Ford Global Technolgoies, LLC

Kay Kunes

Alexander Groh

Sarah Houts

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.

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Marana, AZ, United States of America

Kay Kunes