Artificial Intelligence – Miguel Alexander Peake, Benjamin Englard, Luminar Technologies Inc, Luminar Holdco LLC

Abstract for “Automatically creating training data for a lidar by using simulated vehicles within virtual space”

This document discloses “Automated training data generators that create feature training datasets to be used in real-world autonomous driving apps based on virtual environments.” These feature training datasets can be used to train a machine-learning model that will control real-world autonomous cars. An occupancy grid generator may be used in some embodiments to create an occupancy grid that is indicative of the environment for an autonomous vehicle using an imaging scene. The vehicle’s movement through the environment is controlled by the occupancy grid. A sensor parameter optimizer can be used to determine the parameters for real-world sensors that are part of autonomous driving applications. Based on the operation of an autonomous vehicle, the sensor parameter optimizer can determine the optimal parameter setting. This parameter setting could be applied to real-world sensors that are associated with real-world autonomous-driving applications.

Background for “Automatically creating training data for a lidar by using simulated vehicles within virtual space”

“Overview”

“Example Automated Learning Dataset Generator”

“Example Sensor Parameter Optimizer”

“Example Occupancy Generator”

“Virtual- and Real-World Autonomous Vehicles.”

“Example Self Driving Control Architecture(s).”

“Example Flow Diagrams.”

“General Considerations”

Summary for “Automatically creating training data for a lidar by using simulated vehicles within virtual space”

“Overview”

“Example Automated Learning Dataset Generator”

“Example Sensor Parameter Optimizer”

“Example Occupancy Generator”

“Virtual- and Real-World Autonomous Vehicles.”

“Example Self Driving Control Architecture(s).”

“Example Flow Diagrams.”

“General Considerations”

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