Mobileye Vision Technologies Ltd.

Ofer Fridman

Yoav Taieb

Gideon Stein

Yoram Gdalyahu

Dor Eagelberg

Ofer Springer

Yahel Guberman

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

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Systems and methods for navigating lane merges and lane splits

Systems and methods are provided for crowdsourcing a sparse map for autonomous vehicle navigation. In one implementation, a non-transitory computer-readable medium may include a sparse map for autonomous vehicle navigation along a road segment. The sparse map may include at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature, and wherein the road surface feature is identified through image analysis of a plurality of images acquired as one or more vehicles traverse the road segment and a plurality of landmarks associated with the road segment.

Crowdsourcing a sparse map for autonomous vehicle navigation

Systems and methods are provided for crowdsourcing road surface information collection. In one implementation, a method of collecting road surface information for a road segment may include receiving at least one image representative of a portion of the road segment, identifying in the at least one image at least one road surface feature along the portion of the road segment, determining a plurality of locations associated with the road surface feature according to a local coordinate system of the vehicle, and transmitting the determined plurality of locations from the vehicle to a server. The determined locations may be configured to enable determination by the server of a line representation of the road surface feature extending along the road segment.

Crowdsourcing the collection of road surface information

Systems and methods are provided for distributing a crowdsourced sparse map for autonomous vehicle navigation. In one implementation, a method of generating a road navigation model for use in autonomous vehicle navigation may include receiving navigation information associated with a common road segment from a plurality of vehicles; storing the navigation information associated with the common road segment; generating at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information; and distributing the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating along the common road segment. The autonomous vehicle road navigation model may include at least one line representation of a road surface feature extending along the common road segment, and each line representation may representing a path along the common road segment substantially corresponding with the road surface feature.

Distributing a crowdsourced sparse map for autonomous vehicle navigation

Systems and methods are provided for navigating a vehicle using a crowdsourced sparse map. In one implementation, a method of autonomously navigating a vehicle along a road segment may include receiving a sparse map model, receiving at least one image representative of an environment of the vehicle, analyzing the sparse map model and the at least one image, and determining an autonomous navigational response for the vehicle based on the analysis of the sparse map model and the at least one image. The at least one image may be received from a camera, and the sparse map model may include at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature.

Navigating a vehicle using a crowdsourced sparse map

Systems and methods are provided for aligning crowdsourced map data. In one implementation, a method of determining a line representation of a road surface feature extending along a road segment may include receiving a first set of drive data including position information associated with the road surface feature, receiving a second set of drive data including position information associated with the road surface feature, segmenting the first set of drive data into first drive patches and segmenting the second set of drive data into second drive patches, longitudinally aligning the first set of drive data with the second set of drive data within corresponding patches, and determining the line representation of the road surface feature based on the longitudinally aligned first and second drive data in the first and second draft patches.

Growing community of inventors

Jerusalem, Israel

Ofer Fridman