Mobileye Vision Technologies Ltd.

Ofer Springer

Amnon Shashua

Yoram Gdalyahu

Daniel Braunstein

Aran Reisman

Nadav Shaag

Yoav Taieb

Jeffrey Moskowitz

Gideon Stein

Shai Shalev-Shwartz

Shaked Shammah

David Huberman

David Neuhof

Gal Topel

Yotam Stern

Yahel Guberman

Erez Dagan

Eyal Bagon

Ofer Fridman

Andras Ferencz

Kevin Rosenblum

Shahar Harouche

Roy Maor Lotan

Alon Ziv

Ishay Levi

Tomer Hochbaum

Nir Zabari

Abraham Hendler

David Boublil

Paz Lavi Bunis

Noa Schaffer

Mordechai Ben Zecharia

Ido Kenan

Jeremy Atlan

Daniel Einy

Chaim Blau

Gal Alkon

Yonatan Shemesh

Systems and methods for creating maps used in navigating autonomous vehicles are disclosed. In one implementation at least one processor is programmed to receive drive information from each of a plurality of vehicles that traverse different entrance-exit combinations of a road junction; for each of the entrance-exit combinations, align three-dimensional feature points in the drive information to generate a plurality of aligned three-dimensional feature point groups, one for each entrance-exit combination of the road junction; correlate one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included in every other aligned three-dimensional feature point group from among the plurality of aligned three-dimensional feature point groups; and generate a sparse map based on the correlation, the sparse map including a target trajectory associated with each of the entrance-exit combinations.

Fully aligned junctions

Techniques are disclosed for using augmented reality (AR) displays to convey graphical representations of different types of information to a user. The information may be obtained from various sources such as objects and/or features that are dynamically detected by a vehicle or user-generated content. The graphical representations may also be based upon information that may be obtained via the use of crowdsourced AV map data. The graphical representations may be presented in as an AR view in a medium (such as a vehicle windshield) that matches a field of view of a user, thus accurately blending holographic information with the physical objects in the scene.

Road experience management (REM) map features delivered via augmented reality (AR)

The present disclosure is directed to searching an image database. A system may include at least one processor comprising circuitry and a memory, wherein the memory includes instructions that, when executed by the circuitry cause the at least one processor to: receive a first user input including one or more scenario descriptors, identify in a database an initial plurality of images based on the first user input and display the initial plurality of images to a user. The processor may be further configured to receive a second user input wherein the second user input identifies at least one of the initial plurality of images. The at least one processor may identify a refined plurality of images in the database based on the one or more scenario descriptors in combination with the second user input and display the refined plurality of images to the user.

Systems and methods for searching an image database

A navigation system for a host vehicle may include a processor programmed to: receive from a camera onboard the host vehicle at least one captured image representative of an environment of the host vehicle, wherein the camera is positioned at a first location relative to the host vehicle; receive point cloud information from a LIDAR system onboard the host vehicle, wherein the LIDAR system is positioned at a second location relative to the host vehicle; analyze the at least one captured image and the received point cloud information to detect one or more objects in the shared field of view region; determine whether a vantage point difference between the first location of the camera and the second location of the LIDAR system accounts for the one or more detected objects being represented in only one of the at least one captured image or the received point cloud information.

LIDAR-camera fusion where LIDAR and camera validly see different things

Systems and methods for navigating a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive a first image acquired by a first camera and a second image acquired by a second camera onboard the host vehicle; identify a first representation of an object in the first image and a second representation of the object in the second image; input to a first trained model at least a portion of the first image; input to a second trained model at least a portion of the second image; receive the first signature encoding determined by the first trained model and the second signature encoding determined by the second trained model; input to a third trained model the first signature encoding and the second signature encoding; and receive an indicator of a location of the object determined by the third trained model.

Stereo-assist network for determining an object's location

Systems and methods are provided for controlling vehicle operation. A processor may access route information for navigation of a route by the vehicle including data relating to speed along the route and calculate a speed of the vehicle along the route based on the route information. The processor may cause the vehicle to be operated at the calculated speed along the route; obtain dynamic information for the route based on data collected from one or more other vehicles on the route and indicating current conditions on the route which affect the speed of the vehicle along the route; and cause the vehicle to be operated at an updated speed along the route, based on the dynamic information.

Crowd sourcing data for autonomous vehicle navigation

A navigation system for a host vehicle may include a processor programmed to: receive from a center camera onboard the host vehicle a captured center image including a representation of at least a portion of an environment of the host vehicle, receive from a left surround camera onboard the host vehicle a captured left surround image including a representation of at least a portion of the environment of the host vehicle, and receive from a right surround camera onboard the host vehicle a captured right surround image including a representation of at least a portion of the environment of the host vehicle; provide the center image, the left surround image, and the right surround image to an analysis module configured to generate an output relative to the at least one captured center image; and cause a navigational action by the host vehicle based on the generated output.

Pseudo lidar

Systems and methods navigate a vehicle by determining a free space region in which the vehicle can travel. In one implementation, a system may include at least one processor programmed to receive from an image capture device, a plurality of images associated with the environment of a vehicle, analyze at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary. The first free space boundary, the second free space boundary, and the forward free space boundary may define a free space region forward of the vehicle. The at least one processor of the system may be further programmed to determine a navigational path for the vehicle through the free space region and cause the vehicle to travel on at least a portion of the determined navigational path within the free space region forward of the vehicle.

Navigation based on free space determination

A system and method estimate a future path ahead of a current location of a vehicle. The system includes at least one processor programmed to: obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road; obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads; apply the trained system to the image of the environment ahead of the current arbitrary location of the vehicle; and provide, based on the application of the trained system to the image, an estimated future path of the vehicle ahead of the current arbitrary location.

Systems and methods for estimating future paths

Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

Systems and methods are provided for constructing, using, and updating the sparse map for autonomous vehicle navigation. A system may comprise a processor and a memory. The memory may include instructions, which when executed on the processor, cause the processor to maintain a map; determine, based on analysis of image data, an existence of a non-transient condition that is inconsistent with the map, the image data from a camera integrated with the autonomous vehicle; and update the map.

Self-aware system for adaptive navigation

Systems and methods are provided for constructing, using, and updating the sparse map for autonomous vehicle navigation. A method may comprise processing, by a mapping server, collected navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

Systems and methods navigate a vehicle on a road at least partially covered with snow. In one implementation, a system may include at least one processor programmed to receive from an image capture device, a plurality of images captured of an environment forward of the vehicle, including an area where snow covers a road on which the vehicle travels, analyze at least one of the plurality of images to identify a first free space boundary on a first side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary.

Navigating in snow

A system and method estimate a future path ahead of a current location of a vehicle. The system includes at least one processor programmed to obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads. The at least one processor is also programmed to obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road, and provide, based on an application of the trained system to the obtained image, an estimated future path of the vehicle ahead of the current arbitrary location.

A detection system for a vehicle is provided. The detection system may include at least one image capture device configured to acquire a plurality of images of an area forward of the vehicle, the area including a curb separating a road surface from an off-road surface and a data interface. The detection system may also include at least one processing device programmed to receive the plurality of images via the data interface, and determine a plurality of curb edge line candidates in the plurality of images. The at least one processing device may be further programmed to identify at least one edge line candidate as an edge line of the curb.

Growing community of inventors

Jerusalem, Israel

Ofer Springer