Mobileye Vision Technologies Ltd.

Intel Corporation

Other

Kfir Viente

Daniel Braunstein

Maxim Schwartz

Dori Shapira

Yoram Gdalyahu

Ovadya Menadeva

Maoz Madmony

Iddo Hanniel

Nimrod Nehushtan

Eli Harel

Achiya Tal Tamir

Bnaya Caspi

Gideon Stein

Yoav Taieb

Harel Livyatan

Itamar Gilad

Oded Schwarz

Avigdor Dori Eldar

Yaron Yanai

Tal Tamir

David Huberman

Andras Ferencz

Alexandra Manevitch

Dor Eagelberg

Oren Shamir

Ora Zackay

Ouriel Barzilay

Eli Peker

Ishay Levi

Asaf Bar Zvi

Itamar Glazer

Avigdor Eldar

Matan Sulami

Jonathan Barlev

Adi Hayat

Matan Argaman

Konstantin Lazarev

Ayelet Hashahar Swissa

Moshe Levin

Efrat Rosenberg-biton

Ariel Cohen

A system for mapping road segment free spaces for use in autonomous vehicle navigation. The system includes at least one processor programmed to: receive from a first vehicle one or more location identifiers associated with a lateral region of free space adjacent to a road segment; update an autonomous vehicle road navigation model for the road segment to include a mapped representation of the lateral region of free space based on the received one or more location identifiers; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.

Free space mapping and navigation

Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.

Mapping lane marks and navigation based on mapped lane marks

A system may include at least one processor including circuitry and a memory. The memory may include instructions executable by the circuitry to cause the at least one processor programmed to receive at least one identifier associated with a condition having at least one dynamic characteristic. The at least one identifier may be determined based on acquisition, from a camera associated with a host vehicle, of at least one image representative of an environment of the host vehicle, and analysis of the at least one image to identify the condition in the environment, and analysis of the at least one image to determine the at least one identifier associated with the condition. The at least one processor may also be programmed to update a database record to include the at least one identifier associated with the condition, and distribute the database record to at least one entity.

Aggregation and reporting of observed dynamic conditions

A system for mapping a lane mark for use in autonomous vehicle navigation is provided. The system includes at least one processor programmed to receive two or more location identifiers associated with a detected lane mark, associate the detected lane mark with a corresponding road segment, update an autonomous vehicle road navigation model relative to the corresponding road segment based on the two or more location identifiers associated with the detected lane mark, and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.

Systems and methods are provided for collecting anonymized drive information. A processing device may be configured to receive outputs from one or more sensors; determine at least one motion representation for the host vehicle based on the outputs; receive at least one image representative of an environment of the host vehicle; analyze the at least one image to determine at least one road characteristic associated with a road section; assemble first road segment information relative to a first portion of the road section, wherein the first portion of the road section is separated from a starting point associated with a route traveled by the host vehicle; assemble second road segment information relative to a second portion of the road section; and cause transmission of the first road segment information and the second road segment information to a server for assembly of an autonomous vehicle road navigation model.

Systems and methods for anonymizing navigation information

A computing device configured to: obtain images representative of an environment of a host vehicle, the host vehicle traveling on a roadway; detect, from the images, a mark located on the roadway; identify, from the images, points corresponding to the mark on the roadway; identify the mark as a type of roadway marking, corresponding to the identified points, the type of roadway marking selected from multiple types of roadway markings; determine a position of the mark on the roadway relative to the host vehicle, using the identified points corresponding to the mark; and determine a trajectory to navigate the host vehicle on the roadway, based on the position of the mark within the roadway and the type of roadway marking.

Systems and methods are provided for processing vehicle navigation information. A processing device may be configured to receive navigation information from a plurality of vehicles associated with a common road section; store the navigation information; generate at least a portion of an autonomous vehicle road navigation model for the common road section; and distribute the autonomous vehicle road navigation model to one or more autonomous vehicles. The navigation information from each of the vehicles may include first road segment information relative to a first portion of the common road section, and second road segment information relative to a second portion of the common road section. The second road segment information may include a motion representation for the vehicle and a road characteristic relative to the second portion of the common road section. The second portion may be different from and spatially separated from the first portion of the road section.

