Ford Global Technolgoies, LLC

Kunjan Singh

Mostafa Parchami

Enrique Corona

Gintaras Vincent Puskorius

Gaurav Pandey

Nikhil Nagraj Rao

Faizan Shaik

Akhil Perincherry

Chandana Neerukonda

Stephen Giardinelli

Xiaomin Li

A camera is positioned to obtain an image of an object. The image is input to a neural network that outputs a three-dimensional (3D) bounding box for the object relative to a pixel coordinate system and object parameters. Then a center of a bottom face of the 3D bounding box is determined in pixel coordinates. The bottom face of the 3D bounding box is located in a ground plane in the image. Based on calibration parameters for the camera that transform pixel coordinates into real-world coordinates, a) a distance from the center of the bottom face of the 3D bounding box to the camera relative to a real-world coordinate system and b) an angle between a line extending from the camera to the center of the bottom face of the 3D bounding box and an optical axis of the camera are determined. The calibration parameters include a camera height relative to the ground plane, a camera focal distance, and a camera tilt relative to the ground plane. A six degree-of-freedom (6DoF) pose for the object is determined based on the object parameters, the distance, and the angle.

Neural network object pose determination

A segmentation mask can be determined that includes at least one moving object in a plurality of images based on determining eccentricity for each pixel location in the plurality of images. A first image included in the plurality of images can be segmented by applying the segmentation mask to the image. The segmented first image can be transformed to a compressed dense matrix which includes pixel values for non-zero portions of the segmented first image. The compressed dense matrix can be input to a sparse convolutional neural network trained to detect objects. A detected object corresponding to the at least one moving object included in the first image can be output from the sparse convolutional neural network.

Object detection

A system, comprising a computer that includes a processor and a memory, the memory storing instructions executable by the processor to input a red-green-blue (RGB) image and an eccentricity image to a neural network which outputs a located object based on combining the RGB image and the eccentricity image, wherein the eccentricity image is based on a per-pixel rolling average and a per-pixel rolling variance over a moving window of k video frames. The memory can further include instructions executable by the processor to receive the located object at a computing device included in one or more of a vehicle or a traffic information system.

Eccentricity image fusion

A computer includes a processor and a memory, the memory storing instructions executable by the processor to collect a plurality of images of one or more targets at an intersection, input the images to a machine learning program to determine a number of the targets to which a host vehicle is predicted to yield at the intersection based on time differences between the plurality of images, and transmit a message indicating the number of the targets.

Vehicle intersection operation

A system, including a processor and a memory, the memory including instructions to be executed by the processor to identify first object features from sensor data acquired by a stationary sensor at a first time step, determine second object features at a second time step. The instructions can include further instructions to determine one or more object clusters of first object features by determining distances measured in pixels between the first object features and corresponding second object features and comparing the distances to one or more mean distances and determine one or more object groups of inlier first object features in the one or more object clusters by determining a plurality of similarity transformations for a plurality of random samples of first object features and determining inlier first object features based on maximizing the number of first object features included in a similarity transformation.

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Ann Arbor, MI, United States of America

Kunjan Singh