Mobileye Vision Technologies Ltd.

Oded Berberian

Barak Cohen

Gideon Stein

Harel Livyatan

Amnon Shashua

Shai Shalev-Shwartz

Shaked Shammah

Zeev Adelman

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

Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise an interface to obtain sensing data of an environment of the host vehicle. A processing device may be configured to determine a planned navigational action for the host vehicle; identify a target vehicle in the environment of the host vehicle; predict a following distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a host vehicle braking distance based on a braking capability, acceleration capability, and speed of the host vehicle; determine a target vehicle braking distance, based on a speed and maximum braking capability of the target vehicle; and implement the planned navigational action when the predicted following distance is greater than a minimum safe longitudinal distance based on the determined host vehicle braking distance and the determined target vehicle braking distance.

Systems and methods for navigating a vehicle

Systems and methods are provided for vehicle navigation. In one implementation, a processing device may be configured to obtain a planned driving action for accomplishing a navigational goal of a host vehicle; receive sensor data from an environment surrounding the host vehicle; identify a target vehicle moving in the environment and a velocity of the target vehicle; calculate a stopping distance and a predicted trajectory for the target vehicle; calculate a planned trajectory for the host vehicle corresponding to the planned driving action; identify an intersection of the planned trajectory for the host vehicle with the predicted trajectory for the target vehicle; determine a braking action of the host vehicle to comply with a safety requirement; and cause the braking action to be applied to decelerate the host vehicle to change the planned trajectory, until the changed trajectory does not intersect the predicted trajectory of the target vehicle.

The present disclosure relates to systems and methods for road edge detection and mapping, for vehicle wheel identification and navigation based thereon, and for classification of objects as moving or non-moving. Such systems and methods may include the use of trained systems, such as one or more neural networks. Further, autonomous vehicle systems may incorporate aspects of one or more of the disclosed systems and methods.

Detection and classification systems and methods for autonomous vehicle navigation

Determining three-dimensional structure in a road environment 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, a line is selected below which the road is imaged. The line separates between upper images essentially excluding images of the road and lower images essentially including images of the road. One or more of the lower images is warped, according to a road homography to produce at least one warped lower image. The three-dimensional structure may be provided from motion of a matching feature within the upper images or from motion of a matching feature within at least one of the lower images and at least one warped lower image.

Dense structure from motion

A system for navigating a host vehicle may receive an image representative of an environment of the host vehicle and determine a planned navigational action for accomplishing a navigational goal of the host vehicle. The system may identify a target vehicle, determine a current speed of the target vehicle, and assume a maximum braking rate capability of the target vehicle. The system may determine a next-state distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken. The system may implement the planned navigational action if the host vehicle may be stopped using a predetermined sub-maximal braking rate within a distance that is less than the determined next-state distance summed together with a target vehicle travel distance determined based on the current speed of the target vehicle and the maximum braking rate capability of the target vehicle.

Determining three-dimensional structure in a road environment using a system mountable in a host vehicle including a camera connectable to a processor. Multiple image frames are captured in the field of view of the camera. In the image frames, a line is selected below which the road is imaged. The line separates between upper images essentially excluding images of the road and lower images essentially including images of the road. One or more of the lower images is warped, according to a road homography to produce at least one warped lower image. The three-dimensional structure may be provided from motion of a matching feature within the upper images or from motion of a matching feature within at least one of the lower images and at least one warped lower image.

Auto-calibration of stereo cameras installable behind the windshield of a host vehicle and oriented to view the environment through the windshield. Multiple first image points are located of one of the first images captured from the first camera at a first time and matched with first image points of at least one other of the first images captured from the first camera at a second time to produce pairs of corresponding first image points respectively in the first images captured at the different times. World coordinates are computed from the corresponding first image points. Second image points in the second images captured from the second camera are matched to at least a portion of the first image points. The world coordinates as determined from the first camera are used, to solve for camera parameters of the second camera from the matching second image points of the second camera.

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Jerusalem, Israel

Oded Berberian