US 10,592,805 B2Granted

Physics modeling for radar and ultrasonic sensors

AssigneeFord Global Technologies, LLC

InventorsAlexander Groh, Kay Kunes, Sarah Houts

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Abstract

A machine learning module may generate a probability distribution from training data including labeled modeling data correlated with reflection data. Modeling data may include data from a LIDAR system, camera, and/or a GPS for a target environment/object. Reflection data may be collected from the same environment/object by a radar and/or an ultrasonic system. The probability distribution may assign reflection coefficients for radar and/or ultrasonic systems conditioned on values for modeling data. A mapping module may create a reflection model to overlay a virtual environment assembled from a second set of modeling data by applying the second set to the probability distribution to assign reflection values to surfaces within the virtual environment. Additionally, a test bench may evaluate an algorithm, for processing reflection data to generate control signals to an autonomous vehicle, with simulated reflection data from a virtual sensor engaging reflection values assigned within the virtual environment.

Claims17

Dependent claims

A system, comprising:

a processor set configured to process a set of training data, the set of training data comprising:
three-dimensional modeling data from at least one of a camera system, a LIght-Detection-And-Ranging (LIDAR) system, and a locational-awareness system;
and
physics modeling data from at least one of a radar system and an ultrasonic system;
a machine learning module which, when executed by the processor set, learns a mapping to physical modeling characteristics from three-dimensional modeling information;
a mapping module which, when executed by the processor set, creates a physical model for a virtual environment by applying the mapping to the three-dimensional modeling information, the physical model providing reflection values for at least one of a virtual radar and a virtual ultrasonic system in the virtual environment;
and
a model generation module which, when executed by the processor set, assembles the three-dimensional modeling information into the three-dimensional model of the virtual environment,
wherein the mapping module, when executed by the processor set, also applies the mapping to the three-dimensional modeling information by assigning reflectivity coefficients to surfaces in the three-dimensional model of the virtual environment for reflectivity of each of the surfaces.

6 more elements

A method for physical modeling, further comprising:

applying a machine learning algorithm to a set of training data to create a probability distribution for reflection coefficients conditioned on a set of modeling data of surface reflectivity acquired by at least one of a camera system, a LIght-Detection-And-Ranging (LIDAR) system, and a position system;
and
assigning reflection values to the set of modeling data by applying the set of modeling data to the probability distribution using a mapping function,
wherein the assigning the reflection values comprises assigning the reflection values to a set of surface regions for reflectivity of each of the set of surface regions, and
wherein the applying the machine learning algorithm to the set of training data comprises:
performing supervised learning on a deep neural network with the training data;
and
after training, the deep neural network implements the probability distribution.

5 more elements

Dependencies are inferred from the claim text.