Abstract: A navigation system may include at least one processor programmed to receive, from a camera, one or more images representative of an environment of a host vehicle; receive a first directional indicator representative of a first target trajectory direction for the host vehicle; and receive a first distance estimate associated with the first target trajectory direction. The processor may analyze the one or more images in view of the received first directional indicator and the received first distance estimate to identify in the one or more images a representation of a first road segment consistent with the first target trajectory direction and the first distance estimate; and generate a planned trajectory for the host vehicle, based at least in part on the first target trajectory direction and the first distance estimate. The planned trajectory may be made to extend along at least a portion of the first road segment.

Abstract: A method comprising: receiving a set of images associated with each of a plurality of plants in a plantation; estimating, with respect to each of the plants, based, at least in part, on the set of images associated with the plant, the following data: (i) a count of fruits detected in the plant, and (ii) one or more features associated with the plant; at a training stage, training a machine learning model on a training set comprising, with respect to a subset of the plurality of plants: (iii) the data, and (iv) labels indicating an actual a number of fruits in each of the plants in the subset; and at an inference stage, applying the trained machine learning model to the data associated with the rest of the plurality of plants, to predict a number of fruits in each of the rest of the plurality of plants.

Abstract: A method comprising: receiving an image of an agricultural area-of-interest (AOI) acquired by a remote-sensing platform; performing an initial rectification to correct geometric distortions in the image; assigning initial geographic coordinates within a reference coordinate system to each data point in the image, based, at least in part, on location data recorded by the remote-sensing platform; performing object detection in the image, to detect at least some of the plants in the image; selecting a specified subset of the detected plants; calculating a transformation between the image and the reference coordinate system based on a comparison, with respect to each of the plants in the specified subset, between (i) the initial geographic coordinates assigned to the plant, and (ii) corresponding ground-truth geographic coordinates obtained with respect the plant; and performing an alignment between the image and the reference coordinate system based, at least in part, on the calculated transformation.

Abstract: A navigation system may include at least one processor programmed to receive, from a camera, one or more images representative of an environment of a host vehicle; receive a first directional indicator representative of a first target trajectory direction for the host vehicle; and receive a first distance estimate associated with the first target trajectory direction. The processor may analyze the one or more images in view of the received first directional indicator and the received first distance estimate to identify in the one or more images a representation of a first road segment consistent with the first target trajectory direction and the first distance estimate; and generate a planned trajectory for the host vehicle, based at least in part on the first target trajectory direction and the first distance estimate. The planned trajectory may be made to extend along at least a portion of the first road segment.

Abstract: A navigation system may include at least one processor programmed to receive, from a camera, one or more images representative of an environment of a host vehicle; receive a first directional indicator representative of a first target trajectory direction for the host vehicle; and receive a first distance estimate associated with the first target trajectory direction. The processor may analyze the one or more images in view of the received first directional indicator and the received first distance estimate to identify in the one or more images a representation of a first road segment consistent with the first target trajectory direction and the first distance estimate; and generate a planned trajectory for the host vehicle, based at least in part on the first target trajectory direction and the first distance estimate. The planned trajectory may be made to extend along at least a portion of the first road segment.

Abstract: A navigation system may include at least one processor programmed to receive, from a camera, one or more images representative of an environment of a host vehicle; receive a first directional indicator representative of a first target trajectory direction for the host vehicle; and receive a first distance estimate associated with the first target trajectory direction. The processor may analyze the one or more images in view of the received first directional indicator and the received first distance estimate to identify in the one or more images a representation of a first road segment consistent with the first target trajectory direction and the first distance estimate; and generate a planned trajectory for the host vehicle, based at least in part on the first target trajectory direction and the first distance estimate. The planned trajectory may be made to extend along at least a portion of the first road segment.

Abstract: A method comprising: receiving a source image and a target image, each depicting a dense recurring pattern comprising an array of objects arranged in close proximity to one another; applying a trained machine learning classifier to obtain a classification of each pixel in said source image and said target image into one of at least two classes; determining a pixel-level transformation between said classified source and target images, based, at least in part, on a set of transformation parameters; training a neural network to optimize said set of transformation parameters, based, at least in part, on minimizing a loss function which calculates a weighted sum of per-pixel matches between said classified source and target images; and applying said optimized set of transformation parameters to said target image. to align said target image with said source image.

Abstract: A computerized system mountable on a moving vehicle. The computerized system includes a camera. The camera captures in real time image frames of the environment in the field of view of the camera and transfers the image frames to an image processor. The image processor is programmed for performing traffic sign recognition and for performing another driver assistance function. Information is exchanged between the traffic sign recognition and the other driver assistance function.

Abstract: A traffic sign recognition system including a detection mechanism adapted for detecting a candidate traffic sign and a recognition mechanism adapted for recognizing the candidate traffic sign as being an electronic traffic sign. A partitioning mechanism may be adapted for partitioning the image frames into a first partition and a second partition. The detection mechanism may use the first portion of the image frames and the recognition mechanism may use the second portion of the image frames. When the candidate traffic sign is detected as an electronic traffic sign, the recognition mechanism may use both the first partition of the image frames and the second portion of the image frames.

Abstract: A computerized system mountable on a moving vehicle. The computerized system includes a camera. The camera captures in real time image frames of the environment in the field of view of the camera and transfers the image frames to an image processor. The image processor is programmed for performing traffic sign recognition and for performing another driver assistance function. Information is exchanged between the traffic sign recognition and the other driver assistance function.

Abstract: A method for detecting and identifying a traffic sign in a computerized system mounted on a moving vehicle. The system includes a camera mounted on the moving vehicle. The camera captures in real time multiple image frames of the environment in the field of view of the camera and transfers the image frames to an image processor. The processor is programmed for performing the detection of the traffic sign and for performing another driver assistance function. The image frames are partitioned into the image processor into a first portion of the image frames for the detection of the traffic sign and into a second portion of the image frames for the other driver assistance function. Upon detecting an image suspected to be of the traffic sign in at least one of said image frames of the first portion, the image is tracked in at least one of the image frames of the second portion.

Abstract: A traffic sign recognition system including a detection mechanism adapted for detecting a candidate traffic sign and a recognition mechanism adapted for recognizing the candidate traffic sign as being an electronic traffic sign. A partitioning mechanism may be adapted for partitioning the image frames into a first partition and a second partition. The detection mechanism may use the first portion of the image frames and the recognition mechanism may use the second portion of the image frames. When the candidate traffic sign is detected as an electronic traffic sign, the recognition mechanism may use both the first partition of the image frames and the second portion of the image frames.

Abstract: A method for detecting and identifying a traffic sign in a computerized system mounted on a moving vehicle. The system includes a camera mounted on the moving vehicle. The camera captures in real time multiple image frames of the environment in the field of view of the camera and transfers the image frames to an image processor. The processor is programmed for performing the detection of the traffic sign and for performing another driver assistance function. The image frames are partitioned into the image processor into a first portion of the image frames for the detection of the traffic sign and into a second portion of the image frames for the other driver assistance function. Upon detecting an image suspected to be of the traffic sign in at least one of said image frames of the first portion, the image is tracked in at least one of the image frames of the second portion.