Ford Global Technolgoies, LLC

Steven Chao

Apurbaa Mallik

Vijay K Nagasamy

Gurjeet Singh

Ganesh Kumar

Hitha Revalla

Zafar Iqbal

Systems and methods for transforming autonomous aerial vehicle sensor data between platforms are disclosed herein. An example method can include receiving, by an UAV, vehicle radar data and radar calibration data from a vehicle, as well as location information for a location of the vehicle, determining a simulated UAV at the location, establishing an orientation for a simulated radar on a bottom of the simulated UAV, determining a height for the simulated UAV to match a field of view of the simulated radar, performing a geometrical transformation to convert the vehicle radar data set into a UAV perspective, converting the vehicle radar data into a vehicle coordinate frame using the radar calibration using vehicle global positioning system (GPS) coordinates, converting the vehicle coordinate frame from a global frame into a UAV coordinate frame using UAV GPS coordinates, and converting the UAV coordinate frame into simulated radar sensor frame.

Systems and methods for transforming autonomous aerial vehicle sensor data between platforms

The disclosure is generally directed to systems and methods for inference quality determination of a deep neural network (DNN) without requiring ground truth information for use in driver-assisted vehicles, including receiving an image frame from a source; applying a normal inference DNN model to the image frame to produce a first inference with a first bounding box using a normal inference DNN model; applying a deep inference DNN model to a plurality of filtered versions of the image frame to produce a plurality of deep inferences with a plurality of bounding boxes; comparing the plurality of bounding boxes to identify a cluster condition of the plurality of bounding boxes; and determining an inference quality of the image frame of the normal inference DNN model as a function of the cluster condition.

Systems and methods for detecting deep neural network inference quality using image/data manipulation without ground truth information

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San Francisco, CA, United States of America

Steven Chao