Ford Global Technolgoies, LLC

Hitha Revalla

Vijay K Nagasamy

Gurjeet Singh

Apurbaa Mallik

Zafar Iqbal

Nikita Jaipuria

Aniruddh Ravindran

Steven Chao

A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive an image frame from a camera, generate a feature map from the image frame, generate a depth map from the feature map, classify an object in the image frame based on the feature map, and estimate a distance to the object based on the depth map and based on an input to generating the feature map.

Distance determination from image data

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

A computer includes a processor and a memory storing instructions executable by the processor to receive image data from a camera, generate a depth map from the image data, detect an object in the image data, apply a bounding box circumscribing the object to the depth map, mask the depth map by setting depth values for pixels in the bounding box in the depth map to a depth value of a closest pixel in the bounding box, and determine a distance to the object based on the masked depth map. The closest pixel is closest to the camera of the pixels in the bounding box.

A training system for a neural network system and method of training is disclosed. The method may comprise: receiving, from a sensor, an image frame captured while an operator is controlling a vehicle; using an eye-tracking system associated with the sensor, monitoring the eyes of the operator to determine eyeball gaze data; determining, from the image frame, a plurality of pedestrians; and iteratively training the neural network system to determine, from among the plurality of pedestrians, the one or more target pedestrians using the eyeball gaze data and an answer dataset that is based on the eyeball gaze data, wherein the determined one or more target pedestrians have a relatively-higher probability of collision with the vehicle than a remainder of the plurality of pedestrians.

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Stanford, CA, United States of America

Hitha Revalla