Mobileye Vision Technologies Ltd.

Daniel Braunstein

Yoram Gdalyahu

Amnon Shashua

Aran Reisman

Ofer Springer

Kfir Viente

Dori Shapira

Yoav Taieb

Maoz Madmony

David Huberman

Iddo Hanniel

Maxim Schwartz

Andras Ferencz

Dor Eagelberg

Ouriel Barzilay

Ora Zackay

Eli Harel

Nimrod Nehushtan

Achiya Tal Tamir

Bnaya Caspi

Levi Bellaiche

Igor Tubis

Eli Peker

Ishay Levi

Avigdor Eldar

Matan Sulami

Sergey Rubinsky

Moshe Levin

Jonathan Barlev

Adi Hayat

Matan Argaman

Ariel Cohen

Efrat Rosenberg-biton

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 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

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 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 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

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

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.

A system for autonomously navigating a vehicle through a road junction may include at least one processor programmed to receive from an image capture device at least one image representative of an environment of the vehicle. The processor may also be configured to analyze the image to identify two or more landmarks located in the environment of the vehicle and to determine, for each of the two or more landmarks, a directional indicator relative to the vehicle. The processor may be configured to determine a current location of the vehicle relative to the road junction based on an intersection of the directional indicators a heading for the vehicle based on the directional indicators for the two or more landmarks. The processor may be configured to determine a steering angle for the vehicle by comparing the vehicle heading with a predetermined road model trajectory at the current location of the vehicle.

Navigating road junctions

Systems and methods are provided for determining a lane assignment for an autonomous vehicle along a road segment. In one implementation, at least one image representative of an environment of the vehicle is received from a camera. The at least one image may be analyzed to identify at least one recognized landmark, and an indicator of a lateral offset distance between the vehicle and the at least one recognized landmark may be determined. Moreover, a lane assignment of the vehicle along the road segment may be determined based on the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark.

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Jerusalem, Israel

Daniel Braunstein