Mobileye Vision Technologies Ltd.

David Huberman

Amnon Shashua

Gideon Stein

Daniel Braunstein

Yoav Taieb

Ouriel Barzilay

Shaked Shammah

Yoram Gdalyahu

Ofer Springer

Shai Shalev-Shwartz

Eyal Bagon

Anna Clarke

Kfir Viente

Maoz Madmony

Itai Ben Shalom

Iddo Hanniel

Maxim Schwartz

Andras Ferencz

Dori Shapira

Dor Eagelberg

Ora Zackay

Nimrod Nehushtan

Levi Bellaiche

Ishay Levi

Igor Tubis

Achiya Tal Tamir

Bnaya Caspi

Eli Peker

Avigdor Eldar

Matan Sulami

Matan Argaman

Moshe Levin

Jonathan Barlev

Adi Hayat

Efrat Rosenberg-biton

Ariel Cohen

Shai Shalev-Schwarz

A system for autonomous vehicle navigation. The system includes at least one processor programmed to: receive one or more images representative of an environment of a host vehicle; identify a representation or indicator of a traffic light in the images; and determine a state of the traffic light based on the analysis of the images. The one more process may also be programmed to receive an autonomous vehicle road navigation model including associations between traffic lights and trajectories collected from multiple vehicles The processor is also configured to make determinations of traffic lights state, relevancy of traffic lights to host vehicle trajectory, and navigation actions (in response to the determination of a traffic light being relevant). The system is also configured to cause one or more actuator systems associated with the host vehicle to implement determined navigational actions.

Navigation based on traffic light cycle prediction

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.

System for mapping traffic lights and associated traffic light cycle times

A system for mapping traffic lights and for determining traffic light relevancy for use in autonomous vehicle navigation. The system may include at least one processor programmed to: receive at least one location identifier associated with a traffic light; receive a state identifier associated with the traffic light; receive navigational information indicative of one or more aspects of motion of the first vehicle along the road segment, and determine, based on the navigational information, a lane of travel traversed by the first vehicle along the road segment. The processor may also determine whether the traffic light is relevant to the lane of travel traversed by the first vehicle; update an autonomous vehicle road navigation model relative to the road segment; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.

Relevant traffic light mapping and navigation

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

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

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.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a traffic light detection system is provided for a vehicle. One or more processing devices associated with the system receive at least one image of an area forward of the vehicle via a data interface, with the area including at least one traffic lamp fixture having at least one traffic light. The processing device(s) determine, based on at least one indicator of vehicle position, whether the vehicle is in a turn lane. Also, the processing device(s) process the received image(s) to determine the status of the traffic light, including whether the traffic light includes an arrow. Further, the system may cause a system response based on the determination of the status of the traffic light, whether the traffic light includes an arrow, and whether the vehicle is in a turn lane.

Systems and methods for determining the status and details of a traffic light

A system for autonomously navigating a vehicle along a road segment may be based on a predetermined landmark location. The system may include at least one processor programmed to receive from a camera, at least one image representative of an environment of the vehicle, and determine a position of the vehicle along a predetermined road model trajectory associated with the road segment based, at least in part, on information associated with the at least one image. The at least one processor may be further programmed to identify a recognized landmark forward of the vehicle based on the determined position, wherein the recognized landmark is beyond a sight range of the camera, and determine a current distance between the vehicle and the recognized landmark by comparing the determined position of the vehicle with a predetermined position of the recognized landmark. The at least one processor may also be programmed to determine an autonomous navigational response for the vehicle based on the determined current distance.

Navigation based on expected landmark location

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a traffic light detection system is disclosed. The 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 traffic lamp fixture having at least one traffic light. At least one processing device may be configured to align areas of the plurality of images corresponding to the traffic light, based on a determined center of brightness, expand each pixel within the aligned areas, determine a set of average pixel values including an average pixel value for each set of corresponding expanded pixels within the aligned areas, and determine, based on the set of average pixel values, whether the traffic light includes an arrow.

Systems and methods for detecting traffic signal details

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Jerusalem, Israel

David Huberman