Ford Global Technolgoies, LLC

Argo Ai Gmbh

Volkswagen Group of America Investments, LLC

Nicolas Cebron

Guy Hotson

Richard L Kwant

Deva K Ramanan

Francesco Ferroni

Kiwoo Shin

Harpreet Banvait

Chao Fang

John Peterson

Marius Seritan

Craig Bryan

Mengtian Li

Shaojun Zhu

Chitteshwaran Thavamani

Systems and methods for processing high resolution images are disclosed. The methods include generating a saliency map of a received high-resolution image using a saliency model. The saliency map includes a saliency value associated with each of a plurality of pixels of the high-resolution image. The method then includes using the saliency map for generating an inverse transformation function that is representative of an inverse mapping of one or more first pixel coordinates in a warped image to one or more second pixel coordinates in the high-resolution image, and implementing an image warp for converting the high-resolution image to the warped image using the inverse transformation function. The warped image is a foveated image that includes at least one region having a higher resolution than one or more other regions of the warped image.

Systems and methods for generating object detection labels using foveated image magnification for autonomous driving

Systems and methods for on-board selection of data logs for training a machine learning model. The methods include, by an autonomous vehicle, receiving sensor data logs corresponding to surroundings of the autonomous vehicle from a plurality of sensors, identifying one or more events within each sensor data log. The methods also include, for each sensor data log: analyzing features of the identified one or more events within that sensor data log for determining whether that sensor data log satisfies one or more usefulness criteria for training a machine learning model, and transmitting that sensor data log to a remote computing device for training the machine learning model if that sensor data log satisfies one or more usefulness criteria for training the machine learning model. The features can include spatial features, temporal features, bounding box inconsistencies, or map-based features.

Autonomous vehicle system for intelligent on-board selection of data for training a remote machine learning model

Systems and methods for generating a panoptic segmentation mask for an input image. The methods include receiving the input image comprising a plurality of pixels, generating a semantic mask and an instance mask from the input image, and combining the semantic mask and the instance mask to generate a panoptic mask for the input image. The semantic mask includes a single-channel mask that associates each pixel in the input image with a corresponding one of a plurality of labels. The instance mask includes a plurality of masks, where each of the plurality of masks identifies an instance of a countable object in the input image, and is associated with an indication of whether that instance of the countable object is hidden behind another object in the input image.

Systems and methods for panoptic segmentation of images for autonomous driving

Methods and systems for detecting objects near an autonomous vehicle (AV) are disclosed. An AV will capture an image. A trained network will process the image at a lower resolution and generate a first feature map that classifies object(s) within the image. The system will crop the image and use the network to process the cropped section at a higher resolution to generate a second feature map that classifies object(s) that appear within the cropped section. The system will crop the first feature map to match a corresponding region of the cropped section of the image. The system will fuse the cropped first and second feature maps to generate a third feature map. The system may output the object classifications in the third feature map to an AV system, such as a motion planning system that will use the object classifications to plan a trajectory for the AV.

Attentional sampling for long range detection in autonomous vehicles

Systems and methods for image-based perception. The methods comprise: capturing images by a plurality of cameras with overlapping fields of view; generating, by a computing device, spatial feature maps indicating locations of features in the images; identifying, by the computing device, overlapping portions of the spatial feature maps; generating, by the computing device, at least one combined spatial feature map by combining the overlapping portions of the spatial feature maps together; and/or using, by the computing device, the at least one combined spatial feature map to define a predicted cuboid for at least one object in the images.

Systems and methods for image based perception

Systems and methods for image-based perception. The methods comprise: obtaining, by a computing device, images captured by a plurality of cameras with overlapping fields of view; generating, by the computing device, spatial feature maps indicating locations of features in the images; defining, by the computing device, predicted cuboids at each location of an object in the images based on the spatial feature maps; and assigning, by the computing device, at least two cuboids of said predicted cuboids to a given object when predictions from images captured by separate cameras of the plurality of cameras should be associated with a same detected object.

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Sunnyvale, CA, United States of America

Nicolas Cebron