Anonymizing navigation information for use in autonomous vehicle navigation

Systems and methods are provided for anonymizing navigation data and generating an autonomous vehicle road navigation model with the anonymized data. A navigation system may receive data relating to a road section from a vehicle. The system may determine one or more motion representations associated with the vehicle and one or more road characteristics associated with the road section. The system may assemble navigation information relative to a first portion and relative to a second portion of the road section. The first and second portions may be spatially separated by a third portion. The system may transmit the navigation information relating to the first and second portions and forego transmitting information relating to the third portion. A server may receive the transmitted navigation information and assemble an autonomous vehicle road navigation model. The server may transmit the navigation model to one or more vehicles for use in autonomous navigation.

Techniques are provided for segmentation of a hand from a forearm in an image frame. A methodology implementing the techniques according to an embodiment includes estimating a wrist line within an image shape that includes a forearm and a hand. The wrist line estimation is based on a search for a minimum width region of the shape that is surrounded by adjacent regions of greater width on each side of the minimum width region. The method also includes determining a forearm segment, and a hand segment that is separated from the forearm segment by the wrist line. The method further includes labeling the forearm segment and the hand segment. The labeling is based on a connected component analysis of the forearm segment and the hand segment. The method further includes removing the labeled forearm segment from the image frame to generate the image segmentation of the hand.

Hand segmentation in a 3-dimensional image

An apparatus and method for natural hand processing for virtual reality. For example, one embodiment of an apparatus comprises: graphics processing circuitry to render left and right scenes responsive to a virtual reality application, the left and right scenes to be displayed on left and right displays, respectively, of a virtual reality device; and a natural hand processing pipeline to project a representation of a user's hands into the left and right scenes to allow the user to interact with one or more objects in the scenes, the natural hand processing pipeline comprising: a depth/mask data smoothing and reconstruction module to receive noisy depth and mask data associated with images of the user's hands and to enhance the noisy depth data and mask data to generate enhanced depth and mask data; a hand mesh generator to generate a hand mesh using the enhanced depth and mask data; and a mesh data projector to project the hand mesh into the left and right scenes.

Method and apparatus for natural hand visualization

Systems, apparatuses and methods may track air gestures within a bounding box in a field of view (FOV) of a camera. Air gestures made within the bounding box may be translated and mapped to a display screen. Should the hand, or other member making the air gesture, exit the bounds of the bounding box, the box will be dragged along by the member to a new location within the FOV.

Adaptive cursor technology

A system for hand gesture recognition includes a display, camera, memory, and processor. When the processor is to execute instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor may also estimate an average motion vector of the object, determine a first histogram of optical flow (HOOF) based on the one or more motion vectors and the average motion vector, determine depth values based on motion vectors from the first HOOF, and determine a second histogram of optical flow (HOOF) based on the depth values. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors, a shape descriptor, and the average motion vector and classify the descriptor as a gesture.

Hand gesture recognition for virtual reality and augmented reality devices

A system for hand gesture recognition is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors and the average motion vector and classify the descriptor as a gesture.

Hand gesture recognition for cursor control

A system for a tracker for cursor navigation is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to extract an object mask of an object and execute an optical flow on good feature points from the object mask. The processor is also to estimate a movement of the object and render a cursor on the display based on the movement of the object.

Tracker for cursor navigation

An example apparatus for classifying target objects using three-dimensional geometric filtering includes a patch receiver to receive patches with objects to be classified. The apparatus also includes a geometric filter to filter out patches including objects with sizes outside a target range using three dimensional geometry to generate filtered patches. The apparatus further includes a background remover to remove background pixels from the filtered patches to generate preprocessed patches. The apparatus includes a classification score calculator to calculate a classification score for each of the preprocessed patches.

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Jerusalem, Israel

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