Mobileye Vision Technologies Ltd.

Mobileye Technologies Limited

Other

Mobileye Vision Tehcnologies Ltd.

Gideon Stein

Amnon Shashua

Erez Dagan

Efim Belman

Yoram Gdalyahu

Yoav Taieb

Nadav Shaag

Itay Blumenthal

Barak Cohen

Ofer Mano

Shai Shalev-Shwartz

Shaked Shammah

Harel Livyatan

Oded Berberian

Jeffrey Moskowitz

Ofer Hadassi

Amiad Gurman

Yaniv Elimalech

Andras D Ferencz

Dan Rosenbaum

Ofer Avni

Ori Eytan

Ofer Fridman

David Aloni

Dor Eagelberg

Natalie Carlebach

Yonatan Samet

Uri Wolfovitz

Ori Shachar

Yaakov Kingsley

Kfir Viente

Gaby Hayon

Ori Buberman

Oded Schwarz

Moshe Katiee

Avi Eyal

Ofer Springer

Andras Ferencz

Itay Gat

Harel Liyatan

Noam Ben Haim

Noam Kahlon

Michael Latowicki

A vehicle navigation system for detecting and responding to a cut-in by a target vehicle is disclosed. The vehicle navigation system includes at least one processor configured to receive image data captured by an image capture device of the host vehicle; identify, based on analysis of the image data, a target vehicle traveling in a lane adjacent to a lane in which the host vehicle is traveling; determine, based on analysis of the image data, that a predetermined cut in sensitivity change factor is present; and based on the determination that the predetermined cut in sensitivity change factor is present, cause a navigational response in the host vehicle.

Predicting and responding to cut in vehicles and altruistic responses

The present disclosure relates to navigational systems for vehicles. In one implementation, such a navigational system may receive a plurality of images captured by an image capture device onboard the host vehicle, the plurality of images being associated with an environment of the host vehicle; analyze at least one of the plurality of images to identify the target vehicle and at least one wheel component on a side of the target vehicle; determine, based on an analysis of at least two of the plurality of images, a rotation of the at least one wheel component of the target vehicle; and cause at least one navigational change of the host vehicle based on the rotation of the at least one wheel component of the target vehicle.

Controlling a vehicle based on detected movement of a target vehicle

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

System and techniques for vehicle environment modeling with a camera are described herein. A device for modeling an environment comprises: a hardware sensor interface to obtain a sequence of unrectified images representative of a road environment, the sequence of unrectified images including a first unrectified image, a previous unrectified image, and a previous-previous unrectified image; and processing circuitry to: provide the first unrectified image, the previous unrectified image, and the previous-previous unrectified image to an artificial neural network (ANN) to produce a three-dimensional structure of a scene; determine a selected homography; and apply the selected homography to the three-dimensional structure of the scene to create a model of the road environment.

Vehicle environment modeling with a camera

An imaging system is provided for a vehicle. In one implementation, the imaging system includes an imaging module, a first camera coupled to the imaging module, a second camera coupled to the imaging module, and a mounting assembly configured to attach the imaging module to the vehicle such that the first and second camera face outward with respect to the vehicle. The first camera has a first field of view and a first optical axis, and the second camera has a second field of view and a second optical axis. The first optical axis crosses the second optical axis in at least one crossing point of a crossing plane. The first camera is focused a first horizontal distance beyond the crossing point of the crossing plane and the second camera is focused a second horizontal distance beyond the crossing point of the crossing plane.

Cross field of view for autonomous vehicle systems

Systems and methods are provided for processing reports received from vehicles. A processing device perform operations comprising receiving a first report generated by a first vehicle, the first report generated by the first vehicle for a first hazard detected by the first vehicle; receiving a second report generated by a second vehicle, the second report generated by the second vehicle for a second hazard detected by the second vehicle; analyzing the first report and the second report to make a determination that the first report and the second report identify a related hazard; aggregating the first report and the second report into a consolidated report based on the determination that the first report and the second report identify a related hazard; and generating a map, the map indicating a location of the related hazard based on the consolidated report.

Safety system for a vehicle to detect and warn of a potential collision

A vehicle navigation system may comprise a memory including instructions and circuitry configured by the instructions to identify a target vehicle in an environment of a vehicle that includes the vehicle navigation system. The circuitry may receive image data of the target vehicle from an image capture device of the vehicle; identify, based on analysis of the image data, a situational characteristic of the target vehicle including an indication that the target vehicle is traveling behind an additional vehicle traveling slower than the target vehicle; and change a navigational state of the vehicle to allow an action of the target vehicle. The vehicle may be configured to cause the change in the navigational state based on a determination that the situational characteristic indicates that the target vehicle would benefit from the change in the navigational state.

Systems and methods are provided for navigating a host vehicle. In an embodiment, a processing device may be configured to receive images captured over a time period; analyze images to identify a target vehicle; receive map information associated including a plurality of target trajectories; determine, based on analysis of the images, first and second estimated positions of the target vehicle within the time period; determine, based on the first and second estimated positions, a trajectory of the target vehicle over the time period; compare the determined trajectory to the plurality of target trajectories to identify a target trajectory traversed by the target vehicle; determine, based on the identified target trajectory, a position of the target vehicle; and determine a navigational action for the host vehicle based on the determined position.

Determining road location of a target vehicle based on tracked trajectory

Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise at least one processor. The processor may be programmed to receive an image associated with the environment of the host vehicle. The processor may analyze the image to identify a side of a parked vehicle; a first structural feature of the parked vehicle in a forward region of the side of the parked vehicle or a second structural feature of the parked vehicle in a rear region of the side of the parked vehicle; and a door feature of the parked vehicle in a vicinity of the first or the second structural features. The processor may then determine, based on a subsequent image, a change of an image characteristic of the door feature of the parked vehicle and alter a navigational path of the host vehicle based on the change of the image characteristic.

Controlling host vehicle based on detected door opening events

Computerized methods are performable by a driver assistance system while the host vehicle is moving. The driver assistance system includes a camera connectible to a processor. First and second image frames are captured from the field of view of the camera. Corresponding image points of the road are tracked from the first image frame to the second image frame. Image motion between the corresponding image points of the road is processed to detect a hazard in the road. The corresponding image points are determined to be of a moving shadow cast on the road to avoid a false positive detection of a hazard in the road or the corresponding image points are determined not to be of a moving shadow cast on the road to verify detection of a hazard in the road.

Hazard detection from a camera in a scene with moving shadows

System and techniques for vehicle environment modeling with a camera are described herein. A time-ordered sequence of images representative of a road surface may be obtained. An image from this sequence is a current image. A data set may then be provided to an artificial neural network (ANN) to produce a three-dimensional structure of a scene. Here, the data set includes a portion of the sequence of images that includes the current image, motion of the sensor from which the images were obtained, and an epipole. The road surface is then modeled using the three-dimensional structure of the scene.

Various systems and methods for modeling a scene. A device for modeling a scene includes a hardware interface to obtain a time-ordered sequence of images representative of a scene, the time-ordered sequence including a plurality of images, one of the sequence of images being a current image, the scene captured by a monocular imaging system; and processing circuitry to: provide a data set to an artificial neural network (ANN) to produce a three-dimensional structure of the scene, the data set including: a portion of the sequence of images, the portion of the sequence of images including the current image; and motion of a sensor that captured the sequence of images; and model the scene using the three-dimensional structure of the scene, wherein the three-dimensional structure is determined for both moving and fixed objects in the scene.

Vehicle environment modeling with cameras

Detection of three dimensional obstacles using a system mountable in a host vehicle including a camera connectible to a processor. Multiple image frames are captured in the field of view of the camera. In the image frames, an imaged feature is detected of an object in the environment of the vehicle. The image frames are portioned locally around the imaged feature to produce imaged portions of the image frames including the imaged feature. The image frames are processed to compute a depth map locally around the detected imaged feature in the image portions. Responsive to the depth map, it is determined if the object is an obstacle to the motion of the vehicle.

Monocular cued detection of three-dimensional structures from depth images

Growing community of inventors

Jerusalem, Israel

Gideon Stein