preprocessing to Generate Machine Learning Models"; and U.S. Application No. 16 / 233,989, entitled "Using system". [0003] Copyright Notice [0004] Portions of the disclosure of this patent document contain material that is protected by copyright. The copyright owner has no objection to the reproduction by anyone ...

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