17819881. METHODS AND SYSTEMS FOR GENERATING LANE LINE AND ROAD EDGE DATA USING EMPIRACAL PATH DISTRIBUTIONS simplified abstract (GM GLOBAL TECHNOLOGY OPERATIONS LLC)

METHODS AND SYSTEMS FOR GENERATING LANE LINE AND ROAD EDGE DATA USING EMPIRACAL PATH DISTRIBUTIONS

Organization Name

GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor(s)

Samuel Walker Beck of Dearborn MI (US)

Benjamin Isaacoff of Royal Oak MI (US)

Matthew Kelly Titsworth of Pflugerville TX (US)

Jasmine Kuo of Pflugerville TX (US)

METHODS AND SYSTEMS FOR GENERATING LANE LINE AND ROAD EDGE DATA USING EMPIRACAL PATH DISTRIBUTIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17819881 titled 'METHODS AND SYSTEMS FOR GENERATING LANE LINE AND ROAD EDGE DATA USING EMPIRACAL PATH DISTRIBUTIONS

Simplified Explanation

• • Explanation:**

The patent application describes a method for defining map data used in controlling a vehicle. This method involves receiving telemetry data, determining distribution data of a path, generating sample data based on machine learning models, generating lane line data and road edge data based on the sample data, and storing the map data for vehicle control.

• • Bullet Points:**
• Method involves receiving telemetry data and determining distribution data of a path.
• Sample data is generated based on a trained machine learning model and distribution data.
• Lane line data and road edge data are generated based on sample data and a second machine learning model.
• Map data, including lane line data and road edge data, is stored for vehicle control.

• • Potential Applications:**

- Autonomous driving systems - Advanced driver assistance systems - Fleet management systems

• • Problems Solved:**

- Accurate mapping data for vehicle control - Integration of machine learning models for map data generation

• • Benefits:**

- Improved accuracy in vehicle control - Enhanced safety features in vehicles - Efficient navigation systems

Original Abstract Submitted

Systems and method are provided for defining map data used in controlling a vehicle. In one embodiment, a method includes: receiving, by a processor, telemetry data; determining, by the processor, distribution data of a path based on the telemetry data; determining, by the processor, a plurality of sample data based on a trained machine learning model and the distribution data; generating, by the processor, at least one of lane line data and road edge data based on the sample data and a second machine learning model; and storing, by the processor, the map data including the lane line data and road edge data for use in controlling the vehicle